Guillaume Pellecuer
email: guillaume.pellecuer@umontpellier.fr

François Forest

Jean-Jacques Huselstein

Thierry Martiré

Olivier Arnould

André Chrysochoos

J.-J Huselstein

Toward a multi-physical approach to connection ageing in power modules

Keywords: Reliability, IGBT Power Modules, Power Cycling, Thermomechanical Fatigue, Wire bonds

INTRODUCTION TO THE POWER MODULE AGEING

Semiconductor power modules are key devices in power electronics. They are used in many applications where reliability is an essential concern, such as network interconnections, transportation, and critical infrastructure. In many cases, these devices are subjected to repetitive variations of electrical loading conditions that induce thermal cycling on integrated dies due to their resulting self-heating (Joule effects). Indeed, the modules mainly consists of different stacked material layers through which the heat diffuses, thereby generating significant, nonstationary temperature gradients. The presence of this induced thermal loading within a multilayer structure with varying expansion coefficients thus generates mechanical stresses and consequently the conditions for ageing mechanisms to appear. Experiment shows that the latter mainly affect the upper zone of the components, i.e., the bonds between the wires and the metallization of the emitter. (Fig. 1) [START_REF] Pedersen | Dynamic Modeling Method of Electro-Thermo-Mechanical Degradation in IGBT Modules[END_REF][2] [START_REF] Schilling | Power cycling testing and FE modelling focussed on Al wire bond fatigue in high power IGBT modules[END_REF].

Studied power module

The component studied in this paper is an IGBT (Insulate Gate Bipolar Transistor) power module representative of the today technology [START_REF] Majumdar | Power module technology for home power electronics[END_REF] [START_REF] Yang | Automotive Power Module Packaging: Current Status and Future Trends[END_REF]. It is the MICROSEMI APTGT200A60T3AG 600V-200A module (Fig. 2). This module is made of 2 IGBT chips and 4 diodes forming an inverter arm. An elementary switch consists of one IGBT and 2 diodes associated in parallel. The ageing study of this kind of power modules can be developed following two paths. First, via an experimental approach, lifespan's curves are established on the basis of industrial feedback or on experimental data provided by specially designed test benches. Secondly, it consists of analyzing physically and modeling numerically the different electro-thermo-mechanical mechanisms leading to the degradation of modules.

Experimental approach

Many ageing studies of power semiconductor modules are carried out using test benches [START_REF] Van Der Broeck | A Test Bench for Thermal Characterization of IGBT Power Modules Over Mission Profiles[END_REF] [START_REF] Choi | Advanced Accelerated Power Cycling Test for Reliability Investigation of Power Device Modules[END_REF]. IES (Institut d'Électronique et des Systèmes) institute has developed test benches to place IGBT power modules under realistic power cycling conditions, notably with highly adjustable test parameters. Results have been obtained on different types of modules and with various electrical stresses and thermal conditions. They have highlighted interesting trends such as time/frequency dependence [START_REF] Forest | Fast power cycling protocols implemented in an automated test bench dedicated to IGBT module ageing[END_REF] [START_REF] Czerny | Thermo-mechanical analysis of bonding wires in IGBT modules under operating conditions[END_REF]. The test benches (Fig. 3) implemented to provide the experimental results presented below have been described in previous papers [START_REF] Forest | Fast power cycling protocols implemented in an automated test bench dedicated to IGBT module ageing[END_REF][9] [START_REF] Smet | Aging and failure modes of IGBT power modules undergoing power cycling in high temperature environments[END_REF]. The components under test are IGBT inverter arms that are combined in a full bridge. They share an inductive load allowing them to be tested under a wide range of conditions using the opposition method [START_REF] Forest | Use of opposition method in the test of highpower electronic converters[END_REF]. Due to the Bonding Metalization Metalization Metalization back-to-back operating mode of the two legs, power consumption is limited to the inverter losses. The corresponding PWM control part enables setting various shapes of IGBT currents and, consequently, various power cycling shapes. Throughout the tests, the on-state voltage VCE across each chip is measured periodically for given current and junction temperature conditions [START_REF] Smet | Evaluation of Vce Monitoring as a Real-Time Method to Estimate Aging of Bond Wire-IGBT Modules Stressed by Power Cycling[END_REF]. This allows detecting damage occurring on the wire bond junctions that can be revealed by a low, but measurable, increase in this voltage. Tests are stopped when the voltage increase reaches 3% to 5% of the initial onstate voltage. An increase in 𝑉 of 3% compared to its initial value before ageing corresponds to a critical state where a quarter of the bonding wires can be peeled off [START_REF] Smet | Evaluation of Vce Monitoring as a Real-Time Method to Estimate Aging of Bond Wire-IGBT Modules Stressed by Power Cycling[END_REF] [START_REF] Coquery | Failure criteria for long term Accelerated Power Cycling Test linked to electrical turn off SOA on IGBT module. A 4000 hours test on 1200A-3300V module with AlSiC base plate[END_REF]. Indeed, above 3%-5%, the growth of the voltage 𝑉 is very fast and during the next few thousand cycles the component is completely destroyed and analysis becomes impossible. In some rare instances, failure occurs before the detection of such an increase, in which case the sample cannot be considered when analyzing results. Lifetime is defined as the number of thermal cycles (Fig. 4) reached until this on-state voltage increases by 3-5%. The damaged modules are then opened, the silicone gel is removed and the top part of the dies is observed using an SEM. In all tested samples, the lift-off of wire bonds (3 to 8 per die in Fig. 5) is the only kind of damage that occurs. The results are then used to establish lifespan curves as a function of thermal loading conditions. This statistical approach in the form of an empirical model then allows the definition of laws that can be used by manufacturers or customers. In Fig. 6, an example of results obtained in previous work are compiled to establish a Held model [START_REF] Held | Fast power cycling test of IGBT modules in traction application[END_REF]. This latter allows one to estimate the lifetime as a function of the amplitude of the thermal cycling Δ𝑇 and the low temperature of the 𝑇 cycle. There are other laws more commonly used to describe the lifetime of power modules such as Coffin-Masson's law [14][15]. This law, which is widely used to describe module fatigue, is often modified to include the influence of parameters such as temperature rise time or cycling frequency [START_REF] Scheuermann | A New Lifetime Model for Advanced Power Modules with Sintered Chips and Optimized Al Wire Bonds[END_REF] [START_REF] Bayerer | Model for Power Cycling lifetime of IGBT Modules -various factors influencing lifetime[END_REF]. These laws are generally identified for given modules. They do not, a priori, allow one to predict a lifespan in the case of others IGBT module brands (not yet tested in fatigue), or of known modules overcoming more severe electric conditions than those already imposed.

Motivation

As previously underlined, the experimental study of the ageing of power semiconductor modules is an essential asset to highlight and understand the mechanisms responsible for failures. It also allows to establish laws based on empirical models which have the advantage of directly linking the lifetime of the modules to the thermal loads experienced by the component, as mentioned above.

However, this pragmatic method does not attempt to consider all the physical mechanisms responsible for the ageing of the modules. To take a step forward in understanding mechanisms leading to the modules ageing, the different physics occurring during its lifespan must be considered, trying to take their interactions and their irreversible character into account. Several articles on this topic have already been proposed in the literature [START_REF] Yang | A time-domain physicsof-failure model for the lifetime prediction of wire bond interconnects[END_REF][19] [START_REF] Popok | Comprehensive physical analysis of bond wire interfaces in power modules[END_REF], and it is in this perspective that this work is carried out. Through numerical modelling, all the multiphysical interactions are successively considered, with the ultimate aim of better quantify the intensity of these interactions and get better prediction of the module's lifespan for a large domain of electrical loadings.

THERMOMECHANICAL MODELING BACKGROUND

The starting point of this modelling is the determination of the electrical dissipation sources. Indeed during their operating, the power modules are the seat of time-dependent Joule effects (conduction and switching losses) [START_REF] Czerny | Thermo-mechanical analysis of bonding wires in IGBT modules under operating conditions[END_REF]. The induced distribution of heat sources generates a self-heating which creates, via thermal expansion mechanisms, mechanical stresses (see Fig. 7). If the stress intensity reaches the yield stress, viscoplastic strains occur during each thermal cycle (low cycle fatigue) inducing creep and/or positive/negative hardening leading rapidly the solder wire bond to the failure. When stress paths remain within the so-called elastic domain, electric loadings induce a more gradual degradation of the matter named high cycle fatigue mechanisms. Whatever the fatigue mechanisms involved, it is highly likely that the progressive and heterogeneous evolution of the microstructure damage modifies the distribution of electric current densities within the component and reinforces the heterogeneous character of the heat sources and thus of the temperature gradients and stress intensities, thus accelerating the degradation process. In Fig. 7 an illustration of these different couplings is basically shown. The objective of this work was therefore to identify, model, and simulate the electro-thermo-mechanical interactions appearing on the wire bond/die interface. In a first stage, we considered the electro-thermal couplings and the thermomechanical mechanisms until a viscoplastic stabilization occurred. The next stage will be the consideration of fatigue damage and the influence of the mechanical degradation and the distribution of electric densities.

Electro-thermo-mechanical context

A power module, used in a converter operating under a variable electrical regime, is the seat of time-varying electrical dissipation (Joule effect). This is the so-called "power cycling".

Due to the thermo-dilatability of the materials used, temperature variations generate dilations and therefore mechanical strains, which are limited by the mechanical connections between the assembly's parts, inducing mechanical stresses. Cycle by cycle, these conditions can lead to degradation of wire-bond feet.

The samples are aged on a test bench operating under switching conditions. A general diagram of this bench is shown in Fig. 8. The test bench uses two modules to constitute a PWM Bridge loaded by an inductor (for loss minimization) that operates like a real drive inverter with respect to the electrical stress applied to IGBT dies. Closed-loop Pulse Width Modulation is applied to generate a controlled current in the IGBTs. More detailed descriptions of this test bench can be found in [START_REF] Forest | Fast power cycling protocols implemented in an automated test bench dedicated to IGBT module ageing[END_REF][9][10] [START_REF] Smet | Evaluation of Vce Monitoring as a Real-Time Method to Estimate Aging of Bond Wire-IGBT Modules Stressed by Power Cycling[END_REF].

The dies are thus placed under conditions very similar to those found in industrial applications. Switching frequency can vary between 10 kHz and 40 kHz and can be used as a power cycling adjustment parameter (switching losses). Due to the die's thermal inertia, temperature variations created by switching are negligible. The loss estimation can therefore be determined by calculating average power on each switching period over a total duration equal to the cycling period.

For one switching period, with D being the duty cycle, diode losses 𝑃 are:

𝑃 (𝑡) = (1 -𝐷)𝑉 (𝑡)𝑖 (𝑡) + 𝐸 𝐹 (1)
with 𝑉 , 𝑖 , 𝐸 and 𝐹 are respectively the diode voltage the direct diode current, the recovery reverse energy and switching frequency.

IGBT losses 𝑃 are:

𝑃 (𝑡) = 𝐷𝑉 (𝑡)𝑖 (𝑡) + (𝐸 + 𝐸 )𝐹 (2)
𝑉 , 𝑖 , 𝐸 and 𝐸 are the IGBT collector emitter voltage, the IGBT current, turn on and turn off energy (function of gate resistor, voltage operating, collector current). Fig. 9 shows an example of results for a rectangular current shape. Several parameters are available for adjusting the shape and amplitude of temperature swings: load current shape and amplitude, cycling period 𝑇 , switching period and temperature of the module base plate imposed by the cooling system.

Losses are controlled by current regulation. This action leads indirectly to the control of ∆T at the chip level. However, for a given ∆T, it is also necessary to control the minimum temperature of the cycles. This is managed via the cooling system (consisting of controlled ventilation through a water-air exchanger) which makes it possible to impose a reference temperature Tref on the module base plates. Considering the test frequencies (from a few tenths of Hz to a few Hz [START_REF] Choi | Advanced Accelerated Power Cycling Test for Reliability Investigation of Power Device Modules[END_REF]), the thermal inertia of the assembly has a filtering effect on the temperature profile [START_REF] Smet | Ageing and Failure Modes of IGBT Modules in High-Temperature Power Cycling[END_REF]. The result is an almost constant temperature at the base of the modules. Thermal modeling was based on the following heat diffusion:

𝜌𝐶 -𝑘∆𝜃 = 𝑆, (3)
where ρ, Cp, k, θ are respectively the mass density [kg.m

-3 ], specific heat [J.kg -1 .K -1 ], thermal conductivity [W.m -1 .K -1 ] and temperature variation = T-T0 [ °C]
with respect to a reference equilibrium temperature T0. On the right-hand side of Eq. ( 3), S gathers electrical heat sources per unit volume. Note that the heat sources induced by mechanical transformations (e.g. viscoplastic dissipation, thermoelastic effects) were neglected.

Considering the volume of the IGBT and diode dies (𝒱 and 𝒱

), the electrical sources S can then be written as:

𝑆 = 𝒱 + 𝒱 + 𝒱 + 𝒱 , (4)
Eq. ( 4) represents the sources of a single module. The volume here was defined as the active part of transistors and diodes. Indeed, the edges and gate pad areas of the transistors were not included in this computation volume.

The behavior chosen for the bonding wire material (aluminum) is that of a standard thermo-elastic model in series with a temperature dependent viscoplastic model, with isotropic hardening and a linear viscosity.

The thermoelastic part of the behavior is assumed to be isotropic and its characteristic parameters (Young's modulus E, Poisson's ratio ν, linear expansion coefficient λthe) are assumed to be invariant with the temperature.

The main thermoelastic constitutive equations are:

𝜀 = 𝜀 -𝜀 = 𝜎 -𝑡𝑟(𝜎)𝛿 + 𝜆 𝜃𝛿 , (5)
where 𝜀 , 𝜀 ,𝜀 are the overall, elastic, and viscoplastic strain components respectively. The viscoplastic part of the material behavior includes isotropic hardening with an isotropic linear viscous effect in parallel. The yield function, limiting the elastic domain, reads:

𝑓(𝝈, 𝑅(𝑝, 𝑇)) = 𝜎 -𝑅(𝑝, 𝑇) -𝜎 , ( 6
)
where 𝝈 is the Cauchy stress tensor, 𝜎 the von Mises equivalent stress, 𝑅 the hardening parameter depending on the accumulated viscoplastic strain 𝑝 and temperature 𝑇, and 𝜎 the tensile yield stress.

The von Mises equivalent stress is defined by:

𝜎 = 𝑠 𝑠 , (7)
where 𝑠 = 𝜎 -𝑡𝑟(𝜎)𝛿 are the components of the deviatoric stress tensor 𝒔. The equivalent viscoplastic strain rate 𝑝̇ reads:

𝑝̇= 𝜀̇ 𝜀̇ , (8)
where 𝜀̇ are the viscoplastic strain rate components. Following the Perzyna formulation [START_REF] Perzyna | Fundamental Problems in Viscoplasticity[END_REF], the viscoplastic flow rules can finally be written as:

𝜀̇ = 〈 𝝈, ( , ) 〉 𝝈, ( , ) , (9)
the operator 〈𝑥〉 being the positive part of the variable 𝑥 and 𝜂 the material viscosity. The viscosity coefficient was assumed to be independent of the temperature and the viscoplastic state.

Experimental hardening curves shown hereafter (see subsection 2.4), were obtained at different temperatures and different strain rates, and led us to identify the hardening function R(p,T) and estimate the viscosity 𝜂. Identification was made using a Voce's expression for R and a least-squares fitting method [START_REF] Voce | A practical strain-hardening function[END_REF].

The identified model was expected to reveal two heat sources induced by the deformation mechanisms: a so-called thermoelastic source 𝑆 (Eq. ( 10)), linked to the thermo-dilatable nature of the material, and a dissipative source 𝑆 (Eq. ( 11)) linked to viscoplastic irreversibility:

𝑆 ≈ -𝜆 𝜃 𝝈 𝑰 , (10)
𝑆 ≈ 𝜎 𝑝̇+ 𝜂𝑝̇ , [START_REF] Smet | Evaluation of Vce Monitoring as a Real-Time Method to Estimate Aging of Bond Wire-IGBT Modules Stressed by Power Cycling[END_REF] where 𝜎 𝐼 = tr(𝝈) is the first invariant of the Cauchy stress tensor.

Considering the present context of strong self-heating of electro-thermal origin, it appeared that the sources of mechanical origin were negligible (𝑆 ≫ 1.5 × 10 𝑊/𝑚 , 𝑆 ≈ 1 × 10 𝑊/𝑚 , 𝑆 ≈ 1.5 × 10 𝑊/𝑚 )The heat equation then remained the one presented in Eq. ( 3) with the heat source shown in Eq. (4).

To temporarily conclude the description of the mechanical behavior, let us remind that damage mechanisms are not yet included in this first model. The magnitude of the viscoplastic effects from the very first cycles on the bonding feet led us to limit the modeling to this complexity level. The often-localized damage mechanisms will be considered in a second step. The goal will then be to estimate the influence of the heterogeneous development of damage on the electric current densities and thus on the distribution of the dissipative sources of heat. The coupling loop will then be complete (Fig. 7).

The electro-thermo-mechanical context being defined, it is now possible to establish a numerical strategy to be implemented in a finite element code to characterize the ageing of wire bonds.

TOWARDS DIGITAL MODELLING

The complexity of the problem of ageing bonding wires does not allow us to consider an analytical solution. The use of numerical simulations via finite elements becomes a solution.

All simulations were performed with Matlab® and Comsol Multiphysics® tools. The finite element (FE) software allows us to communicate with Matlab® via a "live link". This solution has the advantage of using the computational flexibility of Matlab® to easily manipulate the models present in the finite element code library, in both preprocessing and post-processing.

The numerous parameters entering the simulation have several origins given by the multi-physical nature of the problem. These parameters are related to:

-the geometry of the system's components, -the electro-thermo-mechanical characteristics of the constitutive materials, -the boundary and initial conditions, -the electrical loading (current consumed by the system or loading frequency, number of periods, etc.), -the integration time step, -the fineness of the spatial mesh size.

Electrical dissipation was computed using Matlab® tools. The dissipation time course was estimated using the "manufacturer" data for the IGBT module. An interpolation of the curves enabled us to calculate the heat rate involved in each of the module's semiconductors, according to the parameters of the component. This data was then inserted into the FE code to simulate the thermal problem (Eq. ( 3)) of the complete system using the finite element method.

All calculations regarding thermomechanical aspects were managed by the FE code. The obtained results (kinematic, stress, and temperature fields) were then transferred to Matlab® for processing and visualization.

Before presenting the modeling results, it is important to indicate how the geometries of the components were defined, how the simulations were performed, and how the material parameters necessary for the calculations were identified.

Electro-thermal simulations

First, simulations of the thermo-electric effects were carried out taking all test bench components into account. The geometry of the bench equipped with 2 IGBT modules was digitally designed using the FE code graphical interface. A preliminary numerical analysis of the self-heating effects of the complete module allowed us to no longer consider the presence of bonding wires (of low thermal inertia) in order to drastically reduce the computation time. For reasons of visibility, Fig. 10 shows the geometry of only one module. However, in the FE code the whole test bench including both IGBT modules has been simulated. Thanks to its symmetry, only half of the system was meshed for finite element calculation to reduce computation time and minimize the memory space required to store the thermal solution.

As results showed low thermal interactions between both modules, only one module was then considered for the mechanical simulation.

The primary interest of thermal simulations was twofold. First, this made it possible to determine the parameters (current shape and amplitude, switching frequency, etc.) to be applied to obtain the desired thermal profile. Second, the use of thermal data extracted from a computation made on the complete assembly (module and cooler) in the thermomechanical simulations but focused on module connectivity.

The initial conditions were those of a system in thermal equilibrium with the room temperature. The boundary conditions were of linear Fourier type, imposed on the active areas of each semiconductor. A convection coefficient was then associated with the lower surface representing the heatsink. The value of this coefficient was set to achieve the desired temperature at the measurement area used in our experimental tests. Since the module case is filled with a passivation gel that does not promote heat exchange with the external environment, we only considered exchanges via the base plate, assuming the other boundaries of the module with the surroundings to be adiabatic.

Once these assumptions were incorporated into the FE code, thermal simulations were then performed to reproduce the operating conditions obtained on the complete test bench with the same electrical parameters as those used on the experimental tests. Fig. 10 : Thermal field of the complete moduleshows an example of a 3D module thermal field.

Thermo-mechanical simulations

As the objective of the numerical work is to better understand the viscoplastic transformations of the wire bonds, thermo-mechanical simulation focused on the attachment zone, thus limiting itself to soldering, chip, and bonding. This multimaterial area of interest (tin/silver compound, silicon, aluminum, see Tab. 1) has very different expansion coefficients (Al/Si interface, SnAg/Si), which induces significant differential thermal expansion and therefore stress concentrations. The next section highlights that aspect. Thermal oscillations then generate a multiaxial stress state that is intense enough to induce a viscoplastic deformation of the bonding from the first quarter of cycle.

To reduce the upcoming thermomechanical calculation to a zone of interest (ZOI) surrounding the wire bond, the "coupling operators" proposed by Comsol Multiphysics® were used. In this situation, the coupling operator enabled us to recover the temperatures at the boundary of the ZOI, resulting from the global thermal calculation of the test bench, to serve as Dirichlet thermal boundary conditions (TBCs) for the thermomechanical coupling problem focused on the wire bond region. To obtain accurate calculations when high gradients occur, the mesh size in the ZOI was refined. The coupling operator then provided the necessary interpolated data from the global mesh data. For surfaces of the ZOI in contact with the surrounding air, Fourier TBCs from the global problem were replaced by interpolated temperature (Dirichlet TBCs) derived from global computations. To reduce the computation time, a IGBT chip equipped with only one wirebond (1.5cm in length and 300𝜇𝑚 in diameter) is simulated.

The lower plane of the ZOI was considered as the reference plane regarding mechanical boundary conditions (MBCs). The center of this plane was fixed to avoid any rigid body translation, and the rotation of rigid body of this plane was prevented by blocking a movement component on one of the border points. A zero-stress normal vector was considered (Neuman MBC) for all other points of the ZOI's external surfaces in contact with the surrounding air (see Fig. 11). Three temperature values (25°C, 80°C, 150°C) and two displacement speed values (25 µm/s and 250 µm/s) were combined. The temperature and cycling speed variables were selected based on the measurements taken on the test bench and the load speed resulting from the thermal cycling. At least three samples were tested in each case. Fig. 12 shows the sample structure based on the use of an FR4 U-shaped supporting frame to (i) prevent damage or plastic deformation when handling the sample and mounting it in the testing device, (ii) achieve proper alignment of the sample, and (iii) prevent slippage in the grips during the test. The bridge between the two heads of the support frame is cut gently using a miniaturized circular saw after the sample has been mounted in the tensile device, before the test. The aluminum wire is glued to the support frame using an epoxy adhesive with high thermal performance. The stress-strain curves obtained in the six cases are shown in Fig. 13. The first point to observe is an initial apparent stiffening of the sample that is only due to final sample realignment and straightening in the machine. This prevents measuring Young's modulus reliably. That is why samples are unloaded/reloaded after plasticity occurs. Two subsequent unloading/reloading operations were done later in the plastic zone to check the possible damage occurrence (i.e., decrease of Young's modulus). Lastly, all the samples of a given testing parameter combination provide the same stress-strain response, confirming the repeatability of the tensile test. The data obtained in the six cases can thus be compared. Analysis of the six cases shows an expected result: the stress for a given strain decreases when the temperature increases. On the other hand, speed has a relatively weak influence, but a viscous effect is nonetheless observed on the plastic behavior (but not on the elastic behavior) and has been considered in the behavior models for this work. For this reasons, we chose a Perzyna-type visco-thermo-elasto-plastic model, with plastic hardening supposed to be isotropic [START_REF] Perzyna | Fundamental Problems in Viscoplasticity[END_REF]. Electro-thermomechanical results are described in the following sections. Finally, we note that even up to (viscoplastic) strain of about ten percent, the elastic modulus remains approximately constant, which legitimizes the fact that up to such a strain level, we neglect the effects of damage.

ELECTROTHERMAL RESULTS

In this section dedicated to thermal modelling, simulation results are shown. The input parameters are the heat sources of electrical dissipation (Joule effect). The heat diffusion equation then allows one to compute the temperature fields within the Three different cycling frequencies, often encountered in accelerated tests, were applied: 0.1 Hz, 1 Hz, and 5 Hz [START_REF] Choi | Advanced Accelerated Power Cycling Test for Reliability Investigation of Power Device Modules[END_REF]. The amplitude of temperature oscillations on junctions was set to ∆Tj = 50°C for all tests with a base plate module temperature of Tref = 80°C (Fig. 4). To obtain the expected power cycling conditions, the electrical parameters, shown on the Tab. 2, have been adjusted for each test protocol by using quantitative thermal imaging [START_REF] Chrysochoos | Infrared thermography applied to the analysis of material behavior: a brief overview[END_REF] of specific modules modified for this purpose (silicone gel removed, application of black paint). These modifications allow one to improve the module emissivity and thus the measurement quality. * The trapezoidal current mode is a current variation form that allows to maximize the temperature cycle amplitude while respecting the maximal current in every IGBT [START_REF] Forest | A Power Cycling Test Bench Dedicated to the Test of Power Modules in a Large Range of Cycling Frequency[END_REF]. An example of this current variation is given on Fig. 9.

These electrical parameters are used in the electro-thermal simulation. To be able to compare the temperatures measured experimentally and those obtained in simulation, a measurement zone (Fig. 14) is determined in simulation corresponding to the measurement area of the thermal camera on the test bench. The final temperature is the average of the temperatures of each element in the defined area. To check the relevance of the thermal computation, a comparison between FE temperature and thermal infrared measurement has been performed. Infrared thermal images were extracted from a previous study aiming at interpreting the influence of the rate of thermal loading on the ageing of connections. Experimental thermal data were then compared with the ones obtained via FE simulation in the same loading conditions. Fig. 15 shows the shapes of the temperature oscillations, first calculated by finite element (FE) simulation applied to a complete module, then measured by infrared camera (FLIR SC6) on the specific modules. The FE computations are also required for the thermo-mechanical part and will be resumed and completed in section 5. For 0.1 Hz and 1 Hz, the load-current is sinusoidal. For 5 Hz, the current is trapezoidal, this shape being the only one able to induce a temperature variation of 50°C in accordance with the allowed current of the present test bench. In each case, the minimal temperature is close to 80°C.

Small differences between experimental and simulated data can be observed. They deal exclusively with the temperature amplitudes and do not exceed a few percent (5-10%). Several hypotheses can be put forward to explain these differences. First, the infrared thermography measurement accuracy can be evoked. The module may be poorly prepared (silicone gel removed, application of black paint, parasitic radiations, etc,..). Second, small heterogeneities of thermal characteristics in the constitutive materials may exist and are not considered in the computation.

This confrontation allows one to verify the correct integration of the heat equation, defined in Eqs [START_REF] Schilling | Power cycling testing and FE modelling focussed on Al wire bond fatigue in high power IGBT modules[END_REF][START_REF] Majumdar | Power module technology for home power electronics[END_REF] and of the analytical model of electric losses, defined in Eqs [START_REF] Pedersen | Dynamic Modeling Method of Electro-Thermo-Mechanical Degradation in IGBT Modules[END_REF][START_REF] Forest | Fast power cycling protocols implemented in an automated test bench dedicated to IGBT module ageing[END_REF]. These first checks are necessary before considering the analysis of mechanical stresses induced by thermal loading and responsible for the degradation of connections in power modules.

THERMOMECHANICAL RESULTS

The heat diffusion having a regularizing effect, one can often be satisfied in thermal FE calculation with meshes much less fine than for a mechanical or thermomechanical calculation where strong coupling mechanisms may be present. Consequently, the mesh size has been refined in the thermomechanical simulations. Indeed, we must deal with thin layers and multi-material interfaces where stress concentrations are likely to develop. From a practical standpoint, it is worth noting that the surfaceto-thickness ratio is very high (e.g., chip: 10 mm x 10 mm x 70 µm). A standard mesh size allowing us to correctly characterize the thickness of the component would produce too many meshes on the surface, considerably increasing both the number of values to store and the CPU time. The method used here consists of defining a suitable mesh on the surface and then extruding this mesh through the thickness of the thin layer using a reasonable number of elements (5 for example). Fig. 16 illustrates the mesh thus created on the chip. Fig. 16 shows two areas where the mesh size was refined. These are the contact surfaces of the wire-bond, areas where high stress gradients can develop.

Once the computations are made for a given cyclic thermal loading, the 3D temperature, strain, and stress fields are exported from Comsol® to Matlab® to use the data processing and graphic visualization tools of that software.

A first way to check the computation relevance is to extract the pure elastic response from the overall thermo-elastoviscoplastic response (Eqs. [START_REF] Yang | Automotive Power Module Packaging: Current Status and Future Trends[END_REF][START_REF] Van Der Broeck | A Test Bench for Thermal Characterization of IGBT Power Modules Over Mission Profiles[END_REF][START_REF] Forest | Use of opposition method in the test of highpower electronic converters[END_REF]). To do this, it is sufficient to plot the evolution of each component over time for the stress tensor σ according to each elastic strain component.

For example, from Eq. ( 5), the component 𝜀 is derived:

𝜀 = 𝜀 + 1 + 𝜈 𝐸 𝜎 - 𝜈 𝐸 (𝜎 + 𝜎 + 𝜎 ) + 𝜆 𝜃 (12)
where 𝜃 = 𝑇 -𝑇 , or conversely:

𝜎 = 𝐸 𝜀 -𝜖 + 𝜈 𝐸 (𝜎 + 𝜎 ) -𝜆 𝜃 (13)
The correspondence, throughout the simulation duration and at any point of the structure, between 𝜎 and the quantity (𝜀 -𝜀 + (𝜎 + 𝜎 ) -𝜆 𝜃) computed using the FE data, must theoretically represent a line whose slope is equal to the elastic modulus 𝐸.

In Fig. 17, the points obtained were superimposed on the theoretical response curve. A second way to verify the relevance of the thermomechanical results is to consider the trace of the viscoplastic strain tensor. In the model, the viscoplastic transformation is supposed to be isovolumic. Throughout thermal loading, we verified that the maximal value over the ZOI of tr(𝜀 ) was less than 10 . These different findings allowed one to check the correct resolution of the thermomechanical equations, defined in Eqs [START_REF] Yang | Automotive Power Module Packaging: Current Status and Future Trends[END_REF][START_REF] Van Der Broeck | A Test Bench for Thermal Characterization of IGBT Power Modules Over Mission Profiles[END_REF][START_REF] Forest | Use of opposition method in the test of highpower electronic converters[END_REF], by the FE software.

Given the complexity of the thermal loading and the composite nature of the module, a heterogeneous thermomechanical response is expected. An illustration of this heterogeneity is proposed in Fig. 18 where the distribution of the von Mises equivalent stress around the bonding feet has been plotted. The calculation shows that the stress level has reached the viscoplastic flow limit in several places. Another advantage given by the numerical simulation is the possibility of following the evolution of the thermal and viscoplastic state cycle by cycle. From this perspective, three small material zones (named "points" in Fig. 19) were chosen at the interface between the bonding wire and the chip (Fig. 19). Each "point" has been indeed associated with 4 meshes. The thermomechanical data displayed in Fig. 20 are the average temperature, equivalent stress and viscoplastic strain associated with each zone. These points were chosen to better visualize the heterogeneity of the mechanical loading at the connection and to highlight the rapid increase of the equivalent viscoplastic strain (Eq. 8) since the very beginning of the cyclic loading. In Fig. 20 the evolution during the first one hundred cycles is plotted. For visibility reasons, the first 5 cycles (0-5s) and the last 5 cycles (95s-100s) are shown. The thermal loading frequency was 1 Hz (maximum frequency of accelerated testing representative of wirebonds ageing [START_REF] Czerny | Thermo-mechanical analysis of bonding wires in IGBT modules under operating conditions[END_REF]). The electrical parameters used for this simulation were identical to those indicated in Tab. 2. Fig. 20.a shows the temperature of the three points at the interface. The good diffusivity of the constituent materials leads to three temperatures that are almost identical. A gradual increase of the mean temperature per cycle can be observed. However, this warming is limited by heat losses with the surroundings by the lower elements of the module assembly (high heat capacity), themselves increasing with the temperature (Fourier TBC). Quantitatively, in this example, the mean temperature over a thermal cycle rose from about 50°C for the first simulation cycles to 80°C for the last cycles. This transient step was shortcut and symbolized by an oriented dashed line on Fig. 20. After 100 loading cycles, the thermal cycle cannot yet be considered as stabilized, (stabilization around 500 cycles to have the 80-130°C cycling). However, this number of cycles allows one to understand the fatigue mechanisms of wire bonds and justify the necessity to create a damage model while having a correct time computation (2 weeks).

In Fig. 20.b, the evolution of the von Mises stress during a cycle was plotted for the three points. Some slight differences in the stress level can be observed from one point to another. Furthermore, note that the von Mises equivalent stress by construction is a norm of the deviatoric stress tensor and must therefore always be positive. During the warming and cooling stages of the thermal cycle, tension and/or compression stresses can be observed periodically, which explains the presence of two different oscillations per cycle. Lastly, because of the gradual saturation of stresses during viscoplastic hardening (cf. Fig. 13), the von Mises equivalent stress becomes periodic rapidly. The stress-strain response stabilizes, inducing a hysteresis loop, and a viscoplastic shakedown occurs progressively. Fig. 20.c shows the evolution of the equivalent viscoplastic strain 𝑝. Its rate must be, by construction, positive (Eq. ( 9)), and therefore 𝑝 only increases cycle by cycle. Saturation of this variable is not possible unless the viscoplastic effects disappear (elastic shakedown). To take the large variations of 𝑝 during the 100 cycles into account, two y-scales were introduced on each side of the graph. This is why the stabilization of 𝑝 seems to occur, but it is only a scale effect. The monotonous growth of 𝑝 depicts the irreversibility of the viscoplastic mechanisms. The numerical calculation still allows access to each component of the stress and strain tensors. To further illustrate the complexity of thermomechanical loading for the three selected points, we plotted in Fig. 21, the paths of principal strain in the 3D space.

To make visible the strong evolution of these deformations, as before, the first and last 5 loading cycles have been plotted. We find the heterogeneous and sometimes multiaxial character of the strain. Between the two blocks of cycles, we find a strong increase of deformation whatever the point considered. During the last 5 cycles, a progressive stabilization of the strain path can be observed. As already mentioned, the presence of a hysteresis loop is associated with the growth of p. These three trajectories show significantly different evolutions in terms of both the level of strains and the shape of the loops.

Lastly, the corresponding 3 stabilized stress loops are plotted in Fig. 22. This figure confirms once again the multiaxial, heterogeneous, and irreversible character of the mechanical response of the wire bond junctions. Once the cyclic stabilization is reached, if the thermal loading is not changed, the cyclic creep-fatigue mechanisms will gradually set in. The thermomechanical model, as proposed in Section 2, does not, in its current state, predict this progressive damage. The consideration of fatigue effects will be presented in a future work. The objective will be to describe the influence of the progressive damage of the component on the electric flows and thus on the distribution of the dissipative sources. In what follows, the outline of what prospective tools will be used to account for damage is briefly evoked.

SOME WORDS ABOUT COHESIVE ZONE MODEL

For the last thirty years, the consideration of damage in the structure design has been increasingly important. There are currently two paths to take charge of this progressive degradation of materials and structures, either in a volumetric way (continuum damage mechanics [START_REF] Chaboche | Continuous damage mechanics -A tool to describe phenomena before crack initiation[END_REF], or in a surface way [START_REF] Dugdale | Yielding of steel sheets containing slits[END_REF]. In the future works, cohesive zone models will be associated to the volumetric finite element model already used. Indeed, cohesive-zone models (CZM), which were first introduced through the pioneering work of Barenblatt [START_REF] Barenblatt | The Mathematical Theory of Equilibrium Cracks in Brittle Fracture[END_REF], are suitable for simulating fractures in a wide range of materials and to account for heterogeneities at various scales from the grain to the structure [START_REF] De Borst | Meshindependent discrete numerical representations of cohesive-zone models[END_REF]. In the so-called 'cohesive-volumetric finite element' framework, CZMs are introduced at interfaces between adjacent elements of a finite element discretization (see Fig. 23 : Basic sketch of a specimen broken in uniaxial tension test by using the cohesive/volumetric finite element approach.). They have been successfully used to simulate and predict the entire fracture process from crack onset to rupture, including crack growth, propagation, potential bifurcation, multiple fracturing, etc. [START_REF] Perales | A NonSmooth Contact Dynamics-based multi-domain solver[END_REF]. Cohesive zone models are embedded along connected meshes and describe the traction-separation relationship as a softening curve. When the traction vanishes, the cohesive bond is broken, and the crack occurs (the resulting crack path can be complex; this figure exhibits a straight crack for sake of simplicity). The diagram 𝑅𝑐-[𝑢] represents the so-called traction separation law where [𝑢] is the displacement jump and 𝑅 the cohesive force.

CONCLUDING COMMENTS

This paper presents the development of a numerical modeling approach focused on bonding ageing of power modules. It seeks to systematically consider the various dissipative and multiphysical coupling mechanisms involved in ageing. The paper focuses on the modeling of electrothermal and thermomechanical effects. The main result is the identification of significant viscoplastic strains of the bonding wires from the very beginning of the loading. Locally, the loads appear to be very heterogeneous and multiaxial. The cyclic viscoplastic shakedown is reached only after several hundreds of cycles, after which the damage mechanisms become preponderant. The strategy to integrate these damage effects in the model, and their consequences on the distribution of current densities within the component, will be based in a future work on the use of cohesive zone models (CZM) [START_REF] Richefeu | Toward local identification of cohesive zone models using digital image correlation[END_REF].

Fig. 1 :

 1 Fig. 1 : SEM images of metalization degration (left) and wirebond lift off (right)

Fig. 2 :

 2 Fig. 2 : IGBT Power Module used as test sample

Fig. 3 :

 3 Fig. 3 : Test benches

Fig. 4 :

 4 Fig. 4 : Example of stabilized thermal cycles of IGBT die

Fig. 5 :

 5 Fig. 5 : View of lift-off in a tested sample

Fig. 6 :

 6 Fig. 6 : Life time curves : experimental and Held Law comparison

Fig. 7 :

 7 Fig. 7 : Basic sketch of multiphysical couplings inducing fatigue, leading liftoff of the wire bond

Fig. 8 :

 8 Fig. 8 : Diagram of a test bench power section

Fig. 9 :

 9 Fig. 9 : (a) Inductor current; (b) IGBT and diode losses over a period cycling

Fig. 10 :

 10 Fig. 10 : Thermal field of the complete module Tab. 1 : Physical characteristics of module materials Al Bonding Si Chip SnAg Soldering Heat conduction 𝑘 (𝑊/𝑚. 𝐾) 238 130 30 Thermal expansion 𝜆 (𝑝𝑝𝑚/𝐾) 23 2.6 21

Fig. 11 :

 11 Fig. 11 : Illustration showing the role of the coupling operators; recovering the thermal boundary conditions at the ZOI boundary; and presenting the mechanical boundary conditions To check the relevance of the Voce and Perzyna models mentioned above, six different experimental tensile tests were performed to identify the thermomechanical parameters of behavior using a TA Electroforce® 3230 mechanical testing instrument equipped with a hot-cold chamber, a 20N load cell, controlled by dedicated WinTest® 7 software.

Fig. 12 :

 12 Fig. 12 : Tensile test samples

Fig. 13 :

 13 Fig. 13 : (a) Uniaxial stress-strain curves -(b) Verification of the correct implementation of hardening curves

  m/s expe 25°C -25 m/s simu 80°C -250 m/s expe 80°C -250 m/s simu 150°C -25 m/s expe 150°C -25 m/s simu module once thermal boundary conditions are defined. The first step is therefore to define the heat sources distribution.

Tab. 2 :

 2 Power

Fig. 14 :

 14 Fig. 14 : Thermal Measurement Area.

Fig. 15 :

 15 Fig. 15 : Junction temperature waveforms during Power Cycling for 0.1 Hz, 1 Hz and 5

Fig. 16 :

 16 Fig. 16 : Meshing of a thin layer.

Fig. 17 :

 17 Fig. 17 : Checking thermomechanical computations: this example shows the 𝜎 component of the stress tensor.

Fig. 18 :

 18 Fig. 18 : Von Mises' equivalent stress distribution in wire bond.

Fig. 19 :

 19 Fig. 19 : Selection of three points at the wire bond interface.

Fig. 20 :

 20 Fig.20: Time courses of (a) temperature, (b) von Mises equivalent stress, and (c) accumulated viscoplastic strain of the three selected points (Fig.19) of the wire bond junctions.

Fig. 21 :

 21 Fig. 21 : Complete strain paths of the 3 selected points in the principal strain space.

Fig. 22 :

 22 Fig. 22 : Stabilized stress loops of the three selected points in the principal stress space.

Fig. 23 :

 23 Fig. 23 : Basic sketch of a specimen broken in uniaxial tension test by using the cohesive/volumetric finite element approach.


PhD Ghaffar Muharram
email: ghaffar.muharram@pasteur-lille.fr

Marion Thépaut

Pierre-Emmanuel Lobert

Teddy Grandjean

Olivier Boulard

Myriam Delacre

Emmrich Wakeford

Richard Wheeler

Lionel Franz Poulin

Ivo Gomperts Boneca

Frank Lafont

Marie-Cécile Michallet

Didier Hober

Ken Cadwell

PhD Mathias Chamaillard
email: mathias.chamaillard@inserm.fr

K C Has

NOD2 dependent signalling exacerbates the intestinal epithelium injury and limits murine Norovirus propagation

Keywords: NOD2, Norovirus, Inflammation, Colitis, Macrophages, Signalling

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

Abstract. Over 90% of epidemic nonbacterial gastroenteritis are caused by human Noroviruses (NoV) which are persisting in a substantial subset of people allowing their spread worldwide. It leads to a significant number of endemic cases and up to 70,000 children deaths in developing countries. NoVs are primarily transmitted through the fecal-oral route. To date studies have focused on the influence of the gut microbiota on viral clearance by enteric immunity. In this study, the use of the persistent mouse Norovirus S99 strain (MNoV_S99) allowed us to provide evidence that bacterial sensing by nucleotide-binding oligomerization domain 2 (Nod2) promotes the noroviral associated pro-inflammatory pathology in a dextran sodium sulphate induced colitis mouse model. By contrast, the establishment of the pathology in the colon is improved in the absence of Nod2. We also show in our cellular models a greater phosphorylation of Signal Transducer and Activator of Transcription1 (STAT1) in macrophages infected by MNoV_S99 when compared with mock-infected cells. This increased STAT1 phosphorylation is associated with an induction of NOD2 expression in macrophages. This in turn enhances myeloid cells response to muramyl dipeptide (MDP) resulting in downstream pro-inflammatory cytokine secretion. Hence, our results uncover a previously unidentified virus-host-bacterial interplay that is particularly important for protecting against several common illnesses, such as Crohn's disease.

Introduction.

With the development of high throughput sequencing techniques, we are progressively gaining a better knowledge of the composition of the enteric virome (ensemble of gut virus communities) [START_REF] Cadwell | Expanding the role of the virome: commensalism in the gut[END_REF] . This is of particular importance for unravelling the pathogenesis of viruses that are establishing persistent infections [START_REF] Norman | Disease-specific alterations in the enteric virome in inflammatory bowel disease[END_REF][START_REF] Pfeiffer | Viral immunity. Transkingdom control of viral infection and immunity in the mammalian intestine[END_REF] . Amongst enteric viruses yielding proinflammatory phenotypes, noroviruses (NoV) are responsible for the majority of non-bacterial gastroenteritis worldwide. These single stranded RNA (+) viruses, belonging to the family of Caliciviridae have been studied for several years. Until recent progress in cultivation methods of noroviruses from genogroups I, II, IV, VIII and IX [START_REF] Ettayebi | Replication of human noroviruses in stem cell-derived human enteroids[END_REF][START_REF] Estes | Human Norovirus Cultivation in Nontransformed Stem Cell-Derived Human Intestinal Enteroid Cultures: Success and Challenges[END_REF][START_REF] Ludwig-Begall | Noroviruses-The State of the Art, Nearly Fifty Years after Their Initial Discovery[END_REF] that infect humans (HuNoVs), mechanistic details were mostly obtained from studies using the mouse norovirus (MNoV) [START_REF] Newman | Norovirus immunology: Of mice and mechanisms[END_REF] .

Indeed, MNoVs can be easily produced in vitro in macrophages or dendritic cells [START_REF] Wobus | Replication of Norovirus in cell culture reveals a tropism for dendritic cells and macrophages[END_REF] . Several MNoV strains have been described based on their shedding time in the stool and classified as persistent or acute (non-persistent) strains [START_REF] Thackray | Murine noroviruses comprising a single genogroup exhibit biological diversity despite limited sequence divergence[END_REF] .

Multiple intracellular pattern recognition receptors (PRR) have been associated with indirect noroviral detection, especially during NoV genome replication, when double stranded RNA intermediates are generated [START_REF] Thorne | Norovirus gene expression and replication[END_REF] . The Toll like receptor 3 (TLR3) that recognizes any dsRNA in the endosomal compartment and the melanoma differentiation associated protein-5 (MDA-5), a member of the RIG-I like receptor (RLR) family that recognizes long dsRNA, have been shown to participate in the detection of the MNoV_1 strain and its plaque isolate the MNoV_CW3 [START_REF] Mccartney | MDA-5 recognition of a murine norovirus[END_REF] . Amongst the Nod like receptor (NLR) family, Nlrp6 was shown to bind with the encephalomyocarditis virus (EMCV), a single strand RNA (+) enteric virus, via the RNA helicase Dhx15. Following the formation of these complexes, interferon (IFN) type I and III signalling responses are triggered via the recruitment of the mitochondrial antiviral signalling (MAVS) protein. Interestingly, this study also showed that, Nlrp6 -/-mice infected with MNoV_1 had higher viremia, suggesting similar mechanisms of NoV sensing by Nlrp6 [START_REF] Wang | Nlrp6 regulates intestinal antiviral innate immunity[END_REF] .

Another member of the NLR family, the nucleotide-binding oligomerization domain 2 (NOD2) mostly known as a sensor of muropeptides from Gram positive and negative bacteria [START_REF] Chamaillard | Nalps and Naip: intracellular regulators of bacterial-induced inflammation[END_REF] , was also shown to directly bind with the genome (ssRNA-) of the respiratory syncytial virus (RSV) to induce antiviral responses [START_REF] Sabbah | Activation of innate immune antiviral responses by Nod2[END_REF] . The recruitment of MAVS by NOD2 was needed to activate the interferon-regulatory factor 3 (IRF3) dependent signalling and provoke IFN- production. So far, no PRR has been identified as a direct sensor of noroviruses. However, NOD2 dependent signalling was shown to potentiate gut inflammation and increase lethality in an E. Coli and MNoV_1 co-infection mouse model [START_REF] Kim | Viral infection augments Nod1/2 signaling to potentiate lethality associated with secondary bacterial infections[END_REF] .

Another molecular player that is essential to activate antiviral signalling is the cytosolic protein signal transducer and activator of transcription 1 (STAT1). MNoV_1 infections were shown to be lethal in STAT1 knock-out mice [START_REF] Karst | STAT1-dependent innate immunity to a Norwalk-like virus[END_REF] . And STAT1 dependent interferon responses were shown to limit MNoV_1 replication and dissemination in vivo [START_REF] Mumphrey | Murine norovirus 1 infection is associated with histopathological changes in immunocompetent hosts, but clinical disease is prevented by STAT1-dependent interferon responses[END_REF] .

Owing to the variety of circulating NoV strains, strain-dependent effects are expected to occur and should be studied more in details. Even a single amino acid change in the NS1/2 viral protein is sufficient to switch the acute MNoV_CW3 strain to a persistent state [START_REF] Nice | A singleamino-acid change in murine norovirus NS1/2 is sufficient for colonic tropism and persistence[END_REF] , showing the high degree of adaptability of NoV genomes and hence their versatility to counteract host defence mechanisms. Type III interferon (IFN-λ) response has been shown to control the in vivo propagation of the persistent MNoV_CR6 strain [START_REF] Nice | Interferon-λ cures persistent murine norovirus infection in the absence of adaptive immunity[END_REF] , while type I and II interferons mediate innate immune responses against acute MNoV infections [START_REF] Karst | STAT1-dependent innate immunity to a Norwalk-like virus[END_REF][START_REF] Hwang | Nondegradative role of Atg5-Atg12/ Atg16L1 autophagy protein complex in antiviral activity of interferon gamma[END_REF] .

Interestingly, Cadwell and colleagues have demonstrated how infection with the MNoV persistent strain CR6 rendered Atg16l1 HM mice more susceptible to dextran sodium sulphate (DSS)-induced colitis [START_REF] Cadwell | Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine[END_REF] . Functional autophagy is critical for microbial elimination. And NOD2's interaction with ATG16L1 was shown to regulate the autophagy clearance of pathogenic bacteria [START_REF] Travassos | Nod1 and Nod2 direct autophagy by recruiting ATG16L1 to the plasma membrane at the site of bacterial entry[END_REF] . Improper detection of microbial content in the digestif tract leads to intestinal inflammation [START_REF] Maloy | Intestinal homeostasis and its breakdown in inflammatory bowel disease[END_REF] and loss-of-function point mutations of either NOD2 or ATG16L1 predispose humans to Crohn's disease [START_REF] Hugot | Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease[END_REF][START_REF] Hampe | A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1[END_REF] . Insights into the mechanisms of how the loss of tolerance of NOD2 against gut microbial communities can lead to uncontrolled proinflammatory signalling are still missing.

In this study, we have investigated the underlying mechanism of the pathogenesis of the persistent MNoV strain S99 (Berlin) [START_REF] Niendorf | Infection with the Persistent Murine Norovirus Strain MNV-S99 Suppresses IFN-Beta Release and Activation of Stat1 In Vitro[END_REF] . MNoV_S99 dependent inflammatory response to DSS was compared in WT vs Nod2 -/-mice. NOD2-dependent signalling pathways activation was further examined in response to MNoV_S99 during early or prolonged times of infection in Raw264.7 cell line, bone marrow derived macrophages, monocytes and dendritic cells.

Finally, we have evaluated whether the priming effect of MNoV_S99 can fine-tune Nod2 dependent pro-inflammatory signalling.

Results.

MNoV_S99 exacerbates the DSS-induced inflammation

Since MNoV_S99 is a natural strain commonly found in animal facilities that is able to establish persistent infections [START_REF] Niendorf | Infection with the Persistent Murine Norovirus Strain MNV-S99 Suppresses IFN-Beta Release and Activation of Stat1 In Vitro[END_REF] , we decided to assess MNoV_S99's implication in the vulnerability to inflammatory responses of the intestinal mucosa. Hence, we examined MNoV_S99's impact in a chemically-induced colitis model. C57BL/6J mice were first infected with a single dose of 10 7 TCID 50 PFU/mL of MNoV_S99 or mock-treated with PBS.

One week later, drinking water was replaced with either a 3% DSS or 5% DSS solution for a period of seven consecutive days (Fig. 1A). Weight-loss was monitored on a daily basis. With both concentrations of DSS, the pre-treatment with MNoV_S99 had induced a more significant weight loss (purple and orange curves Fig. 1B andC). After 7 days of DSS treatment, mice were sacrificed. Histological analysis of colon sections revealed an increased infiltration of inflammatory cells and epithelial damage in infected mice treated with DSS when compared to similarly challenged animals that were not infected (Fig. 1D). Next, we quantified the muscle thickness from these colon sections. An increased thickness was measured in mice treated with the virus in presence of both 3 and 5% DSS in comparison with Mock-treated control mice (Fig. 1E). Similarly, histological scoring of the haematoxylin and eosin (H&E) stained colonic sections expectedly showed significantly more severity in mice treated with MNoV_S99 + 3% or 5% DSS than DSS alone (Fig. 1F). Upon mice sacrifice, resected colons were measured. A significantly reduced length was obtained in mice infected with MNoV_S99 and treated with 5% DSS in comparison with non-infected control animals (PBS) (Fig. 1G). The increased level of colitis was also evidenced by a significantly augmented secretion of the pro-inflammatory cytokine interleukin-6 (IL-6) in the supernatants of over-night in vitro cultured colon explants from MNoV_S99 infected + 5% DSS treated mice versus those from DSS only mice (Fig. 1H). Taken together, these data clearly show a much worse inflammation state in mice infected with MNoV_S99 in our chemically induced colitis model.

MNoV_S99 associated inflammation is abrogated in Nod2 -/-mice

In cells from the myeloid lineage, NOD2 was shown to participate in the sensing of Citrobacter rodentium and of commensal bacteria that are translocating in response to chemically-induced epithelial injury [START_REF] Hrdý | Lactobacillus reuteri 5454 and Bifidobacterium animalis ssp. lactis 5764 improve colitis while differentially impacting dendritic cells maturation and antimicrobial responses[END_REF] . To determine whether NOD2 is also implicated in the immunopathology of MNoV in our preclinical model of colitis, we compared the severity of DSS-induced colitis in the presence of MNoV_S99 or not between WT and Nod2-deficient mice. In contrast to what was observed in WT mice (Fig. 2A), the sensitizing effect of the virus on the daily weight-loss was absent upon MNoV_S99 infection of Nod2 -/-mice (Fig. 2B). These data suggest that the pro-inflammatory effect of MNoV_S99 observed in WT mice is a consequence of a loss of tolerance to bacterial MDP. Indeed, DSS induces an abrasion of the epithelial barrier allowing commensal bacteria to cross the mucosa and unleash a pro-inflammatory response from Nod2 expressing cells. This was further confirmed after mice were autopsied. Colon tissue sections staining confirmed the decreased levels of colonic inflammation from MNoV_S99 infected Nod2 -/-vs WT mice (Fig. 2C). In agreement, muscle thickness quantification from these colon sections showed significantly increased thickness in MNoV_S99-infected WT mice under 3% DSS treatment vs 3% DSS alone (Fig. 2D). While in Nod2 -/-mice subjected to similar conditions, the excess of inflammation caused by the viral infection was lost. Consequently, the variations of colon length measurements that were induced upon MNoV_S99 infection were absent in Nod2 -/-mice when compared to WT infected mice (Fig. S1A). Similarly, differences in histological scores measured from MNoV_S99-infected WT mice vs DSS alone were also lost in Nod2 -/-infected mice (Fig. S1B). And we could observe a clear trend in the increased levels of secretion of Tumour necrosis factor  (TNF) in the supernatants of in vitro cultured colon explants from WT mice infected with MNoV_S99 and treated with 3% DSS vs those with DSS only. This effect was absent in the supernatants from Nod2 -/-mice infected and treated similarly (Fig. S1C).

Overall, these data show the importance of bacterial sensing by NOD2 and the excessive inflammatory response measured in the presence of MNoV_S99 in our DSS-induced colitis mouse model.

MNoV_S99 associated inflammation relies on NOD2 associated signalling

Mouse noroviruses are known to have a tropism for the myeloid cell lineages, namely macrophages and dendritic cells. Recently it was shown that Tuft cells belonging to the gut epithelial cellular lineages, that possess the CD300lf MNoV receptor can also be targeted by the MNoV_CR6 strain [START_REF] Wilen | Tropism for tuft cells determines immune promotion of norovirus pathogenesis[END_REF] .

Making use of the GFP-Nod2 reporter mice line, we first examined in which cellular lineages Nod2 is expressed. We failed to observe GFP-Nod2 expression in epithelial cells, while plenty of signal was detectable in the bulk of the cells bellow the lamina propria where myeloid cells are located (Fig. 3A). Hence, our investigations regarding NOD2 expression variations in response to MNoV_S99 infection focused on macrophages and monocytes in our in vitro cellular models.

Interferon production being one of the most common response to the intracellular presence of RNA genomes of foreign origin, we examined if the STAT1 signalling pathway that lays upstream of ATG16l1-mediated interferon production was activated in response to murine norovirus infection.

To this end, bone marrow derived macrophages (BMDM) from WT mice were infected with several MNoV strains at a multiplicity of infection (Moi) of 1, for 1h and pSTAT1 levels of expression from cell lysates were measured by western blot (Fig. 3B). In contrast to the nonpersistent strain MNoV_CW3 that caused a striking increase in pSTAT1 level, the persistent strains S99 and CR6 infections induced a much milder STAT1 signalling pathway activation.

The CW1 strain was associated with the lowest level of activation. Since MNoV_S99 is a natural strain commonly found in animal facilities that is able to establish persistent infections, we further analysed the mechanistic details behind its infection in our cellular models.

Using the macrophage cell-line Raw264.7, we confirmed that MNoV_S99 is able to induce phosphorylation of STAT1 (Tyrosine 701) at different times at Moi 1 (Fig. S3). pSTAT1 was detectable as early as 15 minutes post-infection and its expression further increased at 30 min and 1h, while total STAT1 was detectable in control non-infected cells and its expression only slightly increased at later time-points. In addition to the STAT1 activation through the Janus kinases JAK1 and Tyk2, we also examined the status of NFκB activation, another proinflammatory signalling pathway. For this purpose, pIκBα levels were quantified from infected cell lysates. Despite basal low-level activation in mock-infected cells, pIκBα intensity increased slightly at 30 min and 1h post-infection. However, no significant increase was measured. When checking PI3K-AKT or the MAPK-ERK signalling axes in the same time frame, MNoV_S99 failed to induce any activation of these signalling pathways (Fig. S2). Next, we analysed the response of BMDM derived from WT or Nod2 -/-mice that were infected with increasing doses of MNoV_S99 for 30 or 60 minutes (Fig. 3C). STAT1 activation increased over the course of the acute infection in cells derived from WT mice. In contrast, the levels of pSTAT1 were barely detectable in Nod2 -/-cells at Moi 1. However, at a higher dose (Moi 5), Nod2-dependent STAT1 activation was only observable at the 30 minutes time point. At 60 minutes post-infection, the decrease in pSTAT1 levels in response to MNoV_S99 infection was not significant anymore when compared with WT cells.

Next, we examined if Stat1 activation was detectable after prolonged times of infection. Indeed, we measured significant differences in pStat1 expression levels between WT and Nod2 -/-MNoV_S99-infected cells 24h post-infection at Moi 0.1 (Fig. 3D).

To examine whether the Mitochondrial antiviral signalling (MAVS) adaptor protein is required for sensing MNoV_S99, BMDM derived from Mavs-deficient mice where compared to WT cells. Interestingly, the higher pStat1 levels measured in WT cells (Moi 0.1, 1h) were significantly decreased in cells derived from Mavs -/-cells (Fig. 3E).

Altogether, these data show a mild but significant activation of pro-inflammatory signalling pathways following MNoV_S99 infection in our in vitro cellular models. These signalling responses can be at least partially abrogated in the absence of MAVS and NOD2.

MNoV_S99 persistence is decreased with loss of tolerance to Bacterial MDP

To investigate the mechanisms behind the excessive inflammatory response induced by MNoV_S99 infection in our in vivo experiments, we further explored how NOD2 expression levels were affected by the viral infections.

NOD2 expression was determined by RT-qPCR in Raw264.7 cells or BMDM derived from WT mice treated with MNoV_S99. Interestingly, NOD2 expression levels are significantly increased in response to the viral infection in both cellular models (Fig. 4A and4B).

Thus, augmented NOD2 expression plus STAT1 activation following MNoV_S99 infection suggests a proinflammatory signalling loop, that could ultimately limit viral propagation on one hand and induce increased levels of inflammation on the other hand. The norovirus induced NOD2 up-regulation could explain the viral-induced pathogenesis that we observed in our colitis model.

Accordingly, when we analyzed the viral replication in BMDM derived from WT or Nod2 -/- mice infected in vitro with MNoV_S99 (Moi 0.1 for 24h), the quantity of viral genomes was significantly lower in the WT cultures (Fig. 4C).

Several reports in the literature have shown an interplay between gut microbiota composition and the intestinal epithelium that modulates the interferon response that in turn favors MNoV infection especially for the MNoV_CR6 persistent strain [START_REF] Nice | Interferon-λ cures persistent murine norovirus infection in the absence of adaptive immunity[END_REF] . WT and Nod2 -/-mice were cohoused for 4 weeks before we measured the viral load in stools by RT-qPCR after RNA extraction. In line with our in vitro data on viral replication in BMDM, we could not detect any viral genome in stools from non-cohoused WT mice (Fig. 4D). 5.49 10 2  1.78 10 2 mean ( sem) genome copies numbers were measured in Nod2 -/-mice co-housed with WT mice and 4.12 10 2  2.63 10 2 in WT mice co-housed with Nod2 -/-animals. These preliminary results need to be further explored but they hint towards the hypothesis that the knock-out of NOD2 favors the multiplication of one or more phylum of the gut microbiota that in turn allows a prolonged persistence of MNoV_S99.

In order to have a better comprehension of how these dual stimuli are articulated together, we examined Stat1 and IκBα signalling pathways activation in response to MNoV_S99 infection followed by secondary MDP (commonly expressed by all bacteria) treatment in monocytes isolated from WT mice. As previously shown with BMDM or Raw264.7 cells, MNoV_S99 infection did also induce Stat1 activation (Fig. 4E and4F) in monocytes. Similarly, IκBα was not activated by the viral infection alone. Though, relatively high basal levels were already detectable in mock-infected cells. As expected the treatment with the peptidoglycan MDP alone triggered potent IκBα activation without activating Stat1 (Fig. 4E, MNoV_S99-, MDP+ condition). Interestingly, while IκBα activation by single MNoV_S99 infection was not achievable (Fig. 4E, MNoV_S99+, MDP-condition), MNoV_S99 infection followed by secondary MDP treatment was associated with both pathways' moderate but significant activation (Fig. 4F, MNoV_S99+, MDP+ vs mock treated cells).

Similarly, we noticed a significantly augmented pro-inflammatory cytokine TNF response to MDP in dendritic cells that were first primed with Noroviral RNA (Fig. 4G).

Finally, we compared WT and Nod2 -/-derived BMDM cultures' TNF response to MNoV_S99 infection followed by MDP treatment. TNF levels were increased with increasing doses of virus in WT cells, while they remained undetectable (Moi 0.1) or much lower (Moi 1) in Nod2 -/-cells (Fig. 4H).

Overall, these in vitro data fit nicely with our in vivo models where excessive bacterialinduced inflammatory responses were observed when mice were infected with MNoV_S99 and treated with DSS. The increase in Nod2 transcription levels in response to noroviral infection favours in turn excessive inflammatory signalling pathways activation. This suggest that MNoV_S99 induces a loss of tolerance to bacterial MDP in WT mice.

Discussion

The breach in gut flora homeostasis termed dysbiosis is becoming central in the onset of chronic inflammatory bowel disease, such as Crohn's disease [START_REF] Hold | Role of the gut microbiota in inflammatory bowel disease pathogenesis: what have we learnt in the past 10 years?[END_REF][START_REF] Kim | Pathogenesis of Inflammatory Bowel Disease and Recent Advances in Biologic Therapies[END_REF] . With the development of molecular biology techniques, alterations in the virome relevant to disease pathogenesis are thoroughly under investigation 2 . The cytosolic sensor NOD2 is very well known for its pathogen associated molecular pattern recognition activity against bacteria [START_REF] Chamaillard | Nalps and Naip: intracellular regulators of bacterial-induced inflammation[END_REF] , and recently it appears it can also participate in detection of certain viruses [START_REF] Sabbah | Activation of innate immune antiviral responses by Nod2[END_REF] . Hence, the combination of genetic susceptibility factors and opportunistic viral infections could be one of the triggers of CD [START_REF] Simmons | Crohn's disease: Genes, viruses and microbes[END_REF] . Using pro-inflammatory signalling activation downstream of NOD2 as a read-out, we have analysed if NOD2 can also participate in MNoV detection. All our in vitro experiments with monocytic lineage derived primary cells (e.g. BMDM, BMDC and BM) and cell line (e.g. Raw264.7) infected with different MNoV strains showed activation of the STAT1 signalling pathway implicated in the pro-inflammatory IFN response. By contrast, these responses were abrogated in Nod2 -/-and Mavs -/-mice derived BMDM.

Amongst enteric viruses commonly found in the microbiota, the Norovirus is considered as a good candidate in the hypothesis of an environmental-driver in genetically susceptible hosts at the origin of Crohn's disease onset. MNoV_CR6 strain was shown to induce colitis in Atg16l1 HM mice presenting defective autophagy in a DSS colitis model [START_REF] Cadwell | Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine[END_REF] .

Several PRR are implicated in viral sensing. MDA-5 and TLR3 have been shown to participate in detection of either MNoV_1 or CW3 [START_REF] Mccartney | MDA-5 recognition of a murine norovirus[END_REF] . These are both strains which do not persist in vivo, contrary to the S99 (Berlin) MNoV strain [START_REF] Niendorf | Infection with the Persistent Murine Norovirus Strain MNV-S99 Suppresses IFN-Beta Release and Activation of Stat1 In Vitro[END_REF] that was used in our study.

Our in vivo data show significantly exacerbated inflammation when applying either 3% or 5% DSS colitis protocol to WT mice. The excessive MNoV-S99 associated inflammatory response was abrogated in Nod2 -/-mice or in BMDM derived from these mice showing NOD2's implication in the pathogenesis and the viral sensing respectively. The genetic ablation of NOD2 also improved the disease risk conferred by MNoV_CR6 in Atg16l1 HM mice (data not shown).

Hence, NOD2 dependent signalling adds a layer of regulation in viral sensing and finetunes the immunopathology levels caused by the infection. While in our Norovirus + DSS model knocking-down NOD2 prevents gut inflammation, it has the opposite effect with Influenza A virus (IAV) infection in the lung of Nod2 -/-or Ripk2 -/-mice [START_REF] Lupfer | Receptor interacting protein kinase 2-mediated mitophagy regulates inflammasome activation during virus infection[END_REF] . Indeed, the NOD2-RIPK2 signalling axe has been shown to downmodulate the NLRP3 dependent mitophagy activation and the associated inflammatory response in the lung tissue of infected mice.

Noroviruses propagation or prolonged persistence is tightly regulated by the interplay between several signalling pathways. MNoV_1 replication was shown to be inhibited in macrophages following LPS mediated NF-κB activation that in turn triggers IFN-β dependent JAK-STAT antiviral activity [START_REF] Yu | Lipopolysaccharide restricts murine norovirus infection in macrophages mainly through NF-kB and JAK-STAT signaling pathway[END_REF] . Interestingly, in our cellular models, in addition to the phosphorylation of STAT1 that we observed when cells were infected with MNoV_S99 alone, the subsequent treatment with MDP induced an increased NfκB signalling pathway via IκBα activation. The synergistic activation of these two pro-inflammatory signalling pathways results in the inhibition of the MNoV_S99 propagation on one hand and the augmented production of the pro-inflammatory cytokine TNF on the other hand. Hence, the aggravated inflammatory pathology we observed in our DSS colitis model can be explained by this NOD2-dependent dual signalling activation in MNoV_S99 infected mice. Similarly, in vitro the signalling and cytokine responses were NOD2 dependent since they were only observed in cultures derived from WT but not in Nod2 -/-mice, at least at a lower dose (Moi 0.1) of infection. At higher doses, NOD2-independent alternative mechanisms seem to be active to promote Stat1 activation and TNF production, but still at a lesser level than in WT cells.

In conclusion, our study shows a NOD2 dependent mechanism that prevents low level propagation of the persistent MNoV_S99 strain. When the noroviral infection is coupled with digestif tract injury a positive signalling loop generates a potent pro-inflammatory response that limits viral propagation.

Bone marrow derived macrophages (BMDM) were isolated from femurs of wild-type, Nod2 -/- or Mavs -/-mice of C57BL/6J background [START_REF] Michallet | TRADD protein is an essential component of the RIG-like helicase antiviral pathway[END_REF] . Briefly, bone marrow cells were flushed out of the bones, red blood cells were lysed using a 160mM NH4CL and 170mM Tris solution for 5 minutes at RT. 3 to 6.10 6 viable cells were plated in non-cell-culture-treated petri dishes and grown for 5-7 days in Iscove's modified Dulbecco's medium (IMDM), supplemented with 10% foetal bovine serum, 1% penicillin/streptomycin, 1% non-essential amino acid, 1% sodium pyruvate, 1% glutamine, and 20% (v/v) conditioned-media form L929 cells.

Bone marrow derived dendritic cells were isolated similarly as BMDM cells. Then, 2.10 6 viable cells were plated in non-cell-culture-treated petri dishes and grown for 7 days in RPMI 1640, supplemented with 10% FBS, 1% P/S, 1% L-glutamine, 1% HEPES and 20% (v/v) conditioned-media from J558 cell line producing murine granulocyte monocyte colony stimulating factor (GM-CSF).

Monocytes were isolated from bone marrow cells by using the mouse Monocyte Isolation Kit (Miltenyi Biotec) and a QuadroMACS separator.

The MNoV-S99 strain (GenBank accession no. DQ911368) was provided by Prof. P. Maris from ANSES Fougères Laboratory (France) and was propagated in RAW 264.7 cells.

Concentration of stock was determined by plaque assay. Mice were infected orally by pipette with 3x10 7 plaque forming units (PFU) resuspended in 25µL PBS.

For in vitro infection experiments, 3-5.10 5 viable cells were seeded in 6 wells plates. The next day, just before viral infection, cells were counted from 1 well to determine precisely the multiplicity of infection (Moi). In the remaining wells medium was removed and replaced with variable quantities of MNoV-S99 from a common viral stock in a final volume of 1 mL fresh medium per well. Mock treated cells were incubated in parallel with matching quantities of culture media from non-infected cells that was generated at the same time as viral stocks.

At various time-points after infection, supernatant was removed and cells were washed once with PBS, before lysis in RIPA buffer for WB analysis or RLT buffer for total RNA extraction (RNAeasy kit, Quiagen) for RT-qPCR experiments.

Immunohistochemistry, ELISA, western blot and RT-qPCR analysis

For immunohistochemistry analysis, formalin-fixed colon samples were embedded in paraffin. 5μm sections were stained with H&E. Slides were imaged with an AxioPlan 2 (Zeiss) microscope and blindly scored for inflammation severity by two investigators as previously described [START_REF] Wirtz | Chemically induced mouse models of acute and chronic intestinal inflammation[END_REF] .

For ELISA analysis, briefly 1cm colon samples were recovered from mice, luminal content was flushed with PBS, samples were cut opened and resuspended over-night in 250µl DMEM supplemented with 1% P/S in 24 wells plate at 37°C. The following day, supernatants were collected and stored at -80°C before cytokine measurement. TNF and Interleukin-6 proteins levels were measured specifically using pre-coated 96 wells ELISA plates (R&D System) following manufacturers indications.

For TNF ELISA from BMDC, 5.10 5 cells were plated in 24 wells plate and treated with MNoV_S99's ssRNA (extracted with RNeasy Quiagen kit)  MDP or MDP-DD (Invivogen) over-night and supernatants were measured for TNF levels.

For TNF ELISA from BMDM, 2.10 5 cells were plated in 96 wells plate infected with MNoV_S99 (Moi 0.1 or 1) for 6h prior to MDP treatment (10ug/mL) over-night and supernatants were measured for TNF levels.

For western blot analysis, total proteins from cell lysates were quantified by BCA protein assay kit (Pierce), equalized amounts of proteins (10-20μg) were resolved by 4-15% gradient SDS-PAGE gels and transferred to nitrocellulose membranes. Membranes were blocked in 0,05% PBS-Tween, 5% skimmed milk solution, before incubation subsequently with primary and secondary antibodies diluted (see Table 1) in blocking solution. Following secondary antibodies incubations, signals of interest were detected using a chemiluminescence reader (ImageQuant TM Las 4000, GE Healthcare Life Sciences), images were processed and band ROI were quantified with Image J software (NCBI). Mean band intensities ± SD of protein of interest were normalized to β-actin. Relative values to control mock-infected conditions were compared between different experiments.

For Nod2 quantification from MNoV_S99 infected BMDM or RAW264.7 cells, total RNA extraction was performed using the RNeasy Mini Kit (Qiagen). One µg of RNA was reversed transcribed in the presence of 2.5mM of oligo-dT using the Reverse Transcriptor kit (Roche) following the manufacturer's instructions. 75ng of cDNAs were amplified using Q5 High-Fidelity 2X Master Mix (New England BioLabs). Primers used in PCR amplification are available upon request.

Actb was used as an internal reference gene in order to normalize the transcript levels.

Relative mRNA levels (2 -ΔΔCt ) were determined by comparing (a) the PCR cycle thresholds (Ct) for Nod2 and Actb (ΔCt) and (b) ΔCt values for treated and control groups (ΔΔCt).

MNoV_S99 load was determined by measuring MNoV genome copy numbers by RT-qPCR analysis as previously described [START_REF] Hwang | Murine norovirus: propagation, quantification, and genetic manipulation[END_REF] . Briefly, total RNA was extracted from BMDM cell cultures using the RNeasy Mini Kit (Qiagen) and from stool using QIAamp Viral RNA Kit (Qiagen). 5µL of RNA were used per reaction of RT-qPCR. Viral genome levels were determined using the

Figures legends:

Figure 1 :

 1 Figure 1: MNoV_S99 aggravates inflammation in DSS-induced colitis A)-In vivo experimental design, C57BL/6J mice were orally gavaged with PBS or MNoV_S99 (10 7 TCID 50 PFU/mL), 7 days later, all the mice were given DSS in drinking water for a week. Mice were weighed daily for weight loss comparison. B)-3% DSS-induced colitis ± MNoV_S99 (n = 6). C)-5% DSS-induced colitis ± MNoV_S99 (n = 6). In B) and C), statistical differences were determined by 2 ways ANOVA test, * p < 0.05, ** p < 0.01, *** p < 0.001. D)-Representative images from H&E staining of colon sections from mock-treated (Control) or 3% DSS ± MNoV_S99 treated mice. Insets showing higher magnifications from each image are presented next to each image. E)-Muscle wall thickness was determined with Image J from colon section images. Percentages of increase are plotted after baseline subtraction relatively to the values obtained from Control mice (n = 6). F)-Histological intestinal epithelial inflammation scores comparison between Control, 3% or 5% DSS ± MNoV_S99 treated mice (n = 6). G)-Colon length comparison between Control and 5% DSS ± MNoV_S99 treated mice H-IL6 secretion levels from colon explants from mock or 5% DSS ± MNoV_S99 treated mice (n = 6). Statistical differences were determined with Student's t-Test in E, F, G and H.

Figure 2 :

 2 Figure 2: MNoV_S99 associated inflammation is NOD2 dependent Weight loss comparison in A)-3% DSS-induced colitis ± MNoV_S99 in WT mice (n=6) and B)-in Nod2 -/-mice (n=6), statistical differences were determined by 2 ways ANOVA test, * p < 0,05, ** p < 0,01, *** p < 0,001. C)-Representative images of H&E staining of colon sections from mice in A). Insets showing higher magnifications are presented below each image. D)-Percentages of increase of muscle wall thickness from colon section images are plotted after baseline subtraction relatively to the values obtained from WT mice treated only with DSS (n = 6), statistical differences were determined with Student's t-Test.

Figure 3 :

 3 Figure 3: MNoV_S99 associated pro-inflammatory signalling is Nod2 and MAVS-dependent in myeloid lineage cells A)-Representative image showing an intestinal villus from a Nod2 GFP mouse (GFP is shown in green, Phalloidin in grey and DAPI in blue); scale bare represents 50m. B)-Representative western blot showing STAT1 signalling pathway activation after 1h infection (Moi 1) with the mentioned MNoV strains in BMDM. Below are shown quantification of pSTAT1 bands intensities normalized to β-actin and relative to mock-infected cells (n = 3, mean ± SEM), statistical differences were determined by 1 way ANOVA test * p < 0,05, ** p < 0,005. C)-STAT1 activation in response to MNoV_S99 (Moi 1 or 5) in BMDM from WT vs Nod2 -/-infected for 30 or 60 minutes. The lower panel shows quantification of pSTAT1, bands intensities normalized to β-actin and relative to mock-infected cells (n = 3, mean ± SEM). Statistical differences were determined by 2 ways ANOVA test * p < 0,05. D)-STAT1 activation in response to MNoV_S99 (Moi 0.1) in WT or Nod2 -/-BMDM infected for 24h. Relative quantification of pSTAT1 (n = 3, mean ± SEM) is shown in the lower panel, statistical differences were determined by 2 ways ANOVA test *** p < 0,0005. E)-STAT1 activation in response to MNoV_S99 (Moi 0.1) in BMDM from WT vs Mavs -/-infected for 60 minutes. Relative quantification of pSTAT1 (n = 4, mean ± SEM) are shown below. The lower panel shows the mock vs Moi 0.1 infected cells quantification of pSTAT1. Statistical differences were determined by 2 ways ANOVA test * p < 0,05.

  Takyon™Dry No Rox One-Step RT Probe Mastermix (Eurogentec). Agilent Technologies). The quantification of viral genome copies was obtained using a standard curve with serial dilutions of the plasmid pMNoV1 corresponding to the full genome of MNoV1 (Dr Christiane Wobus, University of Michigan, USA).NOD2 GFP mice 37 in the C57BL/6 background were maintained at the Institut Pasteur Central Animal facility. We used heterozygous NOD2 GFP mice since the presence of one functional copy of NOD2 ensures that physiological responses dependent on functional NOD2 are not impaired. Heterozygous NOD2 GFP female mice (8-12 weeks of age) were sacrificed and several centimetres of the distal ileum were removed, opened longitudinally and washed in PBS. The tissue was spread flat over a 4% w/v LMP agarose (Merck) pad and fixed with 4% v/v paraformaldehyde (VWR). The ileal tissue was then embedded in a 4% LMP agarose block and cut into 200μm-thick sections using a Microm HM 650 V Vibration microtome.

	Viral Fwd_5'GTGCGCAACACAGAGAAAACG3' and Rev_5'CGGGCTGAGCTTCCTGC3' genomes were specifically amplified using the primers that target MNoV ORF1 region combined to a TaqMan probe using the Stratagene For staining, tissues were blocked, permeabilized and marked with an anti-GFP (rabbit polyclonal antibody A-11122) primary antibody and secondary Alexa Fluor 488 goat anti-rabbit antibody. Tissues sections were counter stained with DAPI (1µg/mL, BD Biosciences) and Phalloidin-iFluor 647 (1:200 dilution, Abcam and mounted using ProLong Gold Antifade reagent. Confocal acquisitions were performed using a Leica HyD SP5 confocal Mx3005P (NOD2 GFP immunohistochemistry imaging microscope. Image analysis was performed using Icy version 2.4.2.0 38 .

Acknowledgments:

For the NOD GFP tissue imaging, we thank the UTechS PBI, a member of the France-BioImaging infrastructure network supported by the French National Research Agency (ANR-10-INSB-04, Investissement d'Avenir program) for microscope usage and assistance. We thank the members of the Institut Pasteur Central Animal Facility for their assistance with animal studies. I.G.B. laboratory was supported by Investissement d'Avenir program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (ANR-10-LABX-62-IBEID), by the Investissement d'Avenir program (RHU Torino Lumière ANR-16-RHUS-0008), by the French National Research Agency (ANR-16-CE15-0021) and by R&D grants from Danone and MEIJI. M.C. was supported by grants from the Fondation pour la Recherche Médicale (DEQ20130326475) and the European Union's European Regional Development Fund. We thank T. Durand and MP. Fourmaux for excellent technical assistance.

data that support the findings of this study are available from the corresponding author, GM or MC, upon request on HAL

Methods:

Unless otherwise indicated, all media and reagents were purchased from ThermoFischer Scientific.

Colitis mouse model and viral infection

All animal experiments were approved by the local ethical committee n° CEEA-2016030717519903. Age and gender related Nod2 -/-and wild type littermates C57BL/6J mice (CDTA, Orléans, France) were housed 4-6 per cage, under strict pathogens-free environment.

Mice were gavaged with 10 7 TCID 50 PFU/mL of MNoV_S99 diluted in 200μl PBS or mock treated with PBS only. 7 days post-infection, drinking water was substituted with a 3-5% dextran sodium sulphate (DSS) (35,000-40,000 MW; TdB Consultancy) solution replaced every 2 days for a total duration of 7 days.

For cohousing experiments, WT or Nod2 -/-(n = 5 each) C57BL/6J mice (CDTA, Orléans, France) coming from non-specific pathogen free area were cohoused for 4 weeks. A group of non-cohoused WT mice (n = 5) were kept separated. Stools were collected and kept frozen (-80°C) until processed for RNA extraction and RT-qPCR measurement of viral genomes (see below).

Cells and virus

Mouse leukemic monocyte macrophage cell line RAW264.7 were purchased from ECCAC (Sigma-Aldrich) and maintained in Dulbecco's modified Eagle's medium (DMEM), supplemented with 10% (v/v) foetal bovine serum, 1% penicillin/streptomycin and 1% sodium pyruvate.

Supplementary Figures

Supplementary Figure 1: Analysis of MNoV_S99 associated NOD2 dependent inflammation measurements from mice showed in Figure 2 A) Colon length comparison from WT and Nod2 -/-3% DSS ± MNoV_S99 treated mice, statistical differences were determined with a t-Test, * p <0,02. B)-Histological scores comparison between WT and Nod2 -/-± MNoV_S99 treated mice, statistical differences were determined with a t-Test, * p <0,05. C)-TNFα production of explants (ON) from WT and Nod2 -/-3% DSS ± MNoV_S99 treated mice was measured by ELISA.

Supplementary Figure 2: MNoV_S99 associated pro-inflammatory signalling pathways activation

Representative western blot showing STAT1, IκBα, AKT and ERK signalling pathways' modulation in cell lysates from Raw264.7 that were either infected with MNoV_S99 (Moi 1) for various times or mock treated for 60 min. β-actin was used as loading control. Tables: