Cristian Andriesei
email: candriesei@etc.tuiasi.ro

Liviu Goras

Farid Temcamani
email: temcamani@ensea.fr

Bruno Delacressonnière

L Goraş

B Delacressonniere

Improved Rf Cmos Active Inductor With High Self Resonant Frequency

Keywords: active filter, circuit modeling, CMOS, gyrators, negative capacitor I

Many architectures of transistor only simulated inductors (TOSI) have been proposed until now in literature. Exhibiting tuning possibilities, low chip area and offering integration facility, they constitute promising architectures to replace passive inductors in RF circuits. An improved CMOS active inductor topology is proposed in this paper. With a novel loss compensation scheme, frequency increase up to 1.1 GHz (30%-66%) of the inductor self resonant frequency is achieved in the frequency band 1.5-3.3 GHz with large quality factors and very low current consumption. Besides, a more accurate passive model is proposed for CMOS TOSI active inductors and tested for this particular topology. Consisting of four parallel branches, it is still second order even though it contains three conservative elements. The model is sufficient general and proves superior performances over the classical RLC model mainly for higher frequencies. The simulations were carried out in a 0.18 um CMOS process.

INTRODUCTION

Various CMOS TOSI architectures have been proposed during the last two decades, covering a wide range of applications: RF bandpass filters [START_REF] Thanachayanont | VHF CMOS integrated active inductor[END_REF][START_REF] Wu | A novel CMOS fully differential inductorless RF bandpass filter[END_REF][START_REF] Xiao | A 5.4-GHz high-Q tunable activeinductor bandpass filter in standard digital CMOS technology[END_REF], (RF/bandpass) amplifiers [START_REF] Zhuo | Programmable low noise amplifier with active-inductor load[END_REF][START_REF] Thanachayanont | A 1.5-V CMOS fully differential inductorless RF bandpass amplifier[END_REF], current or voltage oscillators [START_REF] Wu | CMOS VHF/RF CCO based on active inductors[END_REF][START_REF] Lu | A wide tuning-range CMOS VCO with a differential tunable active inductor[END_REF] and even frequency dividers [START_REF] Jang | An activeinductor injection locked frequency divider with variable division ratio[END_REF].

TOSI architectures basically consist of a capacitively loaded gyrator made of several transistors, typically two, loaded by the parasitic capacitor of one transistor. The main advantages of these capacitorless entirely active simulated inductors over on-chip passive inductors are low silicon area and frequency and quality factor tuning possibility while current consumption, noise and nonlinearity are the main drawbacks. Inductor losses can be reduced by using varactors [START_REF] Xiao | A 5.4-GHz high-Q tunable activeinductor bandpass filter in standard digital CMOS technology[END_REF], one negative resistance or even two [START_REF] Andriesei | On the Tuning Possibilities of an RF Bandpass Filter with Simulated Inductor[END_REF].

The only equivalent model reported so far in literature for TOSI is the lossy LC parallel resonant circuit. Even though such model contains approximations, only gate to source capacitors being taken into account by the authors, it is good enough in applications where the inductive behavior only is exploited (LNAs). However, for voltage controlled oscillators (VCO) or bandpass filters where the self resonant frequency is of utmost importance, this classical RLC model does not approximate the resonant frequency accurately, giving significant errors for higher frequencies in the GHz domain.

This article is organized as follows. First, a more accurate TOSI passive model is proposed and discussed for a particular CMOS TOSI architecture in Section II. Frequency enhancement technique with negative capacitances is applied to the same inductor and studied in Section III. Simulation results are reported in Section IV while conclusions are presented in the last Section.

II. PROPOSED RLC MODEL FOR TOSI

The simulated inductor [START_REF] Wu | A novel CMOS fully differential inductorless RF bandpass filter[END_REF] studied in our research is presented in Fig. 1a together with the RLC model (Fig. 1b), generally adopted in literature for TOSI architectures [START_REF] Xiao | A 5.4-GHz high-Q tunable activeinductor bandpass filter in standard digital CMOS technology[END_REF][START_REF] Zhuo | Programmable low noise amplifier with active-inductor load[END_REF][START_REF] Thanachayanont | A 1.5-V CMOS fully differential inductorless RF bandpass amplifier[END_REF][START_REF] Wu | CMOS VHF/RF CCO based on active inductors[END_REF][START_REF] Lu | A wide tuning-range CMOS VCO with a differential tunable active inductor[END_REF][START_REF] Jang | An activeinductor injection locked frequency divider with variable division ratio[END_REF]. The gyration function is implemented by transistors M 1 (inverting stage) and M 2 (non-inverting transconductor) while a current source I b is used for biasing purpose. In other words, only three transistors are necessary to emulate the inductor behavior, therefore making this active inductor suitable for low power and small chip area RF devices. Furthermore, the inductor input impedance Z in is computed (1-2) and synthesized as obtained in Fig. 1c), the bulk effect being neglected.

ω α + + + = s s b s b s Z in ) 2 ( 2 1 , 2 1 2 1 2 1 2 1 2 0 2 1 1 2 1 2 2 2 1 1 2 1 2 1 2 0            + + = + + + = = =
C C g g g g g g C C C g C g C g C g C b C C g b ω α
As it can be noticed, the input impedance is a function of transistor trans-conductances (gm1, gm2), parasitic capacitors (Cgs1, Cgs2) and output resistances (rds=1/gds).

In order to obtain a more accurate equivalent passive model, several steps must be followed, as presented hereinafter. First, it's important to notice that the TOSI equivalent passive model is obtained through a non-canonical decomposition of the input admittance (or impedance). In addition, the key in finding a more realistic RLC model is to synthesize the input admittance function without any approximation.

Taking into account (1) and synthesizing the input admittance, after rearranging the terms in (1) so that the parasitic parallel resistor R p and capacitor C p are obtained, a first order expression in the form (as+b)/(cs+d) is obtained. This factor naturally appears during the fraction decomposition and the model accuracy is strictly dependent on its synthesis. A common way, reported in several previous papers which focused on the model, is to approximate this term with b/(cs+d). However, as our simulations showed, this approximation affects the model accuracy, offering a much more simplified and not realistic RLC passive model, mainly for RF design. The methodology we propose in our work consists in a further decomposition of the first order expression as presented below (3): For further improvement of the model accuracy, all transistor parameters must be considered when computing the TOSI input impedance, including g mb and current source output resistance. This is the second step and currently the input admittance can be expressed as it follows [START_REF] Zhuo | Programmable low noise amplifier with active-inductor load[END_REF], where G eq is the sum of g m2 , g mb2 and g ds2 . A good matching between theory and practice while studying the RF behavior of MOSFET transistors only is achieved by using a suitable small signal MOSFET transistor model. In addition, the key element which sets the frequency response is represented by the transistor parasitic capacitors. Thus, it is well known that two different MOSFET models are currently developed [START_REF] Sansen | Analog Circuit Design[END_REF]: a) a capacitor model which considers reciprocal capacitors network for transistor; b) charge based model, making using of transcapacitances and considering capacitors as non-reciprocal elements.

These models offer different results but as stated in several RF books, the second model gives better results for GHz frequencies, up to 30% of the cut-off frequency. Excepting [START_REF] Xiao | A 5.4-GHz high-Q tunable activeinductor bandpass filter in standard digital CMOS technology[END_REF] where transcapacitances were taken into account, all references studying active inductors have used the capacitor model only even if the inductors were designed at higher frequencies. Since our inductor model is developed for GHz frequencies where significant errors are expected, the case b) is taken into consideration during our research and this represents also the third step in finding a more accurate TOSI passive model. Consequently, in the previous formula (4), total node capacitances -C(1), C(2), including Cgg, Cdd, Css, are considered instead of node to node capacitances (Cgs, Cgd, Cgb). This means that the gate to source capacitances are replaced with nodal capacitances in (2) and (3) which have adequate values. Moreover, since most TOSI configurations have no floating parasitic capacitors, as the inductor studied in this work, the use of the second model is advisable.

The final model proposed for TOSI architectures with input impedance of second order as described by (1-3) is presented in Fig. 2. Compared to the classical RLC model, our proposed one contains a supplementary RC series branch (the fourth parallel one) which apparently raises the order of the circuit to three. However, since the synthesized circuit is not canonic i.e., there is a pole/zero cancellation explained by the dependencies between the inductive and capacitive branches parameters, the model order remains two. Since this model can be applied to any active inductor having the same input impedance/admittance and all TOSIs implemented with two transistors and having no floating parasitic capacitors are characterized by (1-2), the model is sufficient general. This proposed model is validated by simulations, as shown in Section 4. The utility of this proposed model resides in the theoretical accurate estimation of the simulated inductor parasitic elements and self resonant frequency. Finally, this "improved" model proves that a transistor only simulated inductor is much more than a simple RLC resonator, as considered until now.

III. IMPROVED ACTIVE INDUCTOR

Like any CMOS TOSI, the active inductor studied above has a very small quality factor. This is the reason why a compensation scheme must be used in order to decrease the intrinsic losses at the frequency of interest. The most common compensation method for VCOs and active inductors consists in using negative resistances, implemented with two crosscoupled transistors. In the most applications, the negative resistance is dc coupled therefore negatively changing the inductor biasing. In addition to a larger quality factor, obtained by connecting a negative resistance to an active inductor, always the self resonant frequency decreases while the quality factor increases. However, as reported in [START_REF] Andriesei | Frequency Enhancement Method Applied to a CMOS RF Simulated Inductor BR Filter[END_REF], it is possible to increase the self resonant frequency while using a negative resistance but only in conjunction with a supplementary positive gate resistance.

In this paper, a novel compensation scheme is proposed to active inductors, particularly for the basic cell shown in Fig. 1, considered in differential topology (Fig. 3). Basically, it consists of a dc decoupled negative resistance, as previously used and described in [START_REF] Andriesei | Frequency Enhancement Method Applied to a CMOS RF Simulated Inductor BR Filter[END_REF] but improved in such way that it acts as a more general negative impedance converter. Thus, a small supplementary positive capacitor is connected between the sources of the cross coupled transistors, easy to implement practically by means of layout. Therefore, a negative capacitor must be seen by the active inductor at its inputs. The use of negative capacitors together with active inductors, as we propose in this communication, envisages the increase of inductor self resonant frequency with positive effects upon its frequency capability. No extra compensation scheme is required to increase the inductor quality factor since the negative capacitor already includes a negative resistance, as stated by [START_REF] Thanachayanont | A 1.5-V CMOS fully differential inductorless RF bandpass amplifier[END_REF], where Z neg (s) represents the negative resistance input impedance.

Z neg (s) = -2/g m -1/sC x (5)
Consequently, the active inductor will see at the input a negative resistance of value R neg = -2/g m in series with a negative capacitance whose value is given by the supplementary shunt capacitor (C X ). Therefore, a double compensation is effectively implemented with a single scheme, an increase of the both self resonant frequency and inductor quality factor being expected. The positive results are shown in the following Section.

IV. SIMULATION RESULTS

For testing our proposed passive model (Fig. 2), the active inductor shown in Fig. 1a was biased at four different frequencies in the GHz domain. All transistor parameters were extracted and used to compute the both equivalent RLC models (classical and the proposed one). Finally, the frequency response of these three circuits was compared. The simulations showed a good matching between the results offered by our proposed model and the behavior of the real inductor, implemented with transistor and modeled. These results are further presented in Fig. 4. Regarding the improved inductor architecture, it was biased at three resonant frequencies (1.5 GHz, 2.34 GHz and 3.3 GHz). Although a decrease of the self resonant frequency is expected due to the cross coupled transistors parasitic, the negative capacitor compensates these parasitics and consequently higher frequencies are obtained. Thus, our simulations showed a frequency increase of 1.1 GHz for the resonant frequency of 2.34 GHz, while about 1 GHz is obtained for the first and third cases. Large quality factors are obtained for each frequency and particularly with very small current consumption (< 350 µA). The current consumption is at least halved compared to the case of using negative resistances only.

An interesting aspect is that, besides frequency increase, this compensation method has relaxed constraints since one capacitor is enough for all three simulations (10 fF) while the current through the negative resistance is almost constant (~ 134 µA). In other words, the method is insensitive to the quality factor tuning at different frequencies. By its performances, this compensation scheme is superior to that proposed in [START_REF] Andriesei | Frequency Enhancement Method Applied to a CMOS RF Simulated Inductor BR Filter[END_REF]. In any case, higher current consumption is expected if high linearity, low noise and impedance matching are required for the final filter shown in Fig. 3.

The Simulation Results are illustrated in Figures 567. All simulations were carried out in a 0.18 um CMOS process. Simulations have showed good accuracy for this proposed method, almost perfect matching between the frequencies results of the proposed model and the active inductor being noticed. In addition, a novel compensation scheme was applied to the same active inductor consisting of a more general negative impedance converter. A supplementary frequency increase of 1.1 GHz was obtained in the frequency range 1.5 GHz … 3.3 GHz, with a maximum current consumption of 350 µA. High quality factors are obtained with this compensation scheme but careful design must be applied to avoid the circuit instability.

Figure 1 .

 1 Figure 1. CMOS simulated inductor and equivalent passive model

+

  Since all fraction terms (a, b, c, d) are nonzero for any active inductor and the model obtained this way is to be used at higher frequencies in GHz domain, this decomposition (3) is valid and reveals two series branches (RC and RL) connected in parallel.

Figure 2 .

 2 Figure 2. Proposed RLC model for CMOS active inductors

Figure 3 .

 3 Figure 3. Active inductor with negative capacitance

Figure 4 .

 4 Figure 4. Frequency behavior comparison for the CMOS active inductor, RLC classical model proposed RLC model

Figure 5 .Figure 6 .Figure 7 .

 567 Figure 5. Frequency increase of 1GHz at Fo=1.57 GHz with compensation

This paper was financially supported by the Romanian Higher Education Scientific Research National Council under Grant PN2 -ID_310.


Le Chevalier
email: vincent.le-chevalier@ecp.fr

Jaeger Marc
email: marcjaeg@gmail.com

Mei Xing

Cournède Paul-Henry
email: paul-henry.cournede@ecp.fr

Simulation and Visualisation of Functional Landscapes: Effects of the Water Resource Competition between Plants

Keywords: landscape visualisation, plant growth models, natural phenomena simulation, water cycle models

Vegetation ecosystem simulation and visualisation are challenging topics involving multidisciplinary aspects. In this paper, we present a new generic frame for the simulation of natural phenomena through manageable and interacting models. It focuses on the functional growth of large vegetal ecosystems, showing coherence for scales ranging from the individual plant to communities and with a particular attention to the effects of water resource competition between plants.

The proposed approach is based on a model of plant growth in interaction with the environmental conditions. These are deduced from the climatic data (light, temperature, rainfall) and a model of soil hydrological budget. A set of layers is used to store the water resources and to build the interfaces between the environmental data and landscape components: temperature, rain, light, altitude, lakes, plant positions, biomass, cycles, etc. At the plant level, the simulation is performed for each individual by a structural-functional growth model, interacting with the plant's environment. Temperature is spatialised, changing according to altitude, and thus locally controls plant growth speed. The competition for water is based on a soil hydrological model taking into account rainfalls, water runoff, absorption, diffusion, percolation in soil. So far, the incoming light radiation is not studied in detail and is supposed constant.

However, competition for light between plants is directly taken into account in the plant growth model.

In our implementation, we propose a simple architecture for such a simulator and a simulation scheme to synchronise the water resource updating (on a temporal basis) and the plant growth cycles (determined by the sum of daily temperatures). The visualisation techniques are based on sets of layers, allowing both morphological and functional landscape views and providing interesting tools for ecosystem management.

The implementation of the proposed frame leads to encouraging results that are presented and illustrate

Introduction

Realistic simulation of ecosystems is a challenging topic, involving bio-physical, ecological, social, economical and human aspects. We focus here on vegetation growth in interaction with resources, mainly temperature and water, over a delimited domain. The target applications are agronomy, forestry and landscape planning, at mid range space and time scales, i.e. crops, stands or small landscapes, evolving over a period of several months to several years. Nowadays, process based vegetation models (PBM) and functional structural models (FSPM) [START_REF] De Reffye | Relevant choices in botany and mathematics for building efficient dynamic plant growth models: Greenlab case[END_REF] become mature. Some of them are currently able to model plant development and production, under constraints from a variable resource environment, especially in terms of light, water and temperature [START_REF] Cournède | Structural factorization of plants to compute their functional and architectural growth[END_REF]. Nevertheless, in such approaches, the interaction between plant models and resources is limited.

Another point is that such models cannot usually be easily extended at crop, plantation and higher spatial levels (landscape). Significant results were gained in the case of homogeneous crops, but competition for resources and heterogeneity in terms of space and time make such extensions not obvious at all. The proposed work is to define some simple bases to allow plant and environment interactions, both ways. In this paper, we mainly focus on conservative water resources, to be shared among the components of a crop field, plantation or landscape. Such a study is motivated by the fact that the availability and supply of water resources are fundamental issues for human activities. All the elements of the water cycle have to be modeled, not only plants but also soil and climate.

The next section briefly recalls some interesting pioneering works on this topic, while section three introduces the principles of the proposed landscape simulator. Section four details some specific models and their interaction with resources. Section five presents the techniques used to visualise and analyse simulation results. Some study cases are then illustrated before the conclusion.

Previous works

Landscape modelling is a difficult subject, mainly because of the complex interactions at various time and space scales. Historically, the first goal was to reach a satisfying visual simulation of landscapes, and thus contributions came from computer graphics specialists. A pioneering study in this domain is the work by J.

Hammes [START_REF] Hammes | Modeling of ecosystems as a data source for real-time terrain rendering[END_REF] introducing for the first time the word ecosystem in computer graphics, proposing a multilevel texture-based approach to synthesise such systems.

Relief definition was extensively studied [START_REF] Fournier | Computer rendering of stochastic models[END_REF], including simulation of long term effects such as erosion [START_REF] Musgrave | The synthesis and rendering of eroded fractal terrains[END_REF][START_REF] Chiba | An erosion model based on velocity fields for the visual simulation of mountain scenery[END_REF][START_REF] Neidhold | Interactive physically based fluid and erosion simulation[END_REF]. This research area is still very active nowadays, benefiting from modern GPU capabilities [START_REF] Schneider | Real-time editing, synthesis, and rendering of infinite landscapes on GPUs[END_REF]. Such capabilities offer a high potential for the local refinement of relief, including vegetation-like features [START_REF] Dachsbacher | Interactive terrain rendering : towards realism with procedural models and graphics hardware[END_REF]. Various vegetation generation and evolving environmental approaches were also tested, voxel based [START_REF] Greene | Voxel space automata: modeling with stochastic growth processes in voxel space[END_REF],

with L-Systems [START_REF] Deussen | Realistic modeling and rendering of plant ecosystems[END_REF] or even recently with spatial competition [START_REF] Alweis | Modeling and visualization of symmetric and asymmetric plant competition[END_REF]. The shortcomings of these approaches are mainly the lack of retro-action between elements of the landscape, as well as unrealistic dynamics. Physical accuracy is not an objective of these studies since only the visual aspect is desired.

Landscape sub-models, on the other hand, are getting more and more complex and accurate.

Plant models, soil models, hydrological models [START_REF] Liu | Predicting storm runoff from different land use classes using a gis-based distributed model[END_REF], are all constantly improving. However, they are rarely integrated together to allow a coherent simulation of the whole landscape.

Architecture of functional landscapes

The idea behind functional landscapes is to simulate the multiple interactions taking place within ecosystems at the landscape level, with tools to visually investigate these interactions and the resulting quantitative variability. The notion was first introduced in [START_REF] Jaeger | Tree and plant volume imaging -an introductive study towards voxelized functional landscapes[END_REF] and has been the subject of further reflexions described in this work. However, such definition is to be considered in a very restrictive way. The term 'functional' did not (in [START_REF] Jaeger | Tree and plant volume imaging -an introductive study towards voxelized functional landscapes[END_REF]) include any bio-physical, social, nor economical evolution except vegetation development and its interaction with the water cycle simulation. The water resource was our primary concern, because on one hand water plays a key role for plant growth and on the other hand its cycle is a well-known phenomenon, described for example in [START_REF] Musy | Cours d'hydrologie générale[END_REF][START_REF] Beauchamp | L'eau et le sol[END_REF] (see figure 1). Moreover, it offers nice challenges in modeling, since it covers a wide range of motions with various time scales.

It is, as such, a good test case to derive the proper architecture for functional landscapes.

However, the proposed architecture could be used for other resources as well: light, nutrients, carbon, etc.

Layers

In our study, modularity is a key factor. Our objective is not only to make several models of different parts of the landscape interact, but also to use different models for the same part without impacting the functioning of the rest of the landscape. To achieve this goal, we isolate each model inside its own layer (see figure 2).

To reflect the spatial variability of the landscape, layers are subdivided into cells that contain the information needed by the models. In our implementation, most layers are decomposed into a regular grid of cells. However, the water layer for example uses a cell graph that is not regular.

Layers exchange information and resources.

Since their spatial discretisation is not necessarily the same, interpolation methods are required in order for them to communicate.

Model specifications

Models should be adapted in order to run within our framework. Specifically, they Of course, the full model is a bit too complex for our needs. We choose to implement a simple model to experiment with the impact of the environment at the landscape scale. Specifically, most of the structural part is omitted.

Plant model implementation

We adapt the GreenLab production equation given in [START_REF] Guo | Parameter optimization and field validation of the functional-structural model greenlab for maize[END_REF]:

dQ = E dt α § p 1 -exp -β S S p ( 1
)
where dt is the time step (daily in our case),

dQ
c = t 0 max(0, T -T b ) T g dτ ( 2
)
where t is the current time, T b is the base temperature below which development pauses, T g is the number of degree-day necessary to complete a growth cycle. Synchronising the growth cycle with calendar time can be difficult, with possible side-effects in days when a cycle ends.

We adopted an approach that is valid when the cycle duration is at least several time steps.

At the end of each cycle, the biomass produced is allocated to the organs of the plant according to their demands. The demand of the organs follows a bell-shaped curve during their functioning time.

The parameter E is the primary way of interaction between plants and their environment. We choose to make it dependent on water conditions and temperature:

E = E 0 E T E w (3)
E 0 depends on the photosynthetically active incident radiation (PAR) and is chosen constant in this preliminary study, E T depends on temperature, and E w is a function of the soil water content.

The temperature effect on photosynthesis (also known as biological efficiency) is represented by a simple bell-curve between the minimum and maximum temperature for photosynthesis, with a maximum of 1 at the optimal temperature.

The water effect is simulated thanks to a root model. It allows communication between the soil model and the plant model and covers two processes. First, roots sense the amount of water in soil, allowing the plant to react to water depletion. Second, they take some water from the soil. This latter aspect has been the subject of several studies such as [START_REF] Varado | Development and assessment of an efficient vadose zone module solving the 1D Richards' equation and including root extraction by plants[END_REF][START_REF] Vrugt | Calibration of a twodimensional root water uptake model[END_REF], but seldom implemented with a FSPM.

The simplest model for the first process is to introduce an environment factor linearly related to the soil water content W (see [START_REF] Wu | A water supply optimization problem for plant growth based on greenlab model[END_REF] for applications of this model). However, this is not satisfactory considering the soil model outlined in section 4.2. Indeed, the same water content can lead to great differences for the plant according to the type of soil. That is because different soils have varying water retention capacity; it is harder for the plant to extract water from a soil with a very fine texture. That could explain some observed non-linearities as well.

For this reason, we propose a model for the environment factor that is based on the suction of the soil ψ:

E w = exp(νψ) -exp(νψ min ) exp(νψ max ) -exp(νψ min ) (4)
where ψ min is the soil suction at wilting point, ordinarily taken at ψ min = -150m regardless of the plant species, and ψ max is the soil suction at field capacity, ψ max = -3m with the same hypothesis of independence from plant species. The parameter ν represents how the plant adapts to the soil water content. For a specific soil, there is a value of ν giving the linear relationship.

The water uptake itself is restricted to the first layer of soil. Uptake of plants is defined on a cell of soil, containing plants at a density δ, as described in [START_REF] Wu | A water supply optimization problem for plant growth based on greenlab model[END_REF]:

dW = δ dQ wue ( 5
)
where wue is the water use efficiency of the plant.

It is to be noted that, even with the varying E w , the plant could attempt to take more water than what is available. The problem is purely numerical, and is handled by first checking that the amount of water available stays positive, and changing the production dQ accordingly if it is not the case.

Soil water movement

This section describes the model chosen to represent water movement inside the soil, under the surface of the terrain.

Richards equation

The fundamental equation describing the evolution of soil water content, in unsaturated conditions, is the Richards equation ( [START_REF] Witelski | Motion of wetting fronts moving into partially pre-wet soil[END_REF]),

given below in its one-dimensional form:

∂θ ∂t = ∂ ∂z K(ψ)( ∂ψ ∂z -1) (6)
where θ(dimensionless) is the volume water content of the soil, ψ(m) is the suction of the soil (negative in unsaturated soil), z(m) is depth, and K(ψ)(m.s -1 ) is the soil hydraulic conductivity.

The behaviour of this equation depends on the two functions K(ψ) (conductivity curve) and θ(ψ) (water retention curve). Several empirical models have been proposed to fit the measured behaviour of actual soils (see [START_REF] Kosugi | Water Retention and Storage -Parametric Models[END_REF] for an overview). The best models give complex nonlinear equations, and thus some special care must be taken to ensure an accurate numerical resolution.

We choose the simplest version of the model, because the object of this work is to study rather the interaction of models than their inner complexities.

Linear form

Certain forms of the conductivity and water retention curves make the Richards equation linear. They allow fast and accurate resolution, while still appropriately representing the phenomenon (as used in [START_REF] Witelski | Motion of wetting fronts moving into partially pre-wet soil[END_REF]).

Let us first define:

θ e = θ -θ r θ s -θ r (7)
where θ s is the value of θ in a saturated soil, and θ r is a fitting parameter, that can be interpreted as the residual water content when the soil is subject to an extreme depression.

Inspired by [START_REF] Kosugi | Water Retention and Storage -Parametric Models[END_REF], we model the water retention curve as follows:

θ e (ψ) =    exp(λ(ψ -ψ d )) , ψ < ψ d 1 , ψ ψ d ( 8
)
where λ is a fitting parameter, and ψ d is the air-entry value.

We also make the hydraulic conductivity simply proportional to θ e :

K(θ) = K 0 (θ s -θ r )θ e (9)
and Richards equation ( 6) becomes:

∂θ e ∂t = K 0 λ ∂ 2 θ e ∂z 2 -K 0 ∂θ e ∂z (10)
which is a classical advection-diffusion equation.

This equation is then solved by finite differences in several layers of soil that store the water resources used for example by plants. It is also used to compute a maximum depth of saturation achievable during precipitation events, thus allowing to model water absorption. The water that is not absorbed flows on the soil surface as described in the next section.

Surface runoff

Water runoff is a key point in hydrological simulations, since it is a phenomenon that effects the repartition of water resources over the whole landscape. It is relatively well understood, and many examples of simulations exist. Our approach was based on the idea of contributing area as described in [START_REF] Beven | A physically based variable contributing area model of basin hydrology[END_REF]. However, we adapted the model to the of our problem, as shown in the rest of this section.

Figure 3: Water movements involved in runoff.

Specificities

Water runoff on the surface is clearly one of the quickest processes that we attempt to simulate in our landscapes. In a typical time step of one day, water can run over long distances.

Moreover, the computing load of a detailed resolution of fluid dynamics is prohibitive.

We want to know how the water is distributed in the soil after runoff. Thus we use a simplified model, mostly neglecting the depth of flow. Interestingly, such an approach is also chosen in research about erosion [START_REF] Howard | A detachment limited model of drainage basin evolution[END_REF]. Water flow on the surface is integrated over the whole time step, and the runoff is computed at each point of the landscape during this step.

Implementation basis

The underlying terrain is used to first build a graph of water cells. At the beginning of the simulation, this graph is an image of the regular grid that is used in most digital elevation models (DEM). However, some cells of this basic grid can be merged under certain conditions.

Thus, the graph used for the runoff simulation

R o = max(0, R i + r -A -E v ) ( 11
)
where R i is the input runoff, arriving in the cell, R o is the output runoff, r is the rain, A is absorption in the cell, and E v is evaporation, as illustrated on figure 3.

The quantity R o is then redistributed among the neighbours that are lower than the cell, ac-cording to the slope (i.e. the lowest cell gets most of the water). There are other approaches for the distribution, and the paper [START_REF] Tarboton | A new method for the determination of flow direction and upslope areas in grid digital elevation model[END_REF] gives a good overview of them. We have chosen the basic proportional model because even though it is simplistic, it realistic results.

Given this equation, it becomes clear that the problem is recursive. If we want to compute the output runoff of one cell, we have to know the input runoff. This input runoff is the sum of the output runoffs from higher neighbouring cells.

A recursive algorithm was thus implemented, it computes runoff by going upslope, against the flow.

Lakes

The main problem with the algorithm described occurs in cells that have no lower neighbours towards which the water could flow. We call those particular cells pits. Water arriving in pits through runoff cannot flow out and therefore accumulates. Pits are thus the starting points of lakes over the landscape. Designing algorithm to detect and fill lakes is a complex problem, and several strategies can be adopted.

Other authors have designed algorithms that actually fill the lakes (see [START_REF] Tucker | An integrated hillslope and chan-nel evolution model as an investigation and prediction tool[END_REF] for example). The problem is that their objective was not entirely similar to ours, entitling them to hypotheses that we cannot make. In particular, the assumption is made that the lake will fill until it overflows. This is mostly true when large time period are considered, but over a day, a lake could be only partially filled, without overflowing. It depends on the amount of water that contributes to this lake.

The algorithm we have designed proceeds iteratively in 3 phases. First, an outlet point is associated to each pit, according to topological properties of the underlying terrain, thanks to flags that are set on every cell to indicate their relationship with the pits (see figure 4).

Second, the recursive runoff algorithm is executed, along with a computation of the reserve available in each lake at every altitude up to its outlet's height. Third, lakes' overflows are detected. When a lake overflows, it invalidates the data computed during the runoff phase, and thus the algorithm iterates the 3 phases until no overflow happens. The result of a simple runoff simulation as shown in Figure 5. Here, the goal was not to work with accurate weather conditions models, but just to allow spatial and temporal heterogeneity that may be obtained from real measurements or specific models. In the simulation process, at each step, the simulator dumps selected channels on the disk, for later off-line visualisation and analysis.

Exploring simulation output

For each simulated day, a binary multichannel layer record is dumped on the disk. This

Layer visualisers

The i.e. using constant, linear, exponential, logarithmic, positive or negative function to affect a given scalar value to its color-map entry (thus its color).

The visualiser combines and blends the four channel (geometrical aspect, color table, and mapping functions). The final geometry is defined from a single or from two channels. In this case, the final geometry can result from a logical operation between geometrical meshes, for instance water flood heights over terrain heights (in order to see rivers and lakes, see Figure 5). It can also be processed as two separate geometrical components, a classical mesh built from scalar altitudes, supporting for each cell simple graphical primitives (lines, or spheres) the sizes (length, or radius) of which are defined from the second channel scalar values (see Figure 8).

Similar principles are applied to color table definition and mapping functions. Two specific look-up tables are chosen from the four selected channels, as well as one or two mapping functions. Finally, thanks to transparency capabilities of modern computer graphics, a blending mode is selected by the user to choose or combine mapping functions. As a result, a functional view of the scene is defined by a geometry built from one or two channels, a combination of color tables from one or two channels and a blending linear transparency function on the color mapping functions.

Channel indicators

Results

We present here some simulation results corresponding to simple cases. These examples cannot be considered as validations, but, beside their academic interest, they were used for consistency check (conservation of water resource, comparisons with simple crop models, etc.).

For the sake of clarity, we present here some results with very few changes in environmental conditions. Despite the fact that the simulator is able to manage variable local properties for vegetation and soil we consider here an even-aged and spatially homogeneous plant population (with a given constant density) all over the terrain. Soil parameters (except altitude) are also chosen constant, as well as rainfall level all over the terrain. We also consider a vertical, constant and spatially homogeneous incident light radiation. This last condition is quite restrictive on the yearly basis of our sim-ulations, but this article mostly focuses on the water resources and further work will integrate a detailed light model. However, variations of E 0 in Equation ( 3) over the year would be very easy to implement to illustrate season effect.

More generally, the proposed and developed simulation and visualisation tools are not restricted to all these specific constraints which are mainly related to the availability of consistent data.

We used simple synthetic terrains and digital elevation models downloaded from http://www. helensimage.com/tg.htm. A lake still forms in the crater and saturates the soil underneath. The main effect of the water limitation is to increase the spatial variability of growth.

Figure 1 :

 1 Figure 1: The water cycle. On the left, the descending part of the cycle: water falls on the soil and vegetation, and is finally absorbed into the soil. On the right, the ascending part: water is absorbed through roots and released in atmosphere during plant transpiration, or even directly evaporated from the soil.

Figure 2 :

 2 Figure 2: Links and relationships between the different elements of the simulator. The simulator is split in several layers. Each of them is responsible for running specific models. Arrows represent data fluxes that are established inside the architecture to allow it to function.

  is the biomass produced through photosynthesis over this time step, E is the environment factor (detailed below), S is the surface area of the photosynthetic leaves, α and β are empirical model parameters estimated from experimental data and S p is a characteristic surface area which integrates the competition for light with neighbours. In the simple case of homogeneous stands or crops, S p can be chosen as the inverse of the density, [5]. This equation is not the classical form of Green-Lab because we transposed it to the calendar (physical) time. The computation of plant production is usually synchronised with the development cycle. This one is not determined by the calendar time but rather by the thermal time ([12]): new organs appear rhythmically at integer values of:

  is based on the terrain grid but can differ. The goal of the algorithm is essentially to determine how water flows from one cell to another, following neighbourhood relationship materialised by the edges of the graph. Runoff quantity can be modeled thanks to a very simple balance equation at the level of each cell:

Figure 4 :

 4 Figure 4: Cell flags on a simple slope. The terrain is colored from green to yellow according to altitude. The blue color indicates how the cells are flagged. All the water flowing out of similarly colored cells end up in the same pit. Some cells do not have a flag, when the water flowing out arrives in more than one pit. A pit's outlet is the lowest cell on the edge of the area flagged with the pit's ID.

Figure 5 : 4 . 4

 544 Figure 5: Runoff simulation on a simple slope with holes. Blue color visualises the runoff value, overflooding can be seen on right.

5

 5 Visualisation techniques With a daily time step for the updating of layer interactions, visualisation and analysis tools are necessary. A classical one-year simulation output set is composed by 8 to 10 channels, at the resolution of the digital elevation model (thus 0.1 to 2 million cells), for 365 days; it thus leads to a total storage of 0.2 to 6 Gigabytes. Specific user oriented exports are the classical way to analyse and visualise simulation results.

  record is a table of the spatialised cells, each of them containing the scalar values of the user selected channels. First value stands for the terrain altitude, while others are open to user selection. Each multichannel record header specifies the number of channels, an optional scaling factor and a name. Typical records used in this paper contain 5 to 8 channels: terrain elevation, water depth, water contents in soil, runoff, temperature, plant biomass production, cumulated biomass, plant cycle. Simple ASCII files de-scribing day per day environmental conditions are also exported: precipitation level, average temperature.

  Not many analysers have been developed up to now, we should rather speak of indicators. Two timescales are considered, the current day, corresponding to the current view in the visualiser, and the year. At the day level, for each selected channel, the scalar property distribution on the full scene can be visualised on an histogram and a cumulated histogram. It also displays average, standard deviation, extrema, and median value. Statis-tical dispersion is computed, main distribution orientation deduced. Those values can be exported for further external analyses. At the year level, curves corresponding to the four channel properties are displayed, resulting from the daily average value over the scene. Rainfall histogram is drawn in background, for visual comparison. User can switch to a detailed layer view, focusing on extrema, average and standard deviation curves of a specific channel, as shown in Figure 8.

Figure 6 :

 6 Figure 6: Temporal effect of temperature. Various quantities, averaged over the landscape, are plotted as functions of time: precipitation in light blue, soil water content in dark blue, environment factor in red, produced biomass in dark green, cumulated biomass in light green. Top: no temperature variation -Bottom: temperature varies according to time.

2 .

 2 Changing temperature according to time: temperature corresponding to the raining condition are used (see 4.4). The resulting curves are given in Figure 6.

3 .

 3 Changing temperature according to situation: a classical model modulates locally the temperature according to altitude. On the example, the local variation is quite low (1 C over the altitude range of the terrain). The curves are very similar to the previous case. However, this small change of temperature impacts significantly the spatial repartition of the growth. This simple example shows the potential applications to the study of global warming.6.2 Effect of plant densityThe following simulation example illustrates the effect of vegetation density. Even though the incident light is supposed constant and ho-mogeneous in our simulation, the effects of competition for light can still be illustrated since they are included in the GreenLab production equation (1) through the parameter S p . However, to be able to account for the differences between south and north facing slopes, a more detailed radiation model has to be implemented.

Figure 7 :

 7 Figure 7: Effects of varying plant density. Plant density is respectively 1 plant/m 2 (top) and 15 plants/m 2 (bottom). The curves are averages over the landscape: soil water in blue, produced biomass in dark green, cumulated biomass in light green. Final biomass produced was only multiplied by 9 between the two cases. This can be explained by water shortage, which is readily apparent from the dark blue curves.

3 .

 3 Water Limitation and temperature variation: compared to the former case, temperature variation according to height adds temporal variability to the growth; plants in high areas start their growth later in the year. This is readily apparent in the curves of the total biomass produced. The first plants start growing at day 38, the last start at day 147.

Figure 8 :

 8 Figure 8: Full Simulation. From top to bottom, left to right: no limitation, water limitation, water limitation and temperature variation, semi-realistic visualisation. Below each set of curves (corresponding to those described in section 5.3) is a view showing spatial variability (high biomass in green, low biomass in red). Spatial histograms of cumulated biomass production are also represented, with a gray background.

* This work is supported by the National Natural Science Foundation of China under Grant No. 60473110 and by LIAMA-GREENLAB project 2


Sandrine Bourgoin-Voillard

Dominique Gallo

Ioanna Laïos

Anny Cleeren

Latifa El Bali

Yves Jacquot

Denis Nonclercq

Guy Laurent

Jean-Claude Tabet

Guy Leclercq

Pr Guy Leclercq

Capacity of type I and II ligands to confer to estrogen receptor alpha an appropriate conformation for the recruitment of coactivators containing a LxxLL motif -Relationship with the regulation of receptor level and ERE-dependent transcription in MCF-7 cells

Keywords: Estrogen Receptor alpha, Ligands, LxxLL motif, Downregulation, Transcriptional activity

Estrogen receptor α (ERα) belongs to the superfamily of nuclear receptors and as such acts as a ligand-modulated transcription factor. Ligands elicit in ERα conformational changes leading to the recruitment of coactivators required for the transactivation of target genes via cognate response elements. In many cells, activated ERα also undergoes downregulation by proteolysis mediated by the ubiquitin/proteasome system. Although these various molecular processes have been well characterized, little is known as to which extent they are interrelated. In the present study, we used a panel of type I (estradiol derivatives and "linear", non-steroidal ligands) and type II ("angular" ligands) estrogens, in order to identify possible relationships between ligand binding affinity, recruitment of LxxLL-containing coactivators, ERα downregulation in MCF-7 cells and related transactivation activity of ligand-bound ERα. For type I estrogens, there was a clear-cut relationship between ligand binding affinity, hydrophobicity around C-11 of estradiol and ability of ERα to associate with LxxLL motifs, both in cell-free condition and in vivo (MCF-7 cells). Moreover, LxxLL motif recruitment by ERα seemed to be a prerequisite for the downregulation of the receptor. By contrast, type II ligands, as well as estradiol derivatives bearing a bulky side chain at 11β, had much less tendency to promote ERα-LxxLL interaction or even behaved as antagonists in this respect, in agreement with the well known partial estrogenicity/antiestrogenicity of some of these compounds. Interestingly, some type II ligands which antagonized LxxLL motif recruitment were nonetheless able to enhance ERα-mediated gene transactivation.

Introduction

Estrogen receptor alpha (ERα) is a member of the nuclear receptor superfamily, the members of which function as ligand-regulated transcription factors [START_REF] Tsai | Molecular mechanisms of action of steroid/thyroid receptor superfamily members[END_REF]. Estrogens are essential for the development and the maintenance of the female reproductive system.

Besides, ERα is also known to play a pivotal role in the etiology of hormone-dependent forms of breast cancer [START_REF] Mcguire | Hormone receptors: their role in predicting prognosis and response to endocrine therapy[END_REF]. Hence, for the last 30 years or so there has been an intensive search for agents capable of modulating and/or inhibiting ERα-mediated cell proliferation. This has led to the identification of two main classes of ligands both containing two hydroxyl functions that contribute to their binding to the receptor (Tables I andII) [START_REF] Jordan | Molecular classification of estrogens[END_REF]. Type I ligands include estrogenic steroids and diphenolic structural analogues that similarly stimulate target tissues (trans stilbene derivatives such as diethylstilbestrol, isoflavones such as genistein, coumestanes such as coumestrol). These planar / linear agonists differentiate from angular weak agonists (type II) for which two subsets have been described: cis stilbene-like and geminal structures. Such angular ligands can in some cases antagonize the effect of strong type I estrogens [START_REF] Lubczyk | Investigations on estrogen receptor binding. The estrogenic, antiestrogenic, and cytotoxic properties of C2-alkyl-substituted 1,1-bis(4hydroxyphenyl)-2-phenylethenes[END_REF]. Insofar as their pharmacological profile varies among different tissues, they are currently referred to as SERMs (Selective Estrogen Receptor Modulators).

All investigated type I ligands have been reported to fit within a cleft of the hormone binding domain (HBD) in such a way that their hydroxyl groups can form hydrogen bonds with a few amino acids (for E 2 , 3-OH: Glu-353 and Arg-394; 17β-OH: His-524; Fig. 1) [5;6].

Although this property does not hold for type II ligands because of inappropriate orientation of their hydroxyl groups, interactions with Glu-353 and Arg-394 are conserved and concur to produce non covalent ligand-receptor complexes. Stability of these complexes results from additional electrostatic interactions between the second phenolic ring of these ligands and A c c e p t e d M a n u s c r i p t 4 Thr-347, a residue located in a subregion of the ligand binding domain (LBD) known to attract substituents in C-11 of estradiol (E 2 ) [6;7]. While hydrogen bonding between the hydroxyl of this phenolic ring and Thr-347 (Fig. 1) is of importance for ERα binding, hydrophobic interactions between the C-11 subregion of the steroid core and the LBD are largely dominant [START_REF] Anstead | The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site[END_REF]. Such differences in binding modes between type I and II ligands have been reported to confer to ERα distinct conformations that should influence its ability to recruit coactivators containing a consensus LxxLL binding motif (L = Leucine, x = any amino acid) [START_REF] Heery | A signature motif in transcriptional coactivators mediates binding to nuclear receptors[END_REF][START_REF] Iannone | Correlation between in vitro peptide binding profiles and cellular activities for estrogen receptor-modulating compounds[END_REF][START_REF] Ozers | Analysis of ligand-dependent recruitment of coactivator peptides to estrogen receptor using fluorescence polarization[END_REF][START_REF] Savkur | The coactivator LXXLL nuclear receptor recognition motif[END_REF][START_REF] Tamrazi | Coactivator proteins as determinants of estrogen receptor structure and function: spectroscopic evidence for a novel coactivator-stabilized receptor conformation[END_REF] such as those of the CBP / p300 or the SRC / p160 families known as acetyltransferase enzymes and / or adaptator proteins for the transcription machinery recruitment (see [START_REF] Klinge | Estrogen receptor interaction with co-activators and co-repressors[END_REF][START_REF] Mckenna | Nuclear receptor coregulators: cellular and molecular biology[END_REF][START_REF] Rosenfeld | Sensors and signals: a coactivator/corepressor/epigenetic code for integrating signal-dependent programs of transcriptional response[END_REF]).

Even though there is a general consensus that the ability to recruit such coactivators determines the capacity of ERα to induce gene transactivation, no structure / activity study substantiating this concept has been reported so far. Similarly, there are no systematic data concerning a potential relationship between coactivator recruitment (or the absence of recruitment) and the capacity of a ligand to modulate ERα turnover, a factor known to influence receptor-mediated gene transactivation [START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF][START_REF] Lonard | The 26S proteasome is required for estrogen receptor-alpha and coactivator turnover and for efficient estrogen receptoralpha transactivation[END_REF][START_REF] Reid | Cyclic, proteasome-mediated turnover of unliganded and liganded ERalpha on responsive promoters is an integral feature of estrogen signaling[END_REF]. This gap in our knowledge has led us to conduct such a comparative study based on a panel of type I and type II estrogens. Of note, a part of our investigations focuses on several E 2 derivatives substituted in C-3, C-11 and C-17 since, as discussed above, these three reactive positions of the steroid core are known to play a prominent role in ligand binding and to influence the receptor conformations leading to an estrogenic response. Our experimental approach for this program relied on an ELISA-based assay allowing the quantitative measurements of ligand-activated ERα associated with immobilized LxxLL motif-containing peptide. In previous studies, the specificity of the assay was established by competition experiments showing that a peptide

Material and methods

Compounds

Type I ligands were purchased from Sigma (St Louis, MO) or Steraloids (Newport, RI) while type II [4;21] were obtained from Prof. R. Gust (Pharmacological Institute, Free University Berlin, Germany) ; 11ß long chain derivatives of E 2 [START_REF] Agouridas | Fluorous tolerance of the estrogen receptor alpha as probed by 11-polyfluoroalkylestradiol derivatives[END_REF] were from Prof. J.-C.

Blazejewski (University of Versailles, France). For assays, stock ethanol solutions of these compounds were diluted in buffer (cell-free assays) or medium (cell culture) in order to have a final concentration of solvent below 0.1%. Routine biochemical reagents were from standard suppliers.

Cell-free assays with human ERα recombinant

Assays were conducted with a highly purified human ERα (hERα) recombinant obtained from Calbiochem (Euro-Biochem, Bierges, Belgium). Upon receipt, the protein was diluted (≈ 2.5 pmol/µL) in 50 mM Tris-HCl pH 7.5 containing 500 mM KCl, 2 mM DTT, 1 mM EDTA, 1 mM sodium orthovanadate and 10 % glycerol. Diluted aliquots were stored at -80 °C.

Relative Binding Affinity of compounds for hERα

Binding affinity of the compounds for hERα was evaluated by a solid phase competition assay based on the adsorbing property of the receptor onto hydroxylapatite (HAP) at low ionic strength [START_REF] Maaroufi | Importance of A/B and C domains of the estrogen receptor for its adsorption to hydroxylapatite[END_REF]. For that purpose, hERα diluted in a buffered bovine serum albumin (BSA) solution (1:500 in 10mM Tris-HCl pH 8 containing 1 mg/mL BSA) was adsorbed onto HAP (Bio-Rad). After removal of unbound receptor by centrifugation, the HAP suspension was incubated during 24 hours at 4 °C in the presence of 1 nM [

Ability of compounds to confer to hERα a conformation appropriate for recruitment of a

LxxLL-containing peptide.

The ability of hERα to associate with a capture LxxLL containing peptide was assessed with an ELISA-based assay (Elisa NR peptide ERα-chemiluminescent assay from Active Motif, Rixensart, Belgium). hERα (5 ng in the diluent buffer) was incubated on ice with increasing concentrations of the investigated compounds (from 10 pM to 1 µM) for 30 min. These samples were then added to LxxLL peptide-coated multiwell plates and further incubated for 1 hour at 20°C; controls were run in parallel without any compound. Binding of hERα to the wells was finally assessed by successive exposures to an anti-ERα primary antibody, an HRP-conjugated secondary antibody and a chemiluminescent development reagent according to manufacturer's instructions. Light intensity was measured using an Infinite® 200 microplate reader (Tecan, Mechelen, Belgium). Assays were performed in triplicate and the recruitment capacities (RCs) values expressed as percentages ± SD of the optimal value established with 1µM E 2 .

A c c e p t e d M a n u s c r i p t 8

Assays on MCF-7 cells

Cell culture MCF-7 and MVLN cells (MCF-7 cells stably transfected with a vitERE-tk-Luc reporter plasmid [START_REF] Pons | A new cellular model of response to estrogens: a bioluminescent test to characterize (anti) estrogen molecules[END_REF]) were propagated at 37 °C (5 % CO 2 , humid atmosphere) in Earle's based minimal essential medium (EMEM; Invitrogen, Carlsbad, California) supplemented with Phenol Red, 2 mM L-glutamine, 100 U/mL penicillin,100 mg/mL streptomycin (from Invitrogen) and 10 % heat-inactivated fetal bovine serum (FBS; HyClone, Logan, Utah).

Experiments were conducted in Phenol Red-free EMEM containing 10% charcoal-stripped FBS (estrogen-free medium, EFM).

Ability of compounds to confer to ERα a conformation appropriate for recruitment of an

LxxLL-containing peptide. MCF-7 cells were cultured for 2 days in EFM (6-wells dishes) before treatment with increasing concentrations of investigated compounds. After treatment (usually 15 min), cell monolayers were washed twice with ice-cold PBS (40 mM Na 2 HPO 4 , 10 mM KH 2 PO 4 , 120 mM NaCl, pH 7.2) and the cells were lysed in 50 µL ice-cold RIPA (50 mM Tris-HCl, pH 7.5, 150 mM NaCl containing 1% NP-40, 0.1% SDS, 0.5% sodium deoxycholate, 50 mM NaF, 0.1 mM orthovanadate, 0.6 mM PMSF, 0.3 mM TPCK). Lysates were diluted by adding 200 µL of diluent buffer (reagent # 101251 of the Active Motif's ELISA kit) and clarified by fine needle aspirations followed by centrifugation. Ability of ERα from these samples (150 µL) to associate with LxxLL motif was then assessed as described above.

Ability of compounds to bind to ERα ("whole cell assay")

A c c e p t e d M a n u s c r i p t 9

The binding of test compounds to ERα in MCF-7 cells was evaluated by a [ 3 H]E 2 competitive assay, using E 2 as a reference compound (RBA = 100 %) [START_REF] Stoessel | Competitive binding assay for estrogen receptor in monolayer culture: measure of receptor activation potency[END_REF]. For that purpose, cells were exposed for 45 min to 1 nM [ 3 H]E 2 alone (control) or in the presence of increasing amounts of unlabeled E 2 or investigated compounds in serum-free medium; additional cell cultures were exposed to 500-fold excess unlabeled E 2 for assessment of non-specific binding (NSB). Bound [ 3 H]E 2 was measured by scintillation counting after extraction and the data (total -NSB) expressed in percentages of the control value in order to establish RBA values (i.e. [IC 50 ] E2 / [IC 50 ] X X 100).

Ability of compounds to regulate ERα level

MCF-7 cells were plated in 10 cm-diameter Petri dishes (500,000 cells per dish) containing EFM. After 3 days of culture, medium was removed and cells were exposed to increasing amounts of the investigated compounds for 4 hours in a fresh medium ; control cells were maintained in culture without any compound (this short time period was selected to avoid an effect on ERα synthesis) [START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF]. Cells were then washed with TBS (50 mM Tris, pH 7.5, 150 mM NaCl) and lyzed for 30 min at 4°C in RIPA buffer (see above). ERα levels in these lysates were finally assessed by Western blotting following a procedure described previously [START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF]. Briefly, ERα was detected with a mouse monoclonal antibody (F-10, Santa Cruz Biotechnology, Santa Cruz, CA). Actin, used as a loading control, was detected with mouse monoclonal antibody MAB1501R from Chemicon International (Temecula, CA).

Exposure to primary antibodies was followed by incubation in presence of a horseradish peroxidase-conjugated goat anti-mouse secondary antibody (Pierce, Rockford, IL).

Peroxidase activity was revealed with Supersignal West Pico Chemiluminescent substrate from Pierce. ERα and actin bands were captured with a FLA-3000 digital camera (Fuji, Tokyo, Japan). Band intensities were quantified using AIDA software (Raytest, A c c e p t e d M a n u s c r i p t 10 Straubenhardt, Germany). ERα data were normalized according to actin (ERα / actin) and expressed in percent of control (untreated cells). Ability of bisphenol (# 23) to antagonize E 2induced downregulation of ERα was further assessed by immunofluorescence microscopy using an experimental protocol described previously [START_REF] Jacquot | Synthesis, structure, and estrogenic activity of 4-A c c e p t e d M a n u s c r i p t 28 amino-3-(2-methylbenzyl)coumarins on human breast carcinoma cells[END_REF].

Ability of compounds to enhance ERα-mediated transactivation

MVLN cells were cultured for 3 days in 6-well plates (plating density 100,000 cells/well) containing EFM. Medium was then removed and replaced by fresh medium containing increasing concentrations of the investigated compounds ; control cell cultures were run in parallel in the absence of any compound. After 24 hours of incubation, luciferase activity was assayed according to a previously described procedure [START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF]. Efficiency of compounds was expressed as percentages of luciferase activity induced by 10 nM E 2 .

A c c e p t e d M a n u s c r i p t 11 RESULTS

1.

Ability of estradiol and investigated derivatives to modulate the capacity of a hERα recombinant to recruit coactivators containing LxxLL motifs

Importance of ligand binding affinity for ERα

Interaction of each ERα-ligand complex with LxxLL-coated plates is likely to depend on various factors, including in particular the binding affinity of the ligand for the LBD. In other words, the extent of LxxLL motif recruitment by ERα should be related to the relative ligand binding capacity, even though the measurement of LxxLL recruitment and of ligand binding rely on very different procedures . To validate this view, we checked whether the recruitment curve established with increasing amount of E 2 correlated with the [ 3 H]E 2 / E 2 competition curve. As expected, both curves mirrored each other (Fig. 2) with RC 50 (3 nM) and IC 50 (2 nM) values in the same order of magnitude.

We subsequently assessed whether this property holds for E 2 derivatives whose functions / regions of the steroid core known to influence ERα binding had been modified.

The next sections refer to this study.

Importance of C-3 and C-17 hydroxyl groups of estradiol

In the binding pocket of ERα, Glu-353 and Arg-394, with the participation of water molecules, interact with the 3-phenolic function of E 2 while His-524, modeled as the ε tautomer, attracts its 17β-alcoholic group (Fig. 1) [5;27]. To assess the importance of these bridging properties on LxxLL coactivator recruitment, we tested three E 2 derivatives bearing modified OH group at these critical positions (i.e., -OCH 3 / -OSO 2 O -Na + for 3-OH, and -H for

Influence of substitutions in C-11 of estradiol

The LBD is highly hydrophobic and therefore not well suited to accommodate E 2 derivatives in which an hydrogen in C-11 is substituted by a polar residue. In contrast, hydrophobic substituents are readily accepted if their size is not too large (see Table I) and if they stabilize ERα-ligand complexes at high temperature [START_REF] Anstead | The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site[END_REF].

Figure 3c shows that the grafting of a hydroxyl in C-11 α or β of E 2 (compounds # 6α and 6β) modify its binding properties to the LxxLL-coated plate. While a decrease of the ability of hormone to promote high binding was observed at all concentrations for 11α-OH E 2 , this property was solely recorded at concentrations ≥ 10 nM for 11β-OH E 2 . The latter ligand was indeed more effective than E 2 at 1 nM indicating that regulation of LxxLL binding by the C-11 subregion of the steroid core is influenced by factors other than polarity, that antagonize its insertion within the LBD. In this regard, gas phase acidity study of these steroids has shown that 11β-OH E 2 presents a higher acidity than 11α-OH E 2 and E 2 , indicating that particular electronic effects occur in 11β-OH E 2 [START_REF] Bourgoin-Voillard | Stereochemical effects during [M-H]-dissociations of epimeric 11,17βestratriols and distant electronical effects of substituents at C (11) position on gas phase acidity[END_REF]. Polarity appeared nevertheless of prime importance for LxxLL-containing coregulators recruitment because produced a shift in ERα-LxxLL binding (as compared to the curve generated by E 2 ), similar to that produced by the grafting of -CH 2 Cl of -C CH groups, revealed that it is the whole region around C-11 which exerts an influence on LxxLL recruitment (Fig. 3f).

In contrast to these small substituents, large hydrophobic side chains grafted at 11β (# 12, 13 and 14) totally abrogated the basal LxxLL binding capacity of ERα (Fig. 3e). Hence, such E 2 derivatives, usually not well accommodated by the LBD [8;22], must be considered as LxxLL recruitment antagonists, a property confirmed by the fact that a -C 6 H 13 derivativetested as a prototype -blunted the enhancing effect of E 2 on ERα-LxxLL association.

Ability of non-steroidal estrogens to modulate the capacity of ERα to recruit coregulators containing LxxLL motifs

Type I ligands

A c c e p t e d M a n u s c r i p t 14 Diethylstilbestrol (#15) , whose binding affinity for ERα largely exceeds that of coumestrol (#16) and genistein (#17) enhanced the binding of the receptor to LxxLL-coated plates with a much higher efficiency as compared to the latter compounds (Table II and Fig. 4), suggesting a direct relationship between ligand affinity and capacity to induce receptor-LxxLL association. Hence, "linear" estrogens, the phenolic functions of which interact with Glu-353, Arg-394 and His-524 may be assimilated to steroidal estrogens in terms of coactivator recruitment.

Type II ligands

None of the "angular" ligands tested in this study enhanced the binding of ERα to LxxLL-coated plates, with the notable exception of a diphenolic imidazoline derivative (# 18)

for which a weak ability to induce LxxLL recruitment had already been reported [START_REF] Laios | Effects of (R,S)/(S,R)-4,5-bis(2-chloro-4-hydroxyphenyl)-2-imidazolines and (R,S)/(S,R)-2,3-bis(2-chloro-4-hydroxyphenyl)piperazines on estrogen receptor alpha level and transcriptional activity in MCF-7 cells[END_REF] (Table II, Fig. 5a-b). Moreover, gem-diphenolic ligands (# 21 -25) antagonized ERα binding to LxxLL motifs in the presence as well as in the absence of E 2 , as clearly demonstrated with bisphenol (# 23) and its analogs (Fig. 5c). This antagonism could not be ascribed to a lack of hydrogen bonding with His-524 in view of the fact that substitution of 17β-OH of E 2 by a hydrogen atom (# 4) failed to show such a property. Hence, hydrogen interaction with Thr-347 seems directly responsible of the antagonistic property of these ligands. Thus, type II angular ligands totally distinguish themselves from type I linear ligands in terms of coactivator recruitment. Thus, experiments aiming to assess whether observations under cell-free conditions can be extended to living cells were based on short-term (15 min) incubations of cells with the ligands. After this treatment duration, the LxxLL recruitment curve established with increasing amounts of E 2 displayed a profile quite similar to that observed for the recombinant receptor, although the RC 50 value was about 10 times higher (Fig. 6, Table III), probably reflecting the existence of cellular factors (i.e. endogenous receptor coregulators) limiting the binding of ERα to LxxLL-coated plates. The fact that this property was not recorded for the hydrophobic 11β-substituted derivatives (# 9, 8 and 10) of E 2 (Table III) suggests that the stability of ERα~ligand complexes modulates the impact of these factors. In agreement with this view, we found that the inhibitory effect of 11β-OH E 2 on receptor-LxxLL interactions was exacerbated in cell culture experiments, leading to an almost total suppression of the recruitment capacity of the receptor. As expected, substitutions of 3 or 17β-hydroxyls of E 2 (# 2, 3, 4 and 5) also abrogated its recruitment ability (weak effect at 10 µM).

Recruitment of coactivators containing an

ERα from MCF-7 cells treated with DES bound to the LxxLL-coated plates like the receptor from E 2 -treated cells. By contrast, other non steroidal type I ligands had almost no effect on receptor-LxxLL interactions, most probably because of their low affinity for ERα in cultured cells (Table III). As expected, compounds antagonizing hERα interaction with LxxLL motifs in cell-free conditions (# 12, 23 and 25) produced a similar effect in cell extracts.

On the other hand, kinetic studies of the ERα-LxxLL peptide interaction induced by E 2 and its 11β-CH 2 Cl derivative revealed identical profiles. Thus, any ERα~ligand complexes potentially capable of recruiting LxxLL-motif coactivators progressively disappeared. This an insoluble target, an alteration of its LxxLL binding motif or a proteasomal degradation of the receptor (these various possibilities being non exclusive) [30;31]. On the other hand, concerning the partial estrogenicity of bisphenol (# 23), we found that this compound failed to display any ability to recruit LxxLL motifs even after 4 hours of cell treatment. Hence, the agonistic activity of this ligand [START_REF] Legros | Effect of estrogenic and antiestrogenic triphenylethylene derivatives on progesterone and estrogen receptors levels of MCF-7 cells[END_REF] seems to be unrelated to a delayed recruitment of LxxLL motif-harboring coactivators.

Relationship between ligand-induced recruitment of coactivators containing

LxxLL motif and ERα level in MCF-7 cells

Early studies from our laboratory have shown that E 2 -induced depletion (downregulation) of ERα in MCF-7 cells is evident after 4 hours of hormone treatment [START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF].

As shown in Fig. 7a, after this duration of exposure, E 2 concentrations ranging from 0.1 nM to 1 µM induced a similar extent of ERα decrease (~30-40 %). Thus in the case of E 2 , there was no quantitative relationship between the ligand-induced downregulation of ERα and the degree of ERα association with LxxLL motifs. This apparent absence of relationship was further illustrated by the behavior of 11β-substituted E 2 derivatives (# 9, 8 and 10) as well as the C9=C11 analog (# 11) which at 10 nM produced ERα downregulation with an efficiency similar to that of E 2 , even though they were more efficient at recruiting the LxxLL motif (Fig. 7b and Table III) (note that at higher concentrations of these compounds did not produce more pronounced downregulation). However, the low sensitivity of Western blotting (see SD of the data) could preclude the detection of small ERα level changes which might occur during the 15-min time period used for the assessment of LxxLL binding, obscuring thereby a potential relationship between recruitment of LxxLL-containing coactivators and ERα Nevertheless, such a relationship may exist in view of the fact that, within the exception of 11α-OH E 2 , compounds provoking an extensive LxxLL recruitment downregulated ERα with a higher efficiency than compounds which were less active in this respect (Table III III and representative compounds in Fig. 9). Hence, as already reported [18;19], proteasomal degradation of ERα appeared as an important step in ERα-mediated transcription induced by compounds sharing some structural homology with E 2 . We believe, therefore, that physiological ERα-induced gene transactivation might be relevant to a process involving LxxLL motif-coactivator recruitment followed by the proteasomal degradation of the receptor.

Paradoxically, 11ß C 6 F 13 -E 2 (# 14), which failed to modify the binding of ERα to the LxxLL-coated plate, and compounds that antagonized this binding (# 12, 23, 25) increased luciferase expression. This indicates that recruitment of LxxLL-containing coactivators is not absolutely required for ERE-dependent transcription (Table III, Fig. 9). ERα downregulation seems of higher importance in this regard since enhancement of luciferase expression was recorded at ligand concentrations which decreased receptor level. The additional observation that 11αOH-E 2 , which down regulated ERα by a mechanism most probably independent of LxxLL-motif recruitment, increased luciferase expression supports this view.

At concentrations which failed to decrease ERα level, 11βC 6 H 13 -E 2 (# 12) lost its capacity to enhance transcription, while bisphenol (# 23) remained able to induce transcriptional activity (Fig. 9), in agreement with the ability of these compounds to either up as antagonists in this respect. This is in agreement with the usual classification of these nonsteroidal compounds as partial estrogens/antiestrogens, as opposed to pure antagonists which exclusively abrogate the recruitment of LxxLL-containing coactivators [START_REF] Agouridas | Effect of fluorination on the pharmacological profile of 11beta isomers of fulvestrant in breast carcinoma cells[END_REF].

The insertion of E 2 (and probably of most type I estrogens) into the LBD of ERα provokes a rapid shift of its helix H12 which seals the ligand binding cavity [START_REF] Brzozowski | Molecular basis of agonism and antagonism in the oestrogen receptor[END_REF]. In this new conformation, H12 covers the binding pocket and plays a major role in stabilizing ligandreceptor complexes. In contrast, type II estrogens behave differently inasmuch as they maintain the pocket in an "open conformation", this resulting in less stable complexes. The latter conformation being generated by ligands belonging to various chemical families, one may reasonably postulate that it is associated with a large panel of H12 helix orientations explaining the agonistic / antagonistic profile of these compounds in terms of LxxLLcoactivator recruitment.

Hydrogen bridges involved in E 2 binding (3-OH and 17β-OH), which contribute to the displacement of H12 [START_REF] Brzozowski | Molecular basis of agonism and antagonism in the oestrogen receptor[END_REF], are found here to be essential for optimal LxxLL recruitment. We

show in this study that small hydrophobic substituents in C-11β, as well as the introduction of shows an unexpected behavior that could be relevant to electronic effects [START_REF] Bourgoin-Voillard | Stereochemical effects during [M-H]-dissociations of epimeric 11,17βestratriols and distant electronical effects of substituents at C (11) position on gas phase acidity[END_REF]. In fact, the nature of the interactions between ligand substituents and the LBD most likely influence the affinity of ERα for coactivators since (-CH 3 ) versus (-CH 2 Cl and -C CH) gave distinct LxxLL binding profiles. Hence, steric hindrance and/or electronic delocalization may also play a role. In this context, it should be stressed that C-11β hydrophobic substituents often increase E 2 binding to ERα at 25° C [START_REF] Anstead | The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site[END_REF]. This property, most probably due to a higher plasticity of the LBD at this temperature, was associated with an increase (-CH 3 , -CH 2 Cl, -C≡CH) or a decrease (-C 6 H 13 ) of LxxLL recruitment capacity, stressing its relevance to the agonistic as well as antagonistic character of a ligand.

Stereochemistry change of βOH at C-17 of E 2 significantly decreases its binding affinity for ERα. Our data reveal that such a change would not markedly affect the ability of the hormone to recruit LxxLL-containing coregulators, in agreement with the known estrogenicity of 17α-E 2 . On the contrary, α/β configuration of a hydroxyl at C-11 strongly affects the recruitment of a LxxLL motif in a way closely related to the decrease of binding affinity.

According to our data, Glu-353/Arg-394 and His-524, which contribute to the binding of type I ligands through hydrogen bonds, appear to be similarly implicated in LxxLLcoactivator recruitment. This property does not hold for Thr-347, even though this residue A c c e p t e d M a n u s c r i p t 22 participates to the interaction of type II ligands with the LBD. Indeed, bisphenol displays an evident antagonistic activity on LxxLL motif recruitment. Thus, H-bonds between the aforementioned residues and the phenol / alcohol groups of these two classes of ligands govern the coactivator recruitment capacity of the receptor. It should be noted in this context that the spatial orientation of these hydroxyl groups is also of importance, as revealed by the distinct LxxLL recruitment capacity of steroidal estrogens and the type I estrogens (e.g., genistein and coumestrol).

Influence of substitutions in position 11β of E 2 on LxxLL-motif recruitment could not similarly be explained because of the lack of information concerning the interactions of such substituents with specific residues of the LBD. While their insertion in the subpocket containing Thr-347 may be advocated, a "7α subpocket" could not be excluded in view of the fact that E 2 may rotate around a 3OH-17βOH axis [START_REF] Tedesco | 7 alpha,11 beta-disubstituted estrogens: Probes for the shape of the ligand binding pocket in the estrogen receptor[END_REF]. This 7α subpocket, essentially hydrophobic, is known to accept alkyl substituents which generate ligands with RBA values overpassing that of E 2 [38]. Hence, this mode of insertion within the LBD appears particularly well suited for our -CH 3 , -CH 2 Cl and -C CH derivatives of E 2 , in contrast to the Thr-347 insertion mode which is more appropriate for ligand with aromatic substituents [38].

Hydroxyl groups being hardly accepted by this 7α subpocket, 11β-OH would be rejected away from the latter with a concomitent decrease of the capacity of ERα to recruit LxxLLmotifs.

The acquisition of an ERα conformation appropriate for LxxLL motif recruitment seems to be necessary for its proteasomal degradation in MCF-7 cells. This property does not hold for ERE-dependent gene transactivation, since bisphenol, which antagonizes LxxLL motif recruitment, enhances the transcription of reporter genes at concentrations which LxxLL motif recruitment by ERα suggests that its ability to enhance transactivation in estrogen-free conditions results from a mechanism involving a distinct class of coactivators.

Another possibility would be the induction of intermolecular interactions via the expression of LxxLL-like motifs, which would generate a competitive inhibition in our LxxLL binding assay. To our knowledge, this model, which explains the antagonistic property of SERMs (i.e.

the LLEML motif of H12 that fits between H3 and H5 [START_REF] Shiau | The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen[END_REF]), has never been proposed in the context of type II agonists probably because of the lack of any description of auto-activating mechanism of ERα.

In agreement with previous observations, CHX was found to systematically abrogate the proteasomal degradation of ERα induced by the ligands which enhanced ERE-dependent transcription in MCF-7 cells, even the 11β-C 6 F 13 derivative of E 2 which failed to promote LxxLL-motif recruitment. Hence, the absence of CHX effect on the downregulation of ERα induced by the pure antiestrogen RU 58668 [34;35] which contains a functionalized side chain in C-11ß like this C 6 F 13 derivative, would be related to the antiestrogenic activity of this compound. This interpretation similarly holds for the ERα downregulation induced by ICI 182,780, which is also insensitive to the action of CHX [START_REF] Marsaud | Various phosphorylation pathways, depending on agonist and antagonist binding to endogenous estrogen receptor alpha (ERalpha), differentially affect ERalpha extractability, proteasome-mediated stability, and transcriptional activity in human breast cancer cells[END_REF]. All these considerations indicate that newly synthesized protein(s) operate in concert with the activated receptor to induce its degradation, in a process independent of LxxLL-containing coactivator recruitment.

In MCF-7 cells, E 2 -induced LxxLL-coactivator recruitment was fast and transient.

Although the underlying mechanism has not been investigated as yet, this observation is consistent with the existence of repeated cyclic interactions between ERα and cognate partners during target gene transactivation, a concept developed by several authors A c c e p t e d M a n u s c r i p t 24 [18;19;30;40;41]. Whether the membrane / cytoplasmic receptor pool, which participates to early estrogenic responses [START_REF] Leclercq | Estrogen receptor alpha: impact of ligands on intracellular shuttling and turnover rate in breast cancer cells[END_REF][START_REF] Hammes | Extranuclear steroid receptors: nature and actions[END_REF][START_REF] Kampa | Membrane-initiated steroid action in breast and prostate cancer[END_REF], is implicated in this dynamic LxxLL-coactivator recruitment is unknown. Investigating such a possibility seems to us of primary importance.

Use of ligands that selectively interact with this pool [START_REF] Seo | Evaluation of potential implication of membrane estrogen binding sites on ERE-dependent transcriptional activity and intracellular estrogen receptor-alpha regulation in MCF-7 breast cancer cells[END_REF][START_REF] Taguchi | Binding of estrogen receptor with estrogen conjugated to bovine serum albumin (BSA)[END_REF][START_REF] Wessler | Identification of estrogen receptor ligands leading to activation of non-genomic signaling pathways while exhibiting only weak transcriptional activity[END_REF][START_REF] Harrington | Estrogen dendrimer conjugates that preferentially activate extranuclear, nongenomic versus genomic pathways of estrogen action[END_REF] might be an adequate approach to initiate such a study.

In conclusion, our study shows that ERα-mediated transcription induced by type I estrogens depends upon LxxLL-coactivator recruitment. In contrast, type II estrogens are largely less dependent of this recruitment, a fact which may explain their mixed estrogenic/antiestrogenic properties. Moreover, same hydrophilic and hydrophobic interactions appear to govern the stability of receptor-ligand complexes, the attraction of LxxLL motifs and associated biological responses. Relationship between the capacity of E 2 to compete with [ 3 H]E 2 for binding to purified hERα recombinant and the binding of the receptor to the LxxLL-coated plate. hERα recombinant was incubated with increasing amounts of E 2 and processed for each assay as described in the methods section.

A c c e p t e d M a n u s c r i p t 5 derived

 5 from the SRC-1 coactivator effectively suppresses receptor binding to LxxLL-coated plates through a complexation process[START_REF] Gallo | Molecular basis of agonistic activity of ER alpha 7p, a synthetic peptide corresponding to a sequence located at the N-terminal part of the estrogen receptor alpha ligand binding domain[END_REF].A c c e p t e d M a n u s c r i p t 6

A c c e p t e d M a n u s c r i p t 7 Healthcare

 7 Biosciences, Eindhoven, NL) with or without increasing amounts of the investigated compounds or unlabeled E 2 taken as a reference. Bound [ 3 H]E 2 was then extracted with ethanol and measured by liquid scintillation counting. Relative concentrations of investigated compounds and E 2 required to inhibit the binding of [ 3 H]E 2 by 50% yielded the Relative Binding Affinity values (RBA = [IC 50 ] E2 /[IC 50 ] X X 100). Assays were performed twice, each time in duplicate. In each competition curve, variations between bound [ 3 H]E 2 values were extremely low (SD = 3 %), suggesting a very high reproducibility of the data.

A c c e p t e d M a n u s c r i p t 12 17ß-

 12 OH; compounds # 2, 3 and 4, respectively) (TableI). As shown in Fig.3aand 3b, these substitutions largely decreased the capacity of the hormone to promote ERα binding to LxxLL-coated plates, reflecting the acidic role of phenol in the complex. Interestingly, stereochemistry change of the βOH at C-17 into the α configuration (# 5) decreased by 40% ERα association with LxxLL motifs while it decreased by 70 % ligand binding to the receptor, stressing the importance of the hydrophobic steroidal core of the hormone in coactivator recruitment. Studies concerning substitutions in C-11 of E 2 , reported below, support this view.

  A c c e p t e d M a n u s c r i p t 13 acetylation of the 11β-OH function (# 7) resulted in a ligand able to induce "supraphysiological" receptor-LxxLL motif association.According to this concept, hydrophobic substitutions in the C-11β stereochemistry should confer to ERα a conformation favoring extensive LxxLL recruitment. In agreement with this view, we found that -CH 3 (# 8), -CH 2 Cl (# 9) and -C CH (# 10) substituents enhanced ligand-induced association of ERα with LxxLL motifs (Fig.3d). Interestingly, distinct LxxLL binding profiles were recorded for these three ligands showing that the chemical nature of the substituent influence the ability of the receptor to interact with LxxLL motifs. The observation that the introduction of a double bond between C-9 and C-11( # 11)

LxxLL motif by ERα in MCF- 7 cells

 7 Exposure of MCF-7 cells to E 2 produced in ERα a rapid conformational change favoring the recruitment of LxxLL-containing coactivators, as shown by measuring in cell extracts the extent of ERα binding to LxxLL-coated plates (Fig.6). Enhanced ERα binding, A c c e p t e d M a n u s c r i p t 15 which culminated within the first minutes of incubation, became undetectable after 4 hours.

A c c e p t e d M a n u s c r i p t 16 property

 16 may reflect a competition with cellular coactivators, a strong ERα association with

A c c e p t e d M a n u s c r i p t 17 downregulation

 17 . Indeed, because of the limited sensitivity of Western blotting, downregulation of ERα can only be evidenced after several hours of drug exposure[START_REF] Laios | Role of the proteasome in the regulation of estrogen receptor alpha turnover and function in MCF-7 breast carcinoma cells[END_REF].

A c c e p t e d M a n u s c r i p t 18 5 .

 5 Relationship between ligand-induced recruitment of coactivators containingLxxLL motif and ERE-dependent transcription in MCF-7 cells stably transfected with a vitERE-tk-Luc plasmid (MVLN cells)Steroidal and nonsteroidal ligands able to enhance the binding of ERα to the LxxLLcoated plate increased transcription of the luciferase gene (reporter gene) with an efficiency closely related to their ability to down regulate the receptor (see Table

A c c e p t e d M a n u s c r i p t 19 regulateA c c e p t e d M a n u s c r i p t 20 DISCUSSION

 1920 or stabilize the receptor. The fact that bisphenol was reported to antagonize E 2induced ERE-dependent transcription[START_REF] Lubczyk | Investigations on estrogen receptor binding. The estrogenic, antiestrogenic, and cytotoxic properties of C2-alkyl-substituted 1,1-bis(4hydroxyphenyl)-2-phenylethenes[END_REF] clearly indicates that this compound interferes with the normal action of the hormone, most probably by abrogating the capacity of the hormone to confer a conformation appropriate for the recruitment of LxxLL-motif containing coregulators.The present study evaluates the ability of various ERα ligands to induce the recruitment of LxxLL-containing coactivators by the receptor and examines a possible relationship between this recruitment, ERα level and ERα-mediated gene transactivation. Our data clearly show that both steroidal and planar / linear type I estrogens enhance LxxLL motif recruitment with an efficiency related to their binding affinity. By contrast, angular type II estrogens have much less tendency to promote receptor binding to LxxLL motifs or even act

A c c e p t e d M a n u s c r i p t 21 a

 21 double bound between C-9 and C-11, enhance the recruitment of LxxLL motifs. This observation suggests that hydrophobic interactions between the LBD and substituents around the C-11 position of the ligand strengthen the affinity of the receptor toward LxxLL coactivators. Polar residues, as well as long hydrophobic side chains not readily accommodated by the LBD largely limit this promoting effect. However, the 11β-OH E 2

A c c e p t e d M a n u s c r i p t 23 upregulate

 23 the receptor. The fact that this ligand suppresses the stimulatory effect of E 2 on

A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 30 FIGURE LEGENDS Figure 1 .

 30LEGENDS1 FIGURE LEGENDS

Figure 2 .

 2 Figure 2.Relationship between the capacity of E 2 to compete with [ 3 H]E 2 for binding to purified hERα recombinant and the binding of the receptor to the LxxLL-coated plate. hERα recombinant was incubated with increasing amounts of E 2 and processed for each assay as described in the methods section.

Figure 3 .

 3 Figure 3. Influence of substitution onto E 2 on its capacity to enhance the binding of hERα recombinant to the LxxLL-coated plate. hERα recombinant was incubated with increasing amounts of compounds and processed for LxxLL binding assay. Hydroxyl substitutions: Panel on the left (a) refers to 3-OH, panel on the right (b) to the 17β-OH. Substitutions at C-11: Panels refer to the linkage of (c) a polar group, (d) an hydrophobic group or (e) a long side chain. Panel (f) refers to the introduction of a double bond in C 9 -C 11 onto the steroid.

Figure 4 .

 4 Figure 4. Capacity of non steroidal type I estrogens to enhance the binding of hERα recombinant to the LxxLL-coated plate. hERα recombinant was incubated with increasing amounts of compounds and processed for LxxLL binding assay.

Figure 5 .

 5 Figure 5. Capacity of angular type II ligands to modulate the binding of hERα recombinant to the LxxLL-coated plate. hERα recombinant was incubated with increasing amounts of compounds and processed for LxxLL binding assay. Upper part : The panel on the left (a) refers to "cis diphenolic" compounds, the panel on the right (b) to "gem diphenolic" compounds. The figures evidence that former compounds are agonists while the latter are antagonists. Lower part (c): Strong antagonists (# 21 -23) at 100 nM abrogate the promoting effect of 1nM E 2 while weak antagonists (# 24, 25) display a moderate inhibition, agonists (# 18 -20) are ineffective.

Figure 6 .

 6 Figure 6. Capacity of E 2 to modulate the LxxLL motif recruitment in MCF-7 cells. (a) Kinetics of recruitment: Cells were incubated with 0.1 µM E 2 for various periods (from 15 min to 4 hours; insert: for 5 to 60 min). (b) Dose-effect study: Cells were treated for 15 min with increasing concentrations of E 2 (from 1 nM to 10 µM). Values are expressed as percentage of untreated cells (control). Figure refers to an experiment performed (a) twice and (b) five times (identical results).

Figure 7 .

 7 Figure 7. Effect of E 2 and C-11β derivatives with strong LxxLL recruitment potency on ERα level in MCF-7 cells. Western blotting data established with extracts from cells incubated for 4 hours with the compounds were normalized according to actin and expressed in percentage of ERα level in control, untreated cells. Panel (a) refers to increasing concentrations of E 2 and (b) to C-11β derivatives at 10 nM.

Figure 8 .

 8 Figure 8. Demonstration by immunofluorescence that bisphenol suppresses the down regulation of ERα induced by E 2 . MCF-7 cells were exposed to drugs for a period of 6 hours and processed for immunofluorescence microscopy as described in Methods. (a): no drug, (b): 1 nM E2, (c): 100 nM bisphenol, (d): E 2 and bisphenol in combination. ERα was revealed by using polyclonal antibody HC-20 raised against the receptor. Texas Red labeling.

A c c e p t e d M a n u s c r i p t 31 Figure 9 .

 319 Figure 9.Influence of (a) steroidal and (b) nonsteroidal ligands on ERE-dependent transcription. MVLN cells were incubated for 24 hours with increasing amounts of a set of representative ligands varying in their ability to modulate the binding of ERα in our LxxLL binding assay. ( _____ : agonists; _ . _ . _ : no effect; _ _ _ : antagonists). Luciferase activity was then assayed in cell extracts as described in methods.

Figure 1 Page

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Figure 2 A

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Figure 3 A c c e p t e d M a n u s c r i p

 3 Figure 3

Figure 4 Page 37 of 44 A c c e p t e d M a n u s c r i p

 444 Figure 4

Figure 5 A c c e p t e d M a n u s c r i p Figure 6 Page 39 of 44 A c c e p t e d M a n u s c r i p Figure 7 A c c e p t e d M a n u s c r i p

 56447 Figure 5

Figure 8 A c c e p t e d M a n u s c r i p Figure 9 A

 89 Figure 8

A c c e p t e d M a n u s c r i p t

  Table IIRelative efficiency of nonsteroidal ligands to associate with the E 2 binding site and induce the recruitment of coregulators containing a LxxLL motif.

  3

Table I

 I Relative efficiency of steroidal ligands to associate with the E 2 binding site and induce the recruitment of coregulators containing a LxxLL motif. Ratio of concentrations of E 2 and investigated compounds to produce 50 % of maximal effect. Value are expressed in percentages of E 2 efficiency to compete with [ 3 H]E 2 for ER binding (RBA) or confer a conformation to ER appropriate for binding to a plate coated with a peptide containing a LxxLL motif (RRC). **Value most probably overestimated due to the low optimal RC value (see Figure6).

	N°	Structure						E 2 binding (RBA)*	LxxLL recruitment (RRC)*
	17 estradiol derivatives
		9
		R 3	R 17	R 11
	1 (E 2 )	OH	OH	H				100	100
	2	O-CH 3	OH	H				3	10
	3	O-SO 2 -O -Na + OH	H				<<0.1	0
	4	OH	H	H				10	<<0.01
	5	OH	OH ()	H				30	60
	6	OH	OH	OH ()			0.3	1
	6	OH	OH	OH ()			10	4000**
	7	OH	OH	O-CO-CH 3	30	800
	8	OH	OH	CH 3				100	1300
	9	OH	OH	CH 2 -Cl			100	600
	10	OH	OH	C	CH		30	400
	11	OH	OH	C (9) = C (11)		10	200
	12	OH	OH	H 2 C				10	Antagonist
					F	F
	13	OH	OH	H 2 C			CF 3	10	Antagonist
				F		F F	F
				F	F F	F
	14	OH	OH	F 2 C			CF 3	3	No effect
				F	F F	F

*

  Ratio of concentrations of E 2 and investigated compounds to produce 50 % of maximal effect. Value are expressed in percentage of E 2 efficiency to compete with [ 3 H]E 2 for ER binding (RBA) or confer a conformation to ER appropriate for binding to a plate coated with a peptide containing a LxxLL motif (RRC).

		HO	-H	10	Antagonist
	22		-CH 3	10	Antagonist
	23	R	-CH 2 CH 3 (bisphenol) 10	Antagonist
	24	HO	-CH 2 CH 2 CH 3	3	Antagonist
		HO
	25			5	Antagonist
		HO

*

Table 2

 2

ACKNOWLEDGMENTS

This work was supported by grants from the Belgian Fund for Medical Scientific Research (n° 3.4501.09), the "Fonds Lambeau-Marteaux", the "Fonds Jean-Claude Heuson" for breast cancer research and from "Les amis de l'Institut Bordet". We also acknowledge the French Ministry of Research, CNRS and UPMC for financial supports. Dominique Gallo is recipient of an award from the "Fondation David et Alice Van Buuren". Guy Laurent is Senior Research Associate of the National Fund for Scientific Research (Belgium). We are grateful to Xavier Rousseau from the "Institut Meurice" for its active contribution in this work.