Hakim Si-Mohammed
email: hakim.simohammed@univ-lille.fr

Coralie Haumont

Alexandre Sanchez

Cyril Plapous

Foued Bouchnak
email: foued.bouchnak@orange.com

Jean-Philippe Javaudin

Anatole Lécuyer
email: anatole.lecuyer@inria.fr

Designing Functional Prototypes Combining BCI and AR for Home Automation

Keywords: Brain-Computer Interface (BCI), Augmented Reality (AR), Home Automation, Smart Home, Steady-State Visual Evoked Potentials (SSVEP)

In this technology report we present how to design functional prototypes of smart home systems, based on Augmented Reality (AR) and Brain-Computer Interfaces (BCI). A prototype was designed and integrated into a home automation platform, aiming to illustrate the potential of combining EEG-based interaction with Augmented Reality interfaces for operating home appliances. Our proposed solution enables users to interact with different types of appliances from "on-off"-based objects like lamps, to multiple command objects like televisions. This technology report presents the different steps of the design and implementation of the system, and proposes general guidelines regarding the future development of such solutions. These guidelines start with the description of the functional and technical specifications that should be met, before the introduction of a generic and modular software architecture that can be maintained and adapted for different types of BCI, AR displays and connected objects. Overall this technology report paves the way to the development of a new generation of smart home systems, exploiting brain activity and Augmented Reality for direct interaction with multiple home appliances.

Introduction

The developments in the fields of the Internet of Things and smart homes [START_REF] Gaikwad | A survey based on smart homes system using internet-of-things[END_REF] are pushing towards the design of new interaction mechanisms in order to seamlessly operate the multitude of available connected objects. These new interaction mechanisms largely rely on Natural User Interfaces (NUI) involving speech or gesture recognition [START_REF] Jain | The future of natural user interfaces[END_REF] to enable users to operate the appliances with reduced efforts. Following this trend, a promising way to interact with smart homes and connected objects would be based on Brain-Computer Interfaces (BCIs).

BCIs enable users to interact with computer systems by the sole mean of their brain activity [START_REF] Wolpaw | Brain-computer interfaces for communication and control[END_REF][START_REF] Si-Mohammed | Defining braincomputer interfaces: A human-computer interaction perspective[END_REF]. Historically, the first prototypes [START_REF] Vidal | Toward direct brain-computer communication[END_REF] of BCIs were mainly designed as means to assist people with disabilities, by providing them with systems exploiting brain activity instead of potentially damaged, traditional muscle outputs [START_REF] Wolpaw | Brain-computer interfaces: something new under the sun[END_REF]. Although this purpose still represents one of the main objectives of the BCI community, a new trend can be observed in recent literature, with a number of works targeting the design and development of BCIs that can be used as interaction media for the general public, with applications in Human-Computer Interaction (HCI), entertainment [START_REF] Lécuyer | Braincomputer interfaces, virtual reality, and videogames[END_REF][START_REF] Lotte | Combining bci with virtual reality: towards new applications and improved bci[END_REF], or robotics.

Unfortunately, it is currently difficult to argue that Brain-Computer Interfaces are ready for large scale exploitation. Current BCIs suffer from severe limitations in terms of reliability [START_REF] Lotte | Towards improved bci based on human learning principles[END_REF], speed and user friendliness [START_REF] Kübler | The user-centered design as novel perspective for evaluating the usability of bci-controlled applications[END_REF]. These limitations prevent BCIs from being competitive options with regards to available alternatives for healthy users. Consequently, careful design and realistic specifications are necessary in order to integrate them in any high-end interactive system. As a first step, and in order to pave the way for future systems using BCIs to interact with smart homes, this technology report introduces a prototype combining a BCI and an Augmented Reality (AR) system, in order to integrate them in a home automation system.

In general, BCI and AR are two technologies that can largely benefit each other [START_REF] Si-Mohammed | Braincomputer interfaces and augmented reality : A state of the art[END_REF][START_REF] Kohli | A review on virtual reality and augmented reality use-cases of brain computer interface based applications for smart cities[END_REF] either through providing user interfaces that are integrated in the environment, or by enabling hands-free interaction. The case of smart-homes is particularly conducive for the development of these technologies, as the increasing number and the heterogeneity of the connected objects and appliances offer a wide variety of use cases adapted to the diversity of BCI systems. Combining AR and BCI for home automation also holds the promise of a higher degree of adaptation to the users mental states, as well as the possibility, on the longer term, to seamlessly interact with their homes.

One of the earliest systems using a BCI to interact with house appliances was proposed by [START_REF] Karmali | Environmental control by a brain-computer interface[END_REF]. Their prototype enabled to change TV channels and to turn appliances ON or OFF using the users' brain activity. These appliances were controlled using binary hierarchical menus, in which users navigated by modulating levels of alpha rhythm [START_REF] Bazanova | Interpreting eeg alpha activity[END_REF].

In 2009, Edlinger et al. developed a P3001 [START_REF] Polich | P300 and probability: comparison of oddball and singlestimulus paradigms[END_REF] prototype to control home appliances [START_REF] Edlinger | Brain-computer interfaces for goal orientated control of a virtual smart home environment[END_REF]. Their prototype relied on a virtual representation of a house, displayed in a CAVE [START_REF] Cruz-Neira | Surround-screen projection-based virtual reality: the design and implementation of the cave[END_REF] system. Their interface relied on displaying grids of icons representing commands associated to the manipulated objects. The icons were randomly highlighted following the oddball paradigm for P300. Similarly, [START_REF] Aloise | P300-based brain-computer interface for environmental control: an asynchronous approach[END_REF] proposed a P300-speller based system to operate different appliances. Their interface consisted in grids of icons representing the achievable actions, displayed on a computer screen. More recently, Sahal et al. [START_REF] Sahal | Augmented reality integrated brain computer interface for smart home control[END_REF] used a custom P300 se-lection matrix displayed in AR to enable users to control their environment, specifically aiming at assisting patients with Amyotrophic Lateral Schlerosis.

Following a different approach, [START_REF] Lin | Brain computer interface-based smart living environmental auto-adjustment control system in upnp home networking[END_REF] used passive BCIs in order to automatically adjust the house environment (night lamps and air conditioner) based on the users cognitive states (drowsiness and alertness), associated with α (8-13 Hz) and θ (4-7 Hz) rhythms.

Using an AR Head-Mounted Display, [START_REF] Kansaku | My thoughts through a robot's eyes: An augmented reality-brain-machine interface[END_REF] proposed a system enabling users to operate a distant robot, navigating in a house environment, using brain activity. The robot was equipped with a camera displaying a video feed of its environment where appliances were tagged with fiducial markers. When an object entered the robot's field of view, a control panel that the users could interact with using P300 was displayed. Later on, [START_REF] Takano | Towards intelligent environments: An augmented reality brain machine interface operated with a see-through head-mount display[END_REF] developed a P300-based AR-BCI system to control multiple house appliances based on the same system described earlier, but through a monocular Optical See-Through AR display instead of the robot.

Using a BCI to control home appliances has also been explored through several prototypes and reviews [START_REF] Lee | A brain computer interface for smart home control[END_REF][START_REF] Kosmyna | Feasibility of bci control in a realistic smart home environment[END_REF][START_REF] Kosmyna | A conceptual space for eeg-based brain-computer interfaces[END_REF]. Overall, the scope of these previous work differed on the component they put emphasis on. Some of them focused on the EEG analysis while others highlighted the assistance benefits for people with disabilities [START_REF] Zhang | A bci-based environmental control system for patients with severe spinal cord injuries[END_REF]. For example, [START_REF] Putze | Augmented reality interface for smart home control using ssvep-bci and eye gaze[END_REF] proposed a prototype integrating a Steady-State Visual Evoked Potentials (SSVEP) based BCI, an eye-tracker and an Augmented Reality display to operate a smart home environment. They presented users with flickering AR icons depending on where the users gaze, and enabled the control of different appliances. The developed prototype mainly relied on two components: (1) the Head-Mounted Display (HMD) handling the graphical interfaces and the feedback, and (2) a server responsible for the classification of the EEG and gaze data into issued commands. Later, this study was followed by a usability assessment [START_REF] Vortmann | Attention-aware brain computer interface to avoid distractions in augmented reality[END_REF]. A similar prototype was designed by Park et al. [START_REF] Park | Development of an online home appliance control system using augmented reality and an ssvep-based brain-computer interface[END_REF], using an AR-based SSVEP interface for controlling home appliances.

Most of the so-far developed prototypes were realized for lab-controlled experimentation and never exceeded the state of feasibility studies. Up to now, no developed system was able to cross the borders of the laboratories. Hence the questions: What are the requirements and specifications that an AR-BCI home automation system should meet before we can foresee a broadly available system? How to develop such a system to make it operational and useful to the end-users?

To these days, the best performing type of BCIs in terms of ITR (Information Transfer Rate) and reliability [START_REF] Chen | A high-itr ssvep-based bci speller[END_REF] are the reactive BCIs (e.g. Steady State Visual Evoked Potentials [START_REF] Zhu | A survey of stimulation methods used in ssvep-based bcis[END_REF], P300 [START_REF] Picton | The p300 wave of the human event-related potential[END_REF]) which require the users to focus on specific stimulation in order to send commands. Thanks to Augmented Reality, it becomes possible to integrate these sensory (mostly visual) simulations directly into the user's environment, instead of their traditional display on a computer screen.

In this paper, our goal is to introduce a prototype of combining an SSVEPbased BCI with Augmented Reality in order to interact in a smart home environment. We also propose general guidelines towards the development of AR-BCI smart-home systems, in a way that they would be operational and possible to upgrade with future technological and scientific improvements. The guidelines presented hereafter, are introduced following a top-down approach. We start by discussing the general, functional and technical specifications: What are the technical requirement and the features the system should provide? Then we present a generic system architecture, describing its different components and their interaction and finally, we illustrate the implementation of the prototype by describing a specific operational system that was developed with Orange SA, in an industrial context.

The contributions of this technology report are the following:

-Generic guidelines for the development of smart homes based on BCIs and AR. -An operational smart home system integrated to a smart home platform.

The remainder of this paper is structured as follows: Section 2 discusses the system requirements and specifications of the prototype, introducing in the meantime higher level guidelines regarding the features of such a system. Section 3 describes the implemented prototype in terms of software and hardware architecture while Section 4 discusses the potential impact and presents use cases and future work, while Section 5 concludes the paper.

System Specifications and Guidelines

A smart home is "a dwelling incorporating a communication network that connects the key electrical appliances and services, and allows them to be remotely controlled, monitored or accessed " [START_REF] Jiang | Proceedings of 2004 International Conference on Machine Learning and Cybernetics[END_REF].

When it comes to the specifications of a system combining AR and BCI for home automation, six (6) requirements or features were identified as prominent. These specifications were related to both of the BCI and the AR component of the system and are namely: operability, asynchronicity, modularity, generalisability, contextuality and hardware-independence. These feature are hereafter described in terms of functional and technical requirement of the design system.

Operability

The first requirement of the system, is that it should offer the main features of a smart home and enable the operation of multiple devices. The underlying requirement is that the system has to enable the user to select which object s/he whishes to interact with. In addition to the selection mechanism, the system has to allow the user to send the desired command. It is possible to identify two categories of objects: (1) Binary state objects, that can only be switched ON or OFF (e.g. a lamp, a smart plug, etc.), and (2) Multiple commands/states objects, which can receive more that one command or have more than two states (e.g a television).

In order to meet this specification, a selection and operation strategy has to be provided for both types of objects. For example, binary state objects can be automatically switched between possible states upon selection while multiple command objects can toggle the display of a hierarchical menu leading to the possible commands.

The operation system should also take into account the possibility to include objects that may require continuous commands instead of discrete ones, even though this type of input is harder to achieve through BCIs.

Asynchronicity

Interactive systems depend on unpredictable inputs provided by the users [START_REF] Évain | Design and evaluation of fusion approach for combining brain and gaze inputs for target selection[END_REF]. This unpredictability originates both from the fact that users can issue different commands, and that these commands can be issued at any time. Hence, it is important for an AR-BCI based home-automation system to be asynchronous (also referred to as self-paced) in order to enable users to operate the system at their own pace. In other words, this specification means that the system should to be able to detect idle or rest states, where users do not intend to operate the system, thus, avoiding as much false positives as possible.

In terms of BCIs, the difficulty raised by this specification originates from the difficulty to detect rest states from the users' brain activity due to the constant background noise from unrelated activity. While several algorithms and signal processing methods have been proposed to detect these states, in several BCI paradigms, it remains generally more difficult to design asynchronous BCIs.

Generalisability

In most of the cases, BCIs are conceived as a mean to rehabilitate or assist people with disabilities [START_REF] Wolpaw | Brain-computer interfaces for communication and control[END_REF]. The first reason for this has to do with the nature of the interaction using a BCI which does not rely on muscle activity. Another reason, is the fact that BCIs are usually cumbersome and tedious to apprehend [START_REF] Jeunet | Why standard brain-computer interface (bci) training protocols should be changed: an experimental study[END_REF], further associated with lower performance than other User Interfaces (UIs). Given this statement, and the fact that some BCI paradigms are easier to apprehend and operate than others (P300 being easier than Motor Imagery for example), it is important that the system addresses a large group of users, and is not solely dedicated to people with disabilities. In terms of BCI design, this specification implies that the designed system should require as little training as possible, and ideally that it can be operated by naive users. In addition to the choice of BCI paradigm, this may also require the design of robust signal processing and classification methods that are resilient to inter-subject variability.

Contextuality

One of the main advantages of AR systems is their ability to provide and use information regarding the user context thanks to tracking mechanisms [START_REF] Azuma | Recent advances in augmented reality[END_REF]. This information allows to provide users with the relevant inputs and feedback, depending on their current location in the environment as well as their orientation (gaze direction). In the particular case of smart home systems where the number of interactable objects is typically large, information regarding the user's context allows to limit the presented inputs to the ones related to the objects which are most likely to be interacted with (at close distance, in the field of view, etc.). Moreover, as the performance of most visual-based BCIs are impacted by the amount of information provided at once, it is important that the system takes the user's context into account, both to maximize the system accuracy and reduce the potential confusion arising from too many inputs to process. In shorter terms, the system should adapt its AR interface and only display commands related to the objects in the field of view of the user, or the ones that are close enough to him/her.

Hardware independence

The combination of multiple technologies (AR, BCI and IoT) brings several layers of complexity to the designed system. On the hardware level, multiple choices can be made in terms of hardware components for the AR interface (Head-Mounted Displays, Tablet screens, Optical See-Through Displays, etc.), for the BCI (different EEG headsets are possible) and for the connected objects (several appliances and manufacturers). In order to deal with all these possibilities, it is important for the system to be hardware-independent, i.e abstract its features from hardware considerations. It should, for example, easily integrate new connected objects added to the environment, switch the display of the AR interface from one display to another or work with different EEG electrode configurations. In practice, this can be achieved by adding an abstraction layer to the system to handle the communication between the system's components.

Modularity

Given the heterogeneity of home appliances and the different natures of their commands, as well as the rapid developments in the fields of AR and BCIs, it is important for the system to be highly modular. It is for example difficult to conceive a single BCI paradigm that would fit all the use cases met in homeautomation scenarios. Moreover, given the current state of the art of BCIs, it is important that the system also enables the easy integration with other input modalities in addition or in replacement of BCIs (e.g. speech and gesture). For these reasons, it is important that the system architecture is composed of independent components that are able to be upgraded or replaced without impairing the overall function of the system.

Generic Architecture

In order to meet the previously described specifications, we propose a generic architecture based on four (4) main components: (1) The BCI, (2) the AR sys-tem, (3) the Home-Automation platform, and (4) the Middleware. The main advantage of this architecture is to maintain a high level of independence between the components as the middleware handles all the communication pathways in the system (see Figure 1). As such, changing the BCI paradigm or headset, the AR display or the home-automation platform should not impact the proper functioning of the system. Our proposed architecture is similar to but extends the one which was succesfully used in [START_REF] Park | Development of an online home appliance control system using augmented reality and an ssvep-based brain-computer interface[END_REF], and which uncouples the BCI from the rest of the system and using a middleware for communication.

The BCI

One of the specificities of this proposed architecture is not to consider the overall system as a BCI, but rather to include the BCI as one of its components. In fact, the role of the BCI is to measure and translate the brain activity of the users and to transmit the corresponding mental state to the interactive system, through the middleware. The internal structure of this component is based on the traditional BCI processing pipeline introduced by [START_REF] Mason | A general framework for brain-computer interface design[END_REF]. All the signal processing and classification aspects should be handled by this component, and the result of this analysis is transmitted to the middleware.

In an operational mode, this component should be able to detect changes in the user's mental state, and send the corresponding information to the middleware in real-time, meeting the asynchronicity requirement.

Encapsulating the BCI in a separate block enables to easily update the specific processing pipeline by using with new signal processing methods, changing the BCI-related hardware or the classification methods, without inducing changes in the rest of the components. As long as the detected mental state is transmitted to the middleware, the whole system should be able to operate.

However, some parts of the component, notably the choice of the BCI paradigm cannot be completely uncorrelated from the AR system for example.

In particular, reactive paradigms rely, by definition, on the nature and strategy of the stimuli provided by the system which is handled by the AR system display.

The AR system

The role of this component is twofold. First, it provides the users with information and feedback about the state of the system, as well as the possible commands to send depending on their context (location, objects in field of view, etc.). When developing a system combining BCI and AR, a particular care should be given to the display strategy so that the system is intuitive and easy to apprehend [START_REF] Si-Mohammed | Towards bci-based interfaces for augmented reality: Feasibility, design and evaluation[END_REF]. Second, in addition to the feedback and information, this component is also responsible of providing the visual stimulation (at least in the case of reactive visual BCI paradigms) for modulating the mental state.

AR System

Middleware BCI

Home-Automation

Fig. 1. Illustration of the components of a generic architecture of a home automation system using BCI and AR. The communication between the elements is done solely through the Middleware.

Decoupling this component from the rest of the system allows the designer to change/upgrade the hardware, i.e. the AR display and the AR interface without altering the functioning of the other components.

The Home-Automation platform

This component is the interface between the system and the connected appliances. Its role is to translate the commands obtained from the interpretation of the user mental state determined by the BCI and the context given by the AR system. The nature of this component depends on the specifications and features of the smart objects present in the house, i.e their network protocol, their API 1 and should eventually allow the interaction with heterogeneous objects. If the available smart objects do not share the same protocols, it should be the role of the Home-Automation platform to adapt the commands accordingly.

The Middleware

All the components described above have the specificity of being independent from each other. In order to be able to interact, the system architecture has to provide a communication component. Essentially, the role of the middleware is to determine, from the context of the users and their mental state, the corresponding command to carry to the home automation platform. The general interaction scenario is described as follows: At all time, the AR system provides the user context to the middleware (the location and the appliances detected in the field of view), based on which, the middleware responds with the available commands to provide and display to the user. [START_REF] Garousi | Control flow analysis of uml 2.0 sequence diagrams[END_REF] of the system, representing the interactions and the communications between the different system components.

BCI

Whenever the BCI detects a significant mental state (corresponding to a valid command), it sends the corresponding label to the middleware which, based on the user context, transmits the corresponding command to the home automation platform. As stated before, the home automation platform operated the connected object accordingly, and sends the updated system state to the middleware which in turn, requests the AR system to update the displayed information. This interaction sequence is summarized in Figure 2.

In this type of interaction sequence, it is the middleware that embeds the "intelligence" of the system as it is responsible for aggregating the information originating from the other components, in order to determine the appropriate command. It can also be assimilated to a finite-state automaton as described in Figure 3.

The initial state of the automaton corresponds to the Wait State where the middleware is expecting a system event to proceed. Each time a new object is added to the system, the middleware moves to the state S1, which corresponds to the update state, in which the middleware updates the available objects and commands and sends them to the AR system, and gets back to the Wait State.

Whenever, a mental state is provided by the BCI component, the middleware moves to S2 which corresponds to the BCI-handling state. In the case of SSVEP for example, the received message is under the form of a detected occipital frequency. If this frequency is not matched to any valid command, the middleware ignores the message and gets back to the Wait State. If the received frequency matches a command, the middleware moves to the S3 state, which corresponds to the decoding state, in which it determines the type of the received command.

If it corresponds to an object selection, the middleware switches to the S1 state, to update the available commands and the system state. If the command corresponds to an object operation, the middleware switches to the S4 state in which the middleware transmits the command to the home automation platform, and switches to the S1 state, where it updates the available commands.

Implemented Prototype

As a demonstration of the previously described concepts, specifications and architecture, we designed a prototype of a functional AR and BCI-based Home Automation system (see Figure 4). This prototype was built in the scope of an industrial partnership project, with Orange SA, one of the main Internet Service Provider in France. The aim was to build an operational AR-BCI based homeautomation system that would be integrated to their home-automation platform Home'in.

In this Section, we describe the conceptual and technical choices that were made in order to meet the requirements mentioned in Section 2.

In the scope of the project, the specific objectives of the project were as follows:

1. The system had to enable the control of at least 3 types of objects:

The first requirement was that the developed prototype would enable the online operation of at least 3 objects present in a given environment: a fan (connected to a smart plug), a connected light and a Television. This objective was inline with the Operability specification. 2. The system had to integrate with an existing home automation system: As a collaborative project, the objective was to integrate the global system with the home automation platform provided by Orange. In other words, the outputs of the BCI system had to be issued to the connected objects using the Home'In protocol. 3. Commands should require at most 2s to be issued:

The requirement in terms of performance, was that the system had to be able to issue commands in around 2s. This requirement was particularly prominent for the choice of the BCI paradigm, as well as the employed signal processing methods. 4. The system has to be validated online:

Finally, in order to showcase the functionality of the system, and to validate the results, the demonstration had to be performed online.

In the following parts, we describe the implementation of each component of the generic architecture, with regards to the previously introduced requirements, and how our proposed methodology was particularly suited for the design of this prototype.

The BCI

Considering the number of interactable objects and the possible number of commands offered by the system, and also for its low requirement in terms of user training and its relative reliability, we have opted for the Steady State Visual Evoked Potentials (SSVEP) paradigm. In practice, the possible commands that the user could issue at a given moment were represented by targets flickering at different frequencies. Thus, the goal of the BCI component was to determine online, the flickering stimulation frequency that the user was attending and transmit this information to the middleware.

The different possible flickering stimulations and their corresponding commands were stored in a configuration file, namely the frequency map and managed by the Middleware. This way, it was easy to upgrade the system by adding more frequencies to the frequency map, or by reutilizing frequencies for different commands. For example, a target flickering at 10Hz could designate a specific command when the user was in a particular room, while this same frequency could designate another command in a different room or context, simply by updating the command associated with the given frequency in the frequency map.

Electroencephalographic (EEG) signals were recorded using a SMARTING amplifier (mBrainTrain, Serbia) with a sampling frequency of 500 Hz and using 5 scalp electrodes: O1, O2, POz, Pz and CPz referenced to Cz and grounded to AFz (see Figure 5).

Before being able to use the system online, multi-class (Common Spatial Pattern) and LDA (Linear Discriminant Analysis) classifiers were calibrated to recognize 5 classes: 4 stimulation frequencies (10Hz, 12Hz, 15Hz and 17Hz) and the idle state, meeting the system's requirement of asynchronicity.

The signal acquired online was sliced into 4s long epochs every 2s, meaning that a decision from the classifier was issued every 2s. The power of the signal at the neighbourhood (±0.25Hz) of each stimulation frequency was estimated and combined to form a feature vector. The trained CSPs were applied to spatially filter the data and LDA was used for classification. All implementation was done using OpenViBE [START_REF] Renard | Openvibe: An open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments[END_REF].

The AR System

Fig. 6. Illustration of the implemented AR interface. The default view of the system (Left) represents the different objects in the field of view, with associated flickering icons. The fan and the light could be switched ON or OFF with a single command. The interaction with television was conducted through a hierarchical menu. After selecting the TV, the possible commands to issue appeared on the interface (Right).

Given the employed BCI paradigm (SSVEP), the first objective of this component was to provide the AR interface displaying the flickering SSVEP targets, and to provide the user information about the system state. Its second objective was to provide the contextual information about the objects detected in the user's field of view.

Targets were represented in the form of squared-shape overlays displaying icons representing the associated command. The icon could either represent the state the represented object, in the case of on-off objects, or the commands to navigate into the hierarchical menus for multiple command objects.

Regarding the user's context, in the scope of this prototype, the spatial positions of the interactable objects were hard-coded in the AR application, meaning that every time one of these positions entered the fov (field of view) of the user, the command associated with the object at this position was displayed. Another alternative would have been the use of fiducial markers in order to detect the objects in the fov of the user, regardless of their positions.

The AR interface was displayed on a Microsoft Hololens (see Figure 5) using a frontal ego-centered user-anchored display strategy [START_REF] Si-Mohammed | Towards bci-based interfaces for augmented reality: Feasibility, design and evaluation[END_REF] implemented using Unity (see Figure 6).

The Home Automation Platform

The commands carried out from the middleware were transmitted to the Home'In smart home platform 1 .

The commands were generated by the Middleware and carried to the homeautomation platform through TCP using the MQTT protocol [START_REF] Light | Mosquitto: server and client implementation of the mqtt protocol[END_REF]. The selected objects for the prototype were: a Phillips Hue connected lamp and a connected plug on which a fan was plugged, to illustrate the interaction with binary state objects. And a smart television was used to illustrate the interaction with multicommands objects.

The Middleware

The middleware was developed as a finite-state automaton using Python. It consisted in several listeners awaiting messages from the BCI component and the AR system, and relaying the appropriate messages to the components.

The implemented architecture of the developed system is summarized in Figure 7.

All binary states objects were considered as switches. A single SSVEP selection switches between their 2 states. In the case of multiple commands objects, the interaction was done through a hierarchical menu. First selecting the object, then selecting the available commands (spec N2.1).

Depending on the number of available commands at a certain time, and the context provided by the AR display, the middleware associates a flickering frequency to each object's icon and informs the AR system to display the flickering targets. When the BCI notifies the Middleware that a SSVEP command has been recognized, the Middleware notifies the AR system to update the display with the new objects states and change presented icons. The middleware also determines to what object and/or command, the SSVEP stimulation is associated and notifies the smart home platform.

The middleware allows to meet the specifications 2.5, 2.6. Regardless of the EEG system, as long as the BCI provides a detected frequency, the middleware can perform the required action and update the display regardless of its hardware type. In addition, as its role is to receive, interpret then retransmit commands, the middleware can also potentially enable to interact using other modalities (hand gestures, voice etc.) as long as they are simply used to determine the user's intent.

All in all, the BCI system was developed using OpenViBE. The middleware was developed in Python, and the AR system was developed using Unity. All the communications between the BCI, the AR system and the middleware were achieved using TCP (Transfer Communication Protocol) sockets.

Discussion

The contributions presented in this technology report are two-fold. First, we provided generic guidelines regarding the development of prototypes combining AR and BCI for home automation purposes. Following these guidelines in terms of functional and technical specifications namely operability, asynchronicity, modularity, hardware independence, contextuality and generalisability we proposed a new and reusable architecture decoupling each component of the system, and relying on a highly configurable middleware to operate the system.

Secondly, we illustrated how these guidelines can be implemented into a functional prototype integrated in a home-automation platform. This prototype, designed and implemented in collaboration with an industrial partner, is part of the global trend which aims at bringing BCIs out of laboratories.

Although the guidelines provided in this technology report are specifically targeting the specifications related to AR and BCI integration in the context of home automation, the field of smart home by itself also represents a challenge in many regards. Selecting the right communication protocol (Zigbee [START_REF] Safaric | Zigbee wireless standard[END_REF] or Zwave [START_REF] Yassein | Smart homes automation using z-wave protocol[END_REF] to name a few) or the appropriate architecture in terms of software and hardware [START_REF] Mocrii | Iot-based smart homes: A review of system architecture, software, communications, privacy and security[END_REF] requires a thorough analysis of the considered application.

In terms of AR and BCI, despite undeniable progress in these technologies, our prototype suffers from several limitations:

-Performance: The main limitation of our prototype, similar to many systems involving BCIs, lies in its poor performance when compared to existing alternatives for automation (handheld devices, voice commands) particularly for healthy users. Even though no formal study was conducted to evaluate the objective performance of our prototype, the informal testings showed a high variability in the subjects' performance, and generally remained below the accuracy of more traditional input systems.

-Cumbersomeness: Combining a BCI and a AR system, independently from the application, requires the association of cumbersome and bulky headsets which severely limits the use of these technologies over long period of times. A potential improvement may lie in the development all-inone headsets, integrating easy-to-install EEG electrodes directly into the AR headsets. -Environment configuration: One of the limitations regarding the AR configuration of our prototype was that the positions of the connected devices were hard set in the system. Meaning that moving the appliances from their positions would result in the system not being able to detect them.

A workaround solution would be to add fiducial markers to the appliances so that the AR system can detect them regardless of the position. Unfortunately, this solution still presents the drawback of not enabling the users to remotely operate an appliance that is not in his field of view. -Field of view: Another limitation of the AR system was regarding the width of augmented field of view, i.e. the area of the field of view where virtual content can be displayed. Given the employed AR headset (Hololens), the augmented area of the fov represented a rectangle of 34deg of visual angle. The size of this augmented fov plays a particularly important role with visual evoked potentials, as it may limit the number of displayable visual stimulations at once.

Moreover, associating a BCI with an AR system implies several technical challenges and difficulties to be dealt with. It is for example necessary to ensure the proposer setup of the headsets to prevent noise on the recorded signal, as well as to limit head movements to avoid muscle artefacts. It is also important to reduce the load on the AR core system in order to limit latency and jitter in displaying the stimulation (e.g. ensuring a stable flickering to elicit proper SSVEP responses), and to maximise the sycnhronization between the components (e.g. to accurately date the events in the case of event-related potentials).

Despite all these limitation, the strength of our approach lies in its high scalability that we believe can take benefit from any advancement in the BCI, the AR or the home-automation components of the overall system. The modularity of the proposed architecture can, for example, easily include any future improvement in the signal processing methods to improve the overall performance or the system. It could also easily benefit from the development of new hardware that can be easily replaced, to improve the user-friendliness of the system.

Conclusion

The aim of this technology report was to introduce a new architecture and methodology in building smart home systems based on BCIs and AR. It provided generic guidelines regarding the specifications and requirements that such systems should meet as well as a modular architecture enabling to meet these requirements. It also reported the result of a technological development aiming at integrating such an AR-BCI system in a home-automation platform that was showcased online. Despite the current limits of the system, the proposed prototype paves the way to the upcoming development and democratization of AR and BCI for non-medical, non-rehabilitation purposes.

Fig. 4 .

 4 Fig. 4. Illustration of the final prototype. The user can interact online with the three objects in front of him.

Fig. 5 .

 5 Fig. 5. Illustration of the apparatus used in the developed prototype. (Left) A Microsoft HoloLens AR headset and (Right) an mbt Smarting EEG headset.

Fig. 7 .

 7 Fig. 7. Illustration of the final prototype architecture. The components are the same as proposed in the generic architecture, and all communications were done through the middleware.

  General Automaton representing the states of the middleware. The default state of the middleware is the wait state. All the possible events during the interaction trigger the changing of state, the execution of the corresponding action, then the return to the wait state. S1: Update state, S2: BCI-handling state, S3: Decoding state, S4: Operation state

		New object	S1	Operate object	S4
		Update frequency map
		and display
	Wait State Wait State	No match		Selection	Command
		Frequency detected	S2	Frequency match	S3
	Fig. 3.

P300 is an Event Related Potential (ERP) usually measured through EEG and that is elicited when a rare and/or attended stimulus is displayed.

A Home-Automation platform from Orange SA.


Nathalie Bouquier

Sylvie Fromont

Jean-Christophe Zeeh

Camille Auziol

Pauline Larrousse

Bruno Robert

Mahel Zeghouf

Jacqueline Cherfils

Anne Debant
email: anne.debant@crbm.cnrs.fr

Susanne Schmidt
email: susanne.schmidt@crbm.cnrs.fr

APTAMER-DERIVED PEPTIDES AS POTENT INHIBITORS OF THE ONCOGENIC RHOGEF TGAT

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

INTRODUCTION

By remodeling the actin cytoskeleton, Rho GTPases regulate various cellular processes, such as proliferation, migration, cell adhesion and cell shape [START_REF] Etienne-Manneville | Rho GTPases in cell biology[END_REF].

They are activated by the Dbl family of Rho Guanine Nucleotide Exchange Factors (RhoGEFs), which accelerates their GDP/GTP exchange rate [START_REF] Rossman | GEF means go: turning on RHO GTPases with guanine nucleotide-exchange factors[END_REF].

RhoGEFs represent a large family (over seventy members in mammals) of complex proteins with numerous signaling domains, but they almost invariably contain a functional tandem, including a Dbl Homology (DH) domain responsible for guanine nucleotide exchange, followed by a Pleckstrin Homology (PH) domain, which targets the GEF to the plasma membrane and/or regulates nucleotide exchange [START_REF] Chhatriwala | The DH and PH domains of Trio coordinately engage Rho GTPases for their efficient activation[END_REF][START_REF] Lutz | Structure of Galphaqp63RhoGEF-RhoA complex reveals a pathway for the activation of RhoA by GPCRs[END_REF][START_REF] Rojas | Galphaq directly activates p63RhoGEF and Trio via a conserved extension of the Dbl homology-associated pleckstrin homology domain[END_REF][START_REF] Rossman | Multifunctional roles for the PH domain of Dbs in regulating Rho GTPase activation[END_REF][START_REF] Rossman | GEF means go: turning on RHO GTPases with guanine nucleotide-exchange factors[END_REF]. Deregulation of Rho GTPase function has been associated with various human disorders, including mostly cancer and metastasis, but also cardiovascular and hepatic disease, bacterial and viral pathogenesis, and developmental disorders, including neurodegenerative diseases [START_REF] Sahai | RHO-GTPases and cancer[END_REF][START_REF] Toksoz | The Rho small GTPase: functions in health and disease[END_REF]. Consistently, many Dbl family RhoGEFs have been isolated based on their oncogenic potency, which often results from a truncation of the protein, leading to uncontrolled GEF activity and subsequent aberrant Rho GTPase activation [START_REF] Eva | Isolation of a new human oncogene from a diffuse Bcell lymphoma[END_REF][START_REF] Katzav | vav, a novel human oncogene derived from a locus ubiquitously expressed in hematopoietic cells[END_REF][START_REF] Miki | Oncogene ect2 is related to regulators of small GTP-binding proteins[END_REF][START_REF] Whitehead | Expression cloning of lfc, a novel oncogene with structural similarities to guanine nucleotide exchange factors and to the regulatory region of protein kinase C[END_REF][START_REF] Whitehead | Expression cloning of lsc, a novel oncogene with structural similarities to the Dbl family of guanine nucleotide exchange factors[END_REF].

Rho GTPases and their GEFs therefore represent challenging targets for inhibition, not only to understand their function but also in pathology, and strategies to inhibit their function are actively being sought [START_REF] Bos | GEFs and GAPs: critical elements in the control of small G proteins[END_REF]. The main issue when trying to inhibit RhoGEFs is to achieve a high degree of specificity within such a complex and large family of related proteins, and to target protein-protein interactions which are not yet well characterized. To date only few strategies have been devised successfully, allowing the discovery of chemical and peptidic RhoGEF inhibitors, that block the activation of Rho GTPases by their cognate GEFs [START_REF] Blangy | Identification of TRIO-GEFD1 chemical inhibitors using the yeast exchange assay[END_REF][START_REF] Gao | Rational design and characterization of a Rac GTPase-specific small molecule inhibitor[END_REF][START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. We described previously peptide aptamer screening as such a strategy, which enabled us to discover the first RhoGEF inhibitor [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. Peptide aptamers are short peptides constrained by a bacterial Thioredoxin (TrxA) scaffold, which bind to their protein targets with high affinity [START_REF] Baines | Peptide aptamers as guides for small-molecule drug discovery[END_REF][START_REF] Hoppe-Seyler | Peptide aptamers: specific inhibitors of protein function[END_REF]. This technology has been applied initially to the discovery of inhibitors against various intracellular targets, involved mainly in cell-cycle control or cell survival [START_REF] Butz | Induction of apoptosis in human papillomaviruspositive cancer cells by peptide aptamers targeting the viral E6 oncoprotein[END_REF][START_REF] Colas | Genetic selection of peptide aptamers that recognize and inhibit cyclin-dependent kinase 2[END_REF][START_REF] Crnkovic-Mertens | Induction of apoptosis in tumor cells by siRNA-mediated silencing of the livin/ML-IAP/KIAP gene[END_REF][START_REF] Fabbrizio | Inhibition of mammalian cell proliferation by genetically selected peptide aptamers that functionally antagonize E2F activity[END_REF][START_REF] Martel | p53-dependent inhibition of mammalian cell survival by a genetically selected peptide aptamer that targets the regulatory subunit of protein kinase CK2[END_REF][START_REF] Nouvion | Modulation of Nr-13 antideath activity by peptide aptamers[END_REF]. Peptide aptamers present interesting advantages over other classes of inhibitory molecules, mainly because of their simple design and their high degree of binding specificity, which enables them to discriminate between closely related proteins within a functional family. But most remarkably, these highly combinatorial proteins are screened and designed to function inside living cells and allow the study of protein function within complex regulatory networks [START_REF] Bickle | Selection and characterization of large collections of peptide aptamers through optimized yeast two-hybrid procedures[END_REF].

The RhoGEF inhibitor we have isolated using this aptamer screening strategy, called TRIP (Trio Inhibitory Peptide ), targets specifically the DH2-PH2 tandem of the RhoGEF Trio and inhibits its activation of RhoA both in vitro and in intact cells, reverting the neurite retraction phenotype induced by Trio DH2-PH2 in PC12 cells [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. Most interestingly, although TRIP was initially selected with the TrxA scaffold, it remained equally active as a linear peptide [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF].

The recently identified oncogenic RhoGEF Tgat is an interesting novel candidate target for such peptidic inhibitors. Indeed, Tgat has been identified from Adult T-Cell Leukemia (ATL) patient cells as a gene with oncogenic potency and originates from an alternate splicing of the trio gene (hence the name Tgat, for Trio-related transforming Gene in ATL Tumor cells) [START_REF] Yoshizuka | An alternative transcript derived from the trio locus encodes a guanosine nucleotide exchange factor with mouse celltransforming potential[END_REF]. Tgat retains only the RhoA-specific DH2 domain of Trio and, instead of the associated PH2 domain, carries a unique C-terminal sequence of 15 amino acids. It induces cell transformation and tumor formation in nude mice [START_REF] Yoshizuka | An alternative transcript derived from the trio locus encodes a guanosine nucleotide exchange factor with mouse celltransforming potential[END_REF] and has been proposed to enhance tumor invasion by stimulating Matrix MetalloProteinases (MMPs) via the RECK protein [START_REF] Mori | Tgat oncoprotein functions as a inhibitor of RECK by association of the unique C-terminal region[END_REF] and by activating the transcription factor NF-B, which plays a crucial role in tumorigenesis, including ATL [START_REF] Yamada | Tgat, a Rho-specific guanine nucleotide exchange factor, activates NF-kappaB via physical association with IkappaB kinase complexes[END_REF].

In this context, designing peptide inhibitors against the RhoGEF Tgat is very challenging, not only from a pathological point of view, but also from a conceptual perspective, addressing the important issue of specificity when targeting proteins which, like RhoGEFs, belong to families with high homologies.

In this study we devised an optimization screen based on the TRIP peptide, which allowed us to identify a novel peptide that is active as a Tgat inhibitor, targeting its GEF activity in vitro in a highly specific manner. Moreover, it strongly reduces its oncogenic properties in vivo, most remarkably by decreasing foci formation and tumor development in nude mice.

Our peptide optimization strategy identifies the first inhibitor of the Tgat oncogene, and demonstrates that aptamers can be used to interfere with RhoGEF functions in vivo with exquisite specificity.

RESULTS

The GEF activity of the DH domain is required for Tgat-induced transformation.

In order to design inhibitors that would target Tgat oncogenic activity, we first established whether the GEF activity of Tgat is involved in transformation. To do so, we designed a Tgat mutant, called Tgat L190E , which harbors a point mutation in its DH domain, the equivalent mutation in Trio DH2-PH2 being known to abolish its exchange activity on RhoA (Figure 1A) [START_REF] Bellanger | Different regulation of the Trio Dbl-Homology domains by their associated PH domains[END_REF]. We established NIH3T3 cell lines stably expressing similar levels of GFP or GFP-tagged Tgat or Tgat L190E (Figure 1B, lower panel), and analyzed the ability of these constructs to activate RhoA and to induce transformation. We measured RhoA activation in intact cells by pull-down of RhoA-GTP, using the RhoA-binding domain (RBD) of its effector Rhotekin fused to GST (Figure 1B-C). Tgat strongly stimulated RhoA activation (8-fold over control), while the GEF-impairing mutation completely abolished the formation RhoA-GTP in cells. We then tested the oncogenic properties of the different Tgat constructs, by scoring the formation of foci in the different cell lines (Figure 1D-E). While Tgat-expressing cells formed numerous foci, Tgat L190E -expressing cells presented no foci after 3 weeks in culture, showing that the GEF activity of the DH domain is required for the transforming potential of Tgat. In addition, NIH3T3 cells stably expressing full length Trio did not exhibit any foci, showing that the transforming potential is not inherent to Trio but only to its oncogenic isoform Tgat (Figure 1D-E).

Strategy to identify a Tgat inhibitor.

Since the GEF activity of Tgat is necessary for transformation, molecules that block this biochemical activity could also inhibit its transforming potential. We previously identified a peptide aptamer, TRIP , which targets the RhoA-specific DH2-PH2 tandem of Trio [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. As Tgat harbors the DH2 domain of Trio, we tested whether Tgat activity was also inhibited by TRIP . However, to our surprise, when tested in a [ 3 H]-GDP dissociation inhibition assay in vitro, TRIP was only a weak inhibitor of Tgat (Ki app = 89 ± 33 M; see below).

We thus sought to optimize TRIP inhibition efficiency, by first determining which amino acids are essential for its inhibition, using an Ala-Scan analysis. Each residue of the active core of TRIP (amino acids 9-36; [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF] was mutated to alanine, except cysteines which were changed into serines. All TRIP mutants were then tested for their inhibitory activity on Trio DH2-PH2 in [ 3 H]-GDP dissociation assays. This analysis mapped two essential regions of TRIP , amino acids 9-20 and 28-33, where single mutations were sufficient to impair inhibition (Figure 2A). However, none of the mutants exhibited stronger inhibition towards Trio DH2-PH2 and were not further investigated on Tgat.

We next reasoned that peptides that would bind stronger to the GEF domain may also be better at inhibiting its activity. We thus generated a library of peptide aptamers derived from TRIP by random mutagenesis, which we screened for GEF binding in a yeast two-hybrid assay. We chose a system in which the threshold of interaction detection can be modulated by the concentration of the 3-aminotriazole (3-AT) drug [START_REF] Sardet | E2F-4 and E2F-5, two members of the E2F family, are expressed in the early phases of the cell cycle[END_REF]. Since Tgat is toxic in yeast, we used Trio DH2-PH2 to screen this TRIP-like peptide library. Thirty-five independent clones bound to Trio DH2-PH2 at concentrations of 3-AT at which no interaction with TRIP was detected anymore (80-120mM). These clones were then produced as GSTfusions and analyzed for their inhibition of Trio DH2-PH2, using the [ 3 H]-GDP dissociation assay. As shown in Figure 2B, 10 of them were stronger inhibitors than TRIP . Analysis of their sequence revealed that they contained one to four mutations per peptide, and that, consistently, most of them resided within the two regions identified as crucial for the inhibitory properties of TRIP (Figure 2B, shaded residues).

We then analyzed the inhibitory activity towards Tgat of TRIP E32G and TRIP T16M/L17S , the two TRIP-like peptides that displayed the highest inhibition on Trio DH2-PH2 (Figure 2B). Both peptides inhibited Tgat GEF activity in a dose-dependent manner in a kinetics fluorescence assay, while GST alone (not shown) or GST-TRIP , at the same concentrations, had no effect (Figure 2C). Accordingly, the apparent inhibition constant (Ki app ) of TRIP towards Tgat was 89 ± 33 M, and decreased to 7.4 ± 5 M for TRIP E32G and 5.1 ± 4 M for TRIP T16M/L17S (Figure 2D). These data show that TRIP E32G and TRIP T16M/L17S are both about 15 times more efficient than TRIP at inhibiting the exchange activity of Tgat.

Interestingly, the optimized peptides were equally efficient on Tgat and on Trio DH2, as shown by their similar Ki app values (Figure 2C and 2D). This suggests that the unique Cterminal extension of Tgat is not involved in the inhibitory mechanism of the optimized peptides, and is consistent with the fact that this sequence does not interfere with the GEF activity in vitro (data not shown).

Inhibition by TRIP peptides is specific for Tgat.

We then analyzed the specificity of the optimized TRIP peptides, by testing their inhibitory properties on other related RhoGEFs. We had shown previously that TRIP is not active on the RhoA-specific GEFs p115RhoGEF, Lbc, p63RhoGEF, nor on Dbl [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF] and unpublished results). Similarly, when tested in mant-GTP fluorescence kinetics at a concentration at which Tgat is fully inhibited, TRIP E32G and TRIP T16M/L17S had no effect on the exchange activities of these closely related RhoGEF/Rho-GTPase tandems, p115RhoGEF/RhoA, Lbc/RhoA, Dbl/RhoA, and even the very closely Trio-related p63RhoGEF/RhoA (70% identity within the DH-PH module), or Trio DH1-PH1/RhoG (40% identity with Tgat) (Figure 3). Taken together, these data show that the optimized TRIP peptides are highly specific for Tgat and Trio DH2.

TRIP E32G inhibits the transforming activity of Tgat in vivo.

We next analyzed whether our TRIP-like peptides inhibited Tgat-mediated RhoA activation in intact cells. For that purpose, NIH3T3 cells stably expressing Tgat were transfected with the GFP-tagged TRIP-like peptides or GFP alone, and RhoA activation levels were assessed by the GST-RBD pull-down assay (Figure 4). Although TRIP E32G and TRIP T16M/L17S inhibited the in vitro GEF activity of Tgat to a similar extent, TRIP E32G was more efficient than TRIP T16M/L17S at inhibiting Tgat-mediated activation of RhoA in cells (Figure 4A-B). These data show that, in addition to its effect on in vitro guanine nucleotide exchange, TRIP E32G inhibits Tgat GEF activity also in intact cells.

In order to verify the exquisite specificity of our peptide towards Tgat/DH2 in vivo, we analyzed by GST-RBD pull-down assay whether TRIP E32G could inhibit oncogenic Dblmediated RhoA activation in intact cells. Figure 4C shows that, in contrast to its effect on Tgat activity, TRIP E32G was not able to inhibit RhoA activation by Dbl, confirming the in vitro specificity of TRIP E32G towards Tgat.

We then investigated whether TRIP E32G was able to inhibit Tgat-induced transformation. To do so, we stably expressed GST or GST-TRIP E32G in Tgat-expressing NIH3T3 cells and characterized their transforming potential. After 3 weeks of culture, the foci present in Tgatexpressing cells were severely reduced when co-expressing TRIP E32G (Figure 5A-B). This reduction is not due to a non-specific effect of TRIP E32G on cell proliferation or apoptosis (data not shown). These data show therefore that targeting Tgat GEF activity with TRIP E32G is sufficient to impair Tgat transforming activity.

To further establish the inhibitory effect of TRIP E32G on Tgat transforming activity in vivo, we subcutaneously inoculated Balb/c nude mice with NIH3T3 cells expressing either Tgat, or Tgat and TRIP E32G , and analyzed their effect on tumor formation. Tgat-transformed cells produced tumors in ten out of twelve mice. Remarkably, when TRIP E32G was co-expressed with Tgat, only seven mice had tumors and we observed a delay of about three weeks in the formation of tumors (Figure 5C). In addition, even though tumor formation was not abolished, the weight of the tumors was significantly reduced when TRIP E32G was expressed (Figure 5D). Altogether, these data show that expression of TRIP E32G strongly reduces Tgat transformation activity in cells and affects tumor formation in nude mice, most likely by inhibiting Tgat-mediated GTP loading of RhoA.

DISCUSSION

Peptide aptamers as new inhibitors of RhoGEFs.

Because of their deregulation in many human disorders including cancer, Rho GTPases and their activating GEFs represent challenging targets for inhibition. In humans there are only 20 Rho GTPases but more than 70 RhoGEFs, and it appears that signaling specificity is mostly determined by the GEFs, which activate the GTPases at defined timing and location. RhoGEF inhibitors therefore represent an emerging field of investigation.

Here we developed a peptide aptamer screening strategy to inhibit the RhoGEF Tgat, a potential target in the ATL disease. Since Tgat is an isoform of the RhoGEF Trio, which includes the RhoA-specific DH2 domain plus a unique C-terminal sequence, we based our screen on our previously identified Trio inhibitor TRIP , the first peptidic RhoGEF inhibitor described, which targets the DH2-PH2 domain of Trio [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. Intriguingly, despite the fact that Tgat harbors the Trio DH2 domain, our original TRIP inhibitor was rather ineffective at inhibiting Tgat. This suggests that the PH2 domain of Trio is involved in the mechanism of action of TRIP , and that its replacement by the C-terminal extension decreases TRIP 's ability to inhibit the GEF activity of Tgat.

We show here that GEF inhibitors selected with the peptide aptamer screening approach are readily amenable to structure-activity relationship analysis and optimization. Of the 28 residues located in the active core of TRIP , alanine scanning mapped 9 residues in two regions (residues 9-20 and 28-33) that were critical for the catalytic activity, while 6 had a moderate effect and the others had no effect. We also show that peptide aptamer optimization can be achieved by random mutagenesis combined with a selection screen based on interaction strength. At least one third of the isolated clones yielded stronger inhibition, thus validating the rationale of the screen. The two selected peptides, TRIP E32G and TRIP T16M/L17S , were 15-fold more efficient than TRIP and inhibited Tgat GEF activity at concentrations lying in the low micromolar range. Interestingly, mutations found in these clones also fell within the two important regions identified by the Ala-scan. Furthermore, this approach allowed us to turn TRIP into a Tgat inhibitor, which could be achieved with as few as one mutation, E32G. It remains to be determined whether these different amino acids are important for binding to the GEF and/or for inhibition of the exchange reaction.

It should be emphasized that our screening and optimization method is effective, irrespective of the inhibitory mechanism, which is of big advantage for the discovery of inhibitors of protein-protein interactions. The way the original screen was performed, i.e. two-hybrid screening with the GEF as bait in the absence of GTPase, strongly suggests that the target of the peptides is the GEF itself, rather than the GTPase. This is reinforced by the fact that the peptides do not inhibit spontaneous GDP release from RhoA using [ 3 H]-GDP-loaded RhoA (data not shown), and by our specificity data in vitro and in intact cells, which show that other GEF activities towards RhoA are not inhibited (Figure 3 and4). At this stage we cannot, however, distinguish between competitive and allosteric inhibition, or even a less likely uncompetitive mechanism.

Remarkably, the characterization of our optimized TRIP peptides clearly shows that gain of efficiency is not associated with loss of specificity. Indeed, none of the RhoA-activating GEFs we tested, in particular the very closely related p63RhoGEF, were affected by either TRIP E32G or TRIP T16M/L17S . In addition, the TRIP peptides did not affect the activity of GEFs with different specificity such as the RhoG/Rac1-specific TrioDH1-PH1.

Peptide aptamers are functional in vivo

Our screening method demonstrates that TRIP E32G is not only effective and specific at inhibiting Tgat GEF activity in vitro, but that it also blocks Tgat-induced cell transformation and tumor formation in vivo. This is the first example of a peptidic RhoGEF inhibitor that is functional in vivo, and demonstrates that aptamers can be used as active peptides to perturb the function of GEFs in vivo. In this context, efficient in vivo delivery is a critical issue when working with peptides. To circumvent this problem, the use of recently developed cell penetrating peptides represents a good means of delivery for TRIP E32G , and could be an attractive strategy to investigate the contribution of Tgat in leukemogenesis. Indeed, to date, the incidence of Tgat in ATL leukemogenesis is unknown, but given the strong effect of Tgat on RhoA activation and transformation, we can hypothesize that Tgat is involved in the progression of ATL by contributing to RhoA-mediated proliferation and/or metastasis. Our series of TRIP peptides should now prove useful tools to decipher the cellular role of Tgat.

Peptide aptamers versus other GEF inhibitor screening strategies.

Besides our peptide aptamer screening approach, other strategies have recently been devised to discover chemical inhibitors of Rho GTPase/GEF tandems, and also other classes of small G proteins, such as the Arf family and their activating GEFs [START_REF] Blangy | Identification of TRIO-GEFD1 chemical inhibitors using the yeast exchange assay[END_REF][START_REF] Desire | RAC1 inhibition targets amyloid precursor protein processing by gamma-secretase and decreases Abeta production in vitro and in vivo[END_REF][START_REF] Gao | Rational design and characterization of a Rac GTPase-specific small molecule inhibitor[END_REF][START_REF] Mayer | Controlling small guanine-nucleotide-exchange factor function through cytoplasmic RNA intramers[END_REF][START_REF] Shutes | Specificity and mechanism of action of EHT 1864, a novel small molecule inhibitor of Rac family small GTPases[END_REF][START_REF] Viaud | Structure-based discovery of an inhibitor of Arf activation by Sec7 domains through targeting of protein-protein complexes[END_REF].

Computer-assisted virtual screening, for example, identified the NSC23766 compound, based on structure-function information of the Rac1/Tiam1 complex. This powerful molecule inhibits specifically Rac1-induced events in vitro and in vivo, however the targeted associated RhoGEFs include at least Tiam1 and Trio DH1-PH1 [START_REF] Gao | Rational design and characterization of a Rac GTPase-specific small molecule inhibitor[END_REF]. In silico screening also yielded the LM11 compound, which inhibits specifically the ARNO/Arf1 interface in vitro and is active in cells [START_REF] Viaud | Structure-based discovery of an inhibitor of Arf activation by Sec7 domains through targeting of protein-protein complexes[END_REF]. Given their membrane permeability, both NSC23766 and LM11 have the advantage of being easily applied in vivo.

The Yeast Exchange Assay is another screening method that allowed the identification of the TrioDH1-PH1 specific NPPD compound and its analogues [START_REF] Blangy | Identification of TRIO-GEFD1 chemical inhibitors using the yeast exchange assay[END_REF]. Like peptide aptamer screening, this strategy has the advantage over virtual screening of identifying inhibitors directly in cells, and without any bias as to the targeted interaction site.

Finally, in vitro RNA-aptamer screening selected the RNA aptamer M69 as an inhibitor of the Cytohesin/Arf1 tandem [START_REF] Mayer | Controlling small guanine-nucleotide-exchange factor function through cytoplasmic RNA intramers[END_REF]. Like peptide-aptamers, these RNA aptamers are highly combinatorial and easily screened, but their application as potential drugs remains limited, due to difficult in vivo delivery. To circumvent this problem, RNA-aptamerdisplacement represents an elegant method, in which a small-molecule library is screened for compounds that displace the RNA-aptamer from its target and reproduce its inhibitory activity [START_REF] Hafner | Inhibition of cytohesins by SecinH3 leads to hepatic insulin resistance[END_REF].

Our study shows that peptide aptamer screening represents a valid strategy for inhibitor identification that can be applied to a variety of different proteins, because of the in vivo screening method and the highly combinatorial libraries available, yielding strong affinity inhibitors. This is illustrated here by the identification of a highly specific peptidic RhoGEF inhibitor targeting the Tgat oncogene in vitro and in vivo.

SIGNIFICANCE

When trying to inhibit signaling pathways controlled by small G proteins and their activating GEFs, the challenge is that these are not mere enzymes with a well-defined active site that can be blocked. Rather, protein-protein interactions have to be targeted and the lack of reactive pockets to which inhibitors could bind is a challenging issue. This might in part explain why, although oncogenic Ras has been discovered more than 20 years ago, no inhibitor with clinical validation has been identified. Therefore, research has focused on trying to inhibit the guanine nucleotide exchange factors instead, and recent studies report the successful identification of such inhibitors.

The power of the strategy we used here to identify the TRIP peptides relies on the screening of a highly combinatorial aptamer library, generating immense possibilities of random peptides. This variety makes peptide aptamers very suitable molecules to inhibit complex protein-protein interactions such as the tandem RhoGEF/GTPase, and to discriminate between closely related proteins. One major advantage of this kind of approach is that the screening is cell-based, which gives a direct readout for toxicity and is more stringent. Moreover, peptide aptamers do not mimic cellular targets, which could have non-desired effects in cells. In addition, to circumvent the problem of in vivo delivery when using peptide aptamers, aptamer-displacement screens can be performed, in order to convert an aptamer into a small compound inhibitor [START_REF] Baines | Peptide aptamers as guides for small-molecule drug discovery[END_REF]. The advantage is that the corresponding compound targets the same site and shares the same properties as the already characterized peptide.

In conclusion, peptide aptamers represent a promising alternative for the discovery of leads for new therapeutic drugs.

EXPERIMENTAL PROCEDURES

DNA constructs -Tgat (aa 1-255) was designed by ligating dimerized oligonucleotides coding for the specific C-terminus of Tgat (15 aa) to the Trio DH2 domain (residues 1862-2101, corresponding to aa 1-240). The oligonucleotide sequences are available upon request. The Tgat L190E mutant was obtained using the Quick Change Site Directed Mutagenesis Kit (Stratagene Inc.), according to the manufacturer's instructions.

To create stable NIH3T3 cell lines, GFP-tagged Tgat, Tgat L190E and full length Trio were cloned into the puromycin-resistant retroviral vector pBabePuro. GST-tagged TRIP peptides were cloned into the G418-resistant retroviral vector pLXSN. For transient transfections, both Tgat and TRIP peptides were cloned into the pEGFP vector (Clontech Inc.). Myc-Dbl was a kind gift of Michael Olson (Beatson Institute for Cancer Research, Glasgow). For in vitro GEF assays, Tgat (aa 1-255) was fused to maltose-binding protein (MBP) by cloning into a modified pMAL C2X vector (New England Biolabs Inc.). The TRIP peptides were fused to GST by cloning into the pGEX-5X2 vector (GE Healthcare Inc.). All constructs were checked by sequencing.

Expression and purification of recombinant proteins -Tgat. MBP-Tgat and MBP-DH2

expression in E. coli was induced for 24h at 16°C with 0.1 mM isopropylthiogalactopyranoside (IPTG). After cell lysis (in 50 mM Tris pH 7.5, 1 mM EDTA, 2 mM MgCl 2 , 1 mM DTT), the suspension was centrifuged at 10,000g for 20 min, then at 400,000g for 1h30. The supernatant was applied to a Q-Sepharose column fast flow (GE Healthcare) equilibrated with lysis buffer. The protein was eluted with a linear gradient of 0-250 mM NaCl in 50 mM Tris pH 7.5. Fractions containing the protein were adjusted to a concentration of 2 M NaCl and loaded on a Phenyl sepharose Fast flow High Sub (GE Healthcare Inc.). The protein was eluted with a linear gradient of 2-0 M NaCl in 50 mM Tris pH 7.5. The purified proteins were concentrated on a Vivaspin concentrator (Vivascience AG Inc.) at 18 mg/mL.

Other proteins. Recombinant GST-Trio DH2-PH2, GST-Trio DH1-PH1, GST-Dbl (DH-PH domain), GST-Lbc (DH-PH), GST-p63RhoGEF (DH domain) and GST-RhoG were purified as described previously [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF][START_REF] Souchet | Human p63RhoGEF, a novel RhoAspecific guanine nucleotide exchange factor, is localized in cardiac sarcomere[END_REF]. Expression and purification of GST-p115RhoGEF using the baculo virus system will be described elsewhere.

GST-Peptides. GST-TRIP peptides were purified as described [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF], except that the cell lysate was centrifuged as above, before loading on a GSTrap Fast Flow column (GE Healthcare Inc.) equilibrated with lysis buffer. Peptides were eluted with reduced glutathione (10 mM) in Tris 50 mM pH 7.5 and concentrated on Vivaspin concentrator at about 5-10 mg/mL.

Optimization of TRIP -Alanine-scanning of TRIP . Every amino acid of the active core of TRIP (amino acids 9-36) was mutated to alanine (or serine for cysteine residues) by site directed mutagenesis of GST-TRIP . Each TRIP mutant was tested for its inhibitory activity on DH2-PH2 in [ 3 H]-GDP dissociation assays.

Two-hybrid screening of TRIP -like peptides. An aptamer library derived from TRIP was created by PCR-based random mutagenesis of TRIP inserted into the yeast two-hybrid vector pPC86. Sequencing of a statistically representative number of clones yielded a mutation rate of ~3 mutations/clone. 6x10 5 independent clones were screened for interactors, using Trio DH2-PH2 (in the pPC97 vector) as a bait, in the MAV103 yeast strain, on high concentrations of 3-AT (3-amino-triazol, Sigma) (80-120mM). Selected peptides were then produced as GST-fusions and analyzed for their inhibition of Trio DH2-PH2 using the [ 3 H]-GDP dissociation assay.

Nucleotide exchange kinetics assay -Specific exchange rates of Tgat were measured with a fluorescence-based kinetics assay, using a 6His-RhoA construct (gift of Dr Derewenda, Charlottesville University, Virginia) purified as described [START_REF] Oleksy | Preliminary crystallographic analysis of the complex of the human GTPase RhoA with the DH/PH tandem of PDZ-RhoGEF[END_REF]). Exchange activities were followed by fluorescence resonance energy transfer (FRET) between the GTPase tryptophanes ( ex=292nm) and the methylanthranyloil group of mant-GTP ( em=440nm) as described [START_REF] Zeeh | Dual specificity of the interfacial inhibitor brefeldin a for arf proteins and sec7 domains[END_REF]. All fluorescence measurements were performed with a CARY Eclipse fluorimeter (Varian). For each k obs determination, RhoA (1 M) and Tgat (or Trio DH2) were preincubated 3 min at 25°C in 700 L reaction buffer (50 mM Tris pH 7.5, 50 mM NaCl, 2 mM MgCl 2 , 1 mM DTT). The exchange reaction was initiated by 10 M mant-GTP and measured for 10 min until the plateau was reached. k obs were calculated by fitting the FRET fluorescence changes to a single exponential, using the Kaleidagraph software. Specific exchange activities were calculated by linear regression of k obs values determined for a range of GEF concentrations (0, 0.2, 0.3, 0.4, 0.5 and 1 M).

Nucleotide exchange inhibition assays -Radioactive [ 3 H]-GDP dissociation assays were performed as described [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. Briefly, 0.15 M GST-Trio DH2-PH2 was preincubated for 15 min with 3 M of GST-TRIP inhibitors. The reaction was started by addition of 0.4 M [ 3 H]-GDP-loaded RhoA and 1mM GTP, and the reaction mix was filtered after 0 min and 15 min incubation at 25°C. Inhibition efficiency is expressed as the ratio between [ 3 H]-GDP-bound RhoA at 15 and 0 min. Apparent inhibition constants (Ki app ) of TRIP-like peptides were determined from k obs values obtained at increasing peptide concentrations using the above fluorescence nucleotide exchange assay. Ki app was calculated from the hyperbolic fit of k obs values as a function of the inhibitor concentration as described [START_REF] Zeeh | Dual specificity of the interfacial inhibitor brefeldin a for arf proteins and sec7 domains[END_REF].

TRIP-like peptide specificity was assayed using mant-GTP fluorescence kinetics ( ex =360nm, em =460nm) in a FL X 800 Microplate Fluorescence Reader (BioTek Instruments). 0.5 M Tgat, p63RhoGEF, Lbc, and p115RhoGEF, or 0.1 M Dbl and Trio DH1-PH1 were preincubated 5 min at 25°C in the presence of 20 M GST, GST-TRIP E32G or TRIP T16M/L17S and 1 M mant-GTP. The exchange reaction was initiated by addition of 1 M RhoA or RhoG and monitored for 10 min.

Cell lines, transfection and focus formation assay -NIH3T3 cells were maintained as described previously [START_REF] Sirvent | The tyrosine kinase Abl is required for Src-transforming activity in mouse fibroblasts and human breast cancer cells[END_REF].

Transient transfection experiments were performed using the Jet PEI reagent, according to the manufacturer's protocol (QBiogene Inc.). NIH3T3 cell lines stably expressing GFP-Tgat, GFP-Tgat L190E , or GFP-Trio, with or without the GST-TRIP peptides, were generated as follows : the indicated retroviral constructs were transfected into BOSC packaging cells, using the Lipofectamine reagent (Invitrogen Inc.). Forty-eight hours after transfection, viruscontaining supernatants were collected and used to infect NIH3T3 cells. Infected cells were selected with 6 g/mL puromycin and/or 1mg/mL G418 and stable transfectants were pooled after selection. Tgat or TRIP mRNA levels in the different cell lines were monitored by RT-PCR, and protein expression levels by Western blot analysis using a polyclonal anti-GFP antibody (Torrey Pines Laboratories).

Focus formation assays were performed using stable NIH3T3 cell lines as indicated, seeded at 5x10 4 cells in 6-well plates and maintained for 15 to 21 days in 10% FBS. Medium was renewed every two days. After staining with Crystal Violet (1%), plates were photographed and foci were scored using the Metamorph software. All experiments were done in triplicate.

RhoA activation assay in cells -

The level of GTP-bound RhoA was measured by a GST pulldown assay as described [START_REF] Schmidt | Identification of the first Rho-GEF inhibitor, TRIPalpha, which targets the RhoA-specific GEF domain of Trio[END_REF]. Briefly, cell lysates were incubated with glutathione beads coated with the recombinant Rho-binding domain (RBD) of the RhoAspecific effector Rhotekin (Cytoskeleton Inc.). Total or GTP-bound RhoA in the samples was revealed by Western blot analysis, using a monoclonal anti-RhoA antibody (Santa-Cruz Biotechnology Inc.).

Mice and xenografting -Female Balb/c nu/nu mice were purchased from Charles River

France and used at 6-8 weeks of age. 12 Balb/c nu/nu mice were subcutaneously grafted with

------------------------------G---------- ---------------MS------------------------- ---------------M------G------------------- T----------------D--------L--------------- -------------------------------G---T------ ---------------A-G-------------G-----N---- -----------------A-----A-------G---------- ----

  2x10 6 cells of each cell line on both sides (Tgat on the left and Tgat+TRIP E32G on the right flank of the leg). The appearance of tumors was scored visually every week. 10 weeks post graft, mice were euthanized and tumors excised and weighed. mRNA and protein levels in the tumors were verified by RT-PCR and Western blot (data not shown). All experiments on mice have been approved by the internal ethical committee of the IRCM and have been performed by B.R. under the authorization number N° 34.156, in the animal facility with agreement N°B 34-172-27.

FIGURE LEGENDS Figure 1 :

 LEGENDS1 FIGURE LEGENDS

Figure 2 :

 2 Figure 2: Identification and characterization of optimized inhibitory TRIP-like peptides

Figure 3 :

 3 Figure 3: Specificity of the optimized inhibitory TRIP peptides Comparison of TRIP E32G and TRIP T16M/L17S inhibition efficiency on different

Figure 4 :

 4 Figure 4: TRIP E32G inhibits Tgat GEF activity in cellsRhoA activation in NIH3T3-Tgat cells stably transfected with GFP, GFP-TRIP E32G or GFP-TRIP T16M/L17S was assayed by the GST-RBD-pulldown assay as described in Figure1B. A.

Figure 5 :

 5 Figure 5: TRIP E32G inhibits the transforming activity of Tgat in vivo.

  Figure2

ACKNOWLEDGEMENTS

We thank Michel Brissac and Imad Aït Arsa for technical assistance in manipulating the nude mice in the animal facility of CRLC Val d'Aurelle in Montpellier, Claire Macari and Sandra Pierredon for technical help, Alexandra Joubert (LEBS, CNRS, Gif-sur-Yvette) for the gift of purified p115RhoGEF, Pierre Travo, Julien Cau and Sylvain De Rossi from Montpellier Rio Imaging for invaluable help and constant interest. Jean-Michel Bellanger, Philippe Pasero, Claude Sardet and Gilles Divita are acknowledged for helpful discussion and critical reading of the manuscript. This work was supported by grants from the CNRS, the Agence Nationale de la Recherche ANR-PCV (Physique et Chimie du Vivant) (A.D. and J.C.), the Association pour la Recherche contre le Cancer (grant #2736 to A.D., PhD grants to C.A. and J.C.Z). All authors except Bruno Robert are members of the CNRS consortium GDR2823.