Imran Rafiq Quadri
email: imran.quadri@lifl.fr

Samy Meftali
email: samy.meftali@lifl.fr

Jean-Luc Dekeyser
email: jean-luc.dekeyser@lifl.fr

Integrating Mode Automata Control Models in SoC Co-Design for Dynamically Reconfigurable FPGAs

The number of integrated transistors that can be contained on a chip are increasing at an exponential rate, along with rise in targeted sophisticated applications. Thus the design of Systems-on-Chip (SoC) is becoming more and more complex. Hence there is a critical need to find new seamless methodologies and tools to handle the SoC co-design aspects. This paper presents a novel approach for expressing system adaptivity and reconfigurability in Gaspard, a SoC co-design framework, with special focus on partially dynamically reconfigurable FPGAs. The framework is compliant with UML MARTE profile proposed by Object Management Group, for modeling and analysis of realtime embedded systems. The overall objective is to carry out system modeling at a high abstraction level expressed in UML; and afterwards, transform these high level models into detailed enriched lower level models in order to automatically generate the necessary code for final FPGA synthesis.

I. INTRODUCTION

Modern Systems-on-chips (SoCs) have become integral for designing embedded systems for targeting intensive parallel computation applications. Continuous advances in SoC technology permit to increase the number of hardware resources on a single chip. In parallel, the targeted application domains: such as multimedia video codes, radar/sonar detection systems are becoming more sophisticated. These data-parallel applications focus on regular data partitioning, distribution and their access, etc. This balance of evolution has led to a system complexity issue which is one of the main hurdles faced by SoC designers. The fallout of this complexity is that the system design, particularly software design, does not evolve at the same pace as that of hardware leading to a productivity gap.

Adaptivity is also a critical issue related to SoC design for coping with end user requirements/environments. Mode based control semantics play a significant role for these complex systems; such as switching between color and black and white modes in video processing applications. This type of control offers Quality of Service features to designers/end users. However, the control semantics should be generic enough to be applied to both software and hardware design aspects.

State of the art FPGAs can change their functionality at runtime, known as Partial Dynamic Reconfiguration (PDR) [START_REF] Lysaght | Invited Enhanced Design Methodologies and CAD Tools for Dynamic Reconfiguration Xilinx FPGAs[END_REF]. PDR allows to modify specific regions of an FPGA on the fly, hence time-sharing the available hardware resources for executing multiple (mutually exclusive) tasks. It permits context switching depending upon application needs, hardware limitations and QoS requirements. Currently only Xilinx FPGAs fully integrate partial dynamic reconfiguration. These FPGAs also support internal self dynamic reconfiguration, in which an internal controller (a hardcore/softcore embedded processor) manages the reconfiguration [START_REF] Blodget | A lightweight approach for embedded reconfiguration of FPGAs[END_REF].

Raising design abstraction levels can be viewed as an effective solution to resolve the SoC complexity issues. The challenge is to find effective design methodologies that raise the design abstraction levels to reduce overall complexity, while handling issues: such as accurate expression of inherent system parallelism such as application loops; and hierarchy.

Model Driven Engineering can be viewed as a high level SoC co-design approach, that enables high level system modeling (of both software and hardware). Model transformations can be utilized to generate executable models or executable code, from high level models. MDE is also supported by large number of industry standards and tools.

Gaspard [START_REF]GASPARD SoC Framework[END_REF], [START_REF] Gamatié | A model driven design framework for high performance embedded systems[END_REF] is an MDE-based SoC co-design framework dedicated to parallel hardware and software. It is based on the UML MARTE profile [START_REF] Omg | Modeling and analysis of real-time and embedded systems (MARTE)[END_REF] proposed by Object Management Group; and allows to move from high level MARTE specifications to different execution platforms. It exploits the inherent parallelism included in repetitive constructions of hardware elements or regular constructions such as application loops. The applications targeted by Gaspard also focus on a specific application domain, that of data-parallel applications.

In this paper we present an extension of a generic control semantic for specification of system adaptivity in modern SoCs. The introduced semantics are integrated in Gaspard and are specified at MARTE high abstraction levels. This control extension is applied to the IP deployment level in Gaspard to focus on FPGA synthesis and is specially oriented towards partial dynamic reconfiguration. A mechanism for introducing control determinism at the Register Transfer Level (RTL) is also presented which aids to introduce regularity between the control/data flow present at this level. The goal is to specify part of the reconfigurable system at a high abstraction level: notably the reconfigurable region and the reconfiguration controller. Afterwards, using model transformations, the gap between high level specifications and low implementation details can be bridged to automatically generate the code required for the creation of bitstream(s) for final FPGA implementation.

The rest of this paper is organized as follows. Related works are summarized in section 2. An overview of the Gaspard framework is provided in section 3. Section 4 presents the control model for IP deployment and for PDR integration in FPGAs. Finally section 5 gives the conclusion.

II. RELATED WORKS

We partition the related works in three distinct parts. The first part summarizes concepts related to mode automata and their integration in Gaspard. The second part details the usage of UML for SoC Co-design, FPGA synthesis and PDR aspects. Finally we give some works related to PDR at the RTL level.

Mode automata [START_REF] Maraninchi | Mode-automata: About modes and states for reactive systems[END_REF] are based on the concept of combination of formalisms, similar to multi-paradigms, proposed to express data flow languages with different representative styles. They are used in synchronous domain for formal validation/verification purposes. Mode automata are primarily composed of modes and transitions, with each mode having the same interface. Equations can be specified in modes. Transitions are normally associated with conditions, to act as triggers. Mode automata can also be parallely or hierarchically composed, and adopt a state based approach. An initial version of mode automata based control for Gaspard has been proposed in [START_REF] Labbani | Introducing control in the gaspard2 data-parallel metamodel: Synchronous approach[END_REF], for expressing dynamic adaptivity features at Gaspard application specification level. However, unlike traditional mode automata, the control and data computations are specified independently, allowing a clear distinction between the two flows. Extensions to this work have been proposed in [START_REF] Gamatié | A Model for the Mixed-Design of Data-Intensive and Control-Oriented Embedded Systems[END_REF], [START_REF] Yu | A MARTE-Based Reactive Model for Data-Parallel Intensive Processing: Transformation toward the Synchronous Model[END_REF] and address hierarchical and parallel compositions.

A Simulink-based graphical MPSoC design approach is presented in [START_REF] Atat | Simulink-based MPSoC Design: New Approach to Bridge the Gap between Algorithm and Architecture Design[END_REF], but lacks MDE concepts. In contrast, [START_REF] Gailliard | Transaction level modelling of SCA compliant software defined radio waveforms and platforms PIM/PSM[END_REF] integrates MDE for design of a Software-Defined Radio, but only pure UML specifications are used. Milan [START_REF] Mohanty | Rapid design space exploration of heterogeneous embedded systems using symbolic search and multi-granular simulation[END_REF] is another MDE-based SoC co-design project, but lacks MARTE integration. Mopcom [START_REF] Koudri | Using MARTE in the MOPCOM SoC/SoPC Co-Methodology[END_REF] integrates MDE and MARTE and is able to carry out FPGA synthesis, but does not consider PDR aspects. In [START_REF] Berthelot | A Flexible system level design methodology targeting run-time reconfigurable FPGAs[END_REF], a SynDEx based design flow is presented to manage dynamic reconfiguration in FPGAs, but does not integrates MDE concepts. Similarly [START_REF] Boden | GePARD -a High-Level Generation Flow for Partially Reconfigurable Designs[END_REF] present a high level synthesis flow for PDR, but does not take MDE into account. While works such as [START_REF] Damasevicius | Application of UML for hardware design based on design process model[END_REF] and [START_REF] Mcumber | UML-based analysis of embedded systems using a mapping to VHDL[END_REF] focus on generating VHDL from UML state machines, they are not capable of managing high performance data intensive applications.

In PDR domain, Xilinx initially proposed two design flows, which were not very effective leading to new alternatives. An effective modular approach for 2-D reconfigurable modules was presented in [START_REF] Sedcole | Modular Partial Reconfiguration in Virtex FPGAs[END_REF]. [START_REF] Becker | Real-Time Dynamically Run-Time Reconfigurations for Power/Cost-optimized Virtex FPGA Realizations[END_REF] implemented 1-D modular reconfiguration using a horizontal slice based bus macro in order to connect the static and partial regions. They followed by placing arbitrary 2-dimensional rectangular shaped modules using routing primitives [START_REF] Huebner | New 2-Dimensional Partial Dynamic Reconfiguration Techniques for Real-Time Adaptive Microelectronic Circuits[END_REF]. This approach has been further refined in [START_REF] Schuck | A framework for dynamic 2D placement on FPGAs[END_REF]. In 2006, Xilinx introduced the Early Access Partial Reconfiguration Design Flow [START_REF] Xilinx | Early Access Partial Reconfigurable Flow[END_REF] that integrated concepts of [START_REF] Sedcole | Modular Partial Reconfiguration in Virtex FPGAs[END_REF] and [START_REF] Becker | Real-Time Dynamically Run-Time Reconfigurations for Power/Cost-optimized Virtex FPGA Realizations[END_REF]. Works such as [START_REF] Bayar | Dynamic Partial Self-Reconfiguration on Spartan-III FPGAs via a Parallel Configuration Access Port (PCAP)[END_REF] focus on implementing softcore internal configuration ports on Xilinx FPGAs such as Spartan-3, that do not have the hardware Internal Configuration Access Port (ICAP) reconfigurable core, for implementing PDR. Works such as [START_REF] Claus | A new framework to accelerate Virtex-II Pro dynamic partial self-reconfiguration[END_REF], [START_REF] Cuoccio | A Generation Flow for Self-Reconfiguration Controllers Customization[END_REF] illustrate usage of customized ICAPs. Finally in works such as [START_REF] Koch | An adaptive system-on-chip for network applications[END_REF], the ICAP reconfigurable core is connected with Network on chips (NoCs) based FPGAs.

While there exists lots of related tools and works; we have detailed only few and have not given an exhaustive summary. To the best of our knowledge, only our methodology takes into account the following domains: SoC co-design and adaptivity features, data-parallel applications, control/data flow, MDE, UML MARTE profile and PDR for FPGAs; which is the novelty of our design framework.

III. GASPARD: MDE BASED SOC CO-DESIGN FRAMEWORK

Gaspard [START_REF]GASPARD SoC Framework[END_REF], [START_REF] Gamatié | A model driven design framework for high performance embedded systems[END_REF] is a MDE based MARTE compliant SoC design framework, that allows rapid design and code generation with the aid of UML graphical tools and technologies such as Papyrus1 and Eclipse Modeling Framework2 (EMF). 1 shows a global view of the Gaspard framework. In Gaspard, system co-modeling is carried out using the UML MARTE profile at high abstraction levels. Gaspard permits modeling of software applications, hardware architectures, their allocations (mapping of applications onto architectures) and IP deployment separately, but in a unique modeling environment. In Gaspard, models of software applications and hardware architectures can be defined concurrently and independently thus allowing a clear separation of concerns between the hardware/software models. While MARTE is suitable for modeling purposes, it lacks the means to move from high level modeling specifications to execution platforms. In order to bridge this gap, Gaspard introduces the notion of IP deployment that associates every elementary component, the building blocks of both the hardware and the application, to an implementation, thus facilitating IP reuse. Up till the deployment level, the integrated models are platform-independent.

In order to generate executable code to target different execution platforms such as FPGA synthesis [START_REF] Quadri | A model driven design flow for fpgas supporting partial reconfiguration[END_REF], Gaspard adopts MDE model transformations, as shown in Figure .1. Model transformation chains allow to move from high abstraction levels to low enriched levels. Usually, the initial high level models contain only domain-specific concepts, while technological concepts are introduced seamlessly later on in the intermediate levels.

It should be observed that the transformation chains are currently unidirectional in nature. While existing RTL chain only generates synthesizable VHDL; in order to implement dynamic reconfiguration by usage of an embedded processor, the extended RTL chain must generate the state machine source code in C language as well. However, this future extension has not been illustrated in Figure .1.

A. MARTE Repetitive structure modeling concepts

Gaspard has also contributed in the development of the MARTE standard. One of the key MARTE packages, the Repetitive Structure Modeling (RSM) package has been inspired from Gaspard. Gaspard, and in turn RSM, is based on Array-OL [START_REF] Boulet | Array-OL revisited, multidimensional intensive signal processing specification[END_REF] model of computation, that describes the potential parallelism in a system; and is dedicated to data intensive multidimensional signal processing. In Gaspard, data are manipulated and represented in the form of multidimensional arrays. RSM allows to describe the regularity of a system's structure and topology in a compact manner.

Gaspard utilizes RSM for modeling of complex regular hardware architectures (such as multiprocessor architectures) and parallel applications. For a software functionality, RSM allows to express both data parallelism and task parallelism effectively. A repetitive component expresses data parallelism in an application: in the form of sets of input and output patterns consumed and produced by the repetitions of the interior part. A hierarchical component can contain several parts. It defines complex functionalities in a modular way and provides a structural aspect of the application in order to express task parallelism. The shape of a pattern is described by means of a tiler connector, that describe the tiling of produced and consumed arrays. The reshape connector allows to represent complex link topologies in which the elements of a multidimensional array are redistributed in another array. An interrepetition dependency is used to specify an acyclic dependency among the repetitions of the same component, compared to a tiler, that describes the dependency between the repeated component and its owner component. Particularly, an interrepetion dependency specification leads to the sequential execution of repetitions. A defaultlink provides a default value for repetitions linked with an interrepetion dependency, when the source of dependency is absent.

B. Gaspard control modeling semantics

We first recall the basic control semantics present in Gaspard. Several basic control concepts, such as Mode Switch and State Graphs are initially presented. Afterwards, a simple composition of these concepts, which builds the mode automata, is presented. The notion of exclusion among modes in mode automata permits to separate different computations. 2) State graphs: A state graph in Gaspard as presented in Figure .3, is similar to state charts [START_REF] Harel | Statecharts: A Visual Formalism for Complex Systems[END_REF], used for modeling of system behavior via a state-based approach. It can be described as a graphical representation of transition functions [START_REF] Gamatié | A Model for the Mixed-Design of Data-Intensive and Control-Oriented Embedded Systems[END_REF]. A state graph is constructed of a set of vertices, called states. A state connects with other states through directed edges which are called transitions. Transitions can have conditions depending upon some events or Boolean expressions. A transition is triggered when its associated condition is met. A special label all, on a transition outgoing from state s, indicates any other events that do not satisfy the conditions on other outgoing transitions from s. Each state is linked to some mode value specifications that provide mode values for the state.

3) Composition of mode switch and state graph: Once mode switches and state graphs are conceptualized, a MACRO component can be used to compose them together. The macro in Figure .4 illustrates one possible composition. The components Gaspard State Graph and Mode Switch Component are associated with state graphs and mode switches respectively. In the macro, the Gaspard state graph produces a mode value (or a set of mode values) associated with the current executing state and sends the value(s) to the mode switch component which switches the modes accordingly. Some data dependencies (or connections) between these components are not always necessary, for example, data dependency between I d and i d . They are drawn with dashed lines in Figure .4. The illustrated figure is used as a basic composition, however, other variations are also possible, for instance, one Gaspard state graph can control several mode switch components.

Once the macro is constructed, it is possible to create a Gaspard Mode Automata (GMA). The Gaspard state graph acts as a state-based controller and the mode switch component achieves the mode switch function. Secondly, interrepetition dependency specifications should be specified for the macro when it is placed in a repetitive context. This is because a macro component represents a single transition between states. For continuous transitions similar to mode automata, the macro must be repeated. An interrepetition dependency enables continuous sequential execution and permits creation of a mode automata. An interrepetition dependency connects the repetitions of the macro structure and conveys the current state. Hence, it sends the target state of one repetition as the source state for the next repetition of the macro. The states and transitions of the automata are represented via the Gaspard state graph. The data computations inside a mode are set in the mode switch component. The detailed formal semantics related to Gaspard mode automata can be found in [START_REF] Gamatié | A Model for the Mixed-Design of Data-Intensive and Control-Oriented Embedded Systems[END_REF].

C. IP deployment in Gaspard

The Gaspard IP deployment level allows one to select a specific IP for each elementary component of application or architecture, among several possible choices. The reason being: in SoC design, a functionality can be implemented in different manners. For example, an application functionality can either be optimized for a processor (written in C/C++), or implemented as a hardware accelerator using Hardware Description Languages. Hence the deployment level distinguishes between the hardware/software functionalities; and allows to move from platform-independent high level models to platform-dependent models, for eventual implementation.

The concept of VirtualIP represents the functionality of a given elementary component, independently from the compilation target. For an elementary component J, it associates J with all its possible IPs. The desired IP(s) is (are) then selected by the SoC designer by linking it (them) to J via an implements dependency. Finally, the CodeFile concept determines the physical path related to the source code of an IP, along with required compilation options. . At the deployment level, this component may have several possible implementation choices; either for the same execution platform (same abstraction level): in a given language, or for different ones. In the given example, the component can be simulated in SystemC or can be implemented as a hardware accelerator in an FPGA via VHDL. The implements dependency from the Huffman-VHDL component to the HuffmanCoding represents the targeted implementation choice/execution platform.

Figure.6: Linking a CodeFile to an IP

A limitation of the deployment level is that for final compilation for code generation, an elementary component can be linked to only one IP. This drawback does not effect platforms where system adaptivity in not concerned, however in the case of dynamically reconfigurable FPGAs, this issue must be addressed. The deployment level is thus extended to integrate reconfiguration aspects which are presented later in the paper.

IV. INTEGRATING PDR ASPECTS IN GASPARD: ADDING CONTROL EXTENSIONS AT IP DEPLOYMENT LEVEL

For implementing PDR, an embedded reconfiguration controller has two functionalities: one responsible for communicating with the FPGA ICAP reconfigurable core; and a state machine for switching between the configurations. The first functionality is written manually due to some low level RTL details which cannot be expressed via a modeling approach; and is treated as a macro. The control model introduced at the deployment level is used to generate the second functionality automatically via model transformations. Finally the two parts can be used to implement PDR in an FPGA that can be divided into several static/reconfigurable regions.

In our works, the application being modeled at the high abstraction levels in transformed into a hardware functionality, i.e., a hardware accelerator, by means of the model transformations [START_REF] Quadri | A model driven design flow for fpgas supporting partial reconfiguration[END_REF]. This hardware accelerator is treated as a reconfigurable region at the RTL level. A reconfigurable region can have several implementations, with each having the same interface, and can be viewed as a mode switch component with different modes. We now explain the control model at the deployment level. We first summarize the extended version of the deployment that determines the implementations linked to the reconfigurable region, followed by the deployment control model that is responsible for generating the controller. Adopting control in deployment allows to create several configurations for final FPGA implementation. Each configuration is defined as a collection of different IPs, with each IP linked to its respective elementary component. Thus, a new stereotype ConfigurationInfo (as illustrated in Figure .7) is introduced, that is added to the implements dependency associating an IP with its corresponding elementary component. A ConfigurationNumber attribute related to this stereotype allows the designer to link, a particular IP of an elementary component, with a desired final configuration.

A. Introduction of "Configurations" at the deployment level

Figure.8: Deploying a Gaspard application having three elementary components

An elementary component can also be linked to the same IP in different configurations. This point is related to the semantics of FPGA partial bitstreams which support glitchless dynamic reconfiguration. If a configuration bit holds the same value before/after reconfiguration, the resource controlled by that bit does not experience any loss in its operation. If the same IP for an elementary component is used in several configurations, that IP is not swapped during reconfiguration. Hence, it is possible to link several IPs to a corresponding elementary component; each link specifying a unique configuration. Also, for any n number of configurations, with each having m elementary components, each elementary component of a configuration must have at least one IP. This enables successful creation of a complete configuration.

Finally, two enumerations, Modes and States are created. The first contains the mode values related to all possible configurations; while the second contains the states of the Gaspard state graph. With information provided in deployment and model transformations, each state (configuration) is linked to its respective IPs. Each state also has an associated Boolean flag (with a default value of 0). A value of 1 specified by the designer indicates that this state is chosen as the initial state/configuration for the Gaspard state graph. This information is then sent onto the control concept modeled in the second phase of deployment using the model transformations. By modifying the RTL level model transformations, it is possible to generate different hardware accelerators (different configurations). Once the configurations are created, each is viewed as a source for a partial bitstream. Each partial bitstream signifies a unique implementation for a reconfigurable hardware accelerator, connected to an embedded controller. While this extension allows to create different configurations, the state machine part of the controller is created manually. For automatic generation of this functionality, the deployment extensions are inadequate. We then use the existing Gaspard control concepts to solve these issues.

B. Introducing control model at the deployment level

The first point is related to the nature of modes in a mode switch component. For a mode switch component at the application level control model, each mode is an instance of an application component that can be either elementary, repetitive or hierarchical in nature. However, at the deployment level, a mode switch component is related to an elementary component and contains, all the related IPs as its modes.

As explained in section III.B, only the input mode ports are necessary for the creation of a mode switch component. In control model at application level, the related data flow is represented via data input/output ports. However as control model at the deployment level is concerned with behavioral semantics and not structural ones, the data flow is not explicitly expressed. For all IPs linked to an elementary component, their input/output data flow values are equivalent to that of the elementary component. Model transformations are capable to link ports of each of the IPs in a mode switch component to the corresponding elementary component. This version of a mode switch component is termed as a Deployed Mode Switch Component. We apply a condition that for the construction of a deployed mode switch component, the corresponding elementary component must have more than one available IP.

Another difference in the deployment control model is related to the modeling of the collaborations associated with a deployed mode switch component, for expressing its behavior. Compared to application level control model collaborations, the delegate connectors are absent between the interior parts of the deployment level collaborations due to absence of data flow [START_REF] Yu | A MARTE-Based Reactive Model for Data-Parallel Intensive Processing: Transformation toward the Synchronous Model[END_REF]. Given a Gaspard state graph Q and a mode switch component P, the name of an associated collaboration corresponds to a mode value associated with a state of Q and defines the activity of P upon receiving that particular mode value. Figure.9 illustrates a deployed mode switch component and its corresponding collaborations. As a deployed mode switch component relates to only one elementary component; and an application (or architecture) can have several; this requires the creation of several deployed mode switch components being controlled by a single Gaspard state graph. The modified Gaspard state graph is termed as a Deployed Gaspard State Graph. Each of the deployed mode switch components receives the array of mode values and observes its own related mode values: the name of the related collaborations which are defined in the Modes enumeration. If a mode value in the array matches the mode value associated with a deployed mode switch component, it switches to the corresponding mode. However, if there is no match, it remains inactive. Once the deployed Gaspard state graph and the deployed mode switches are constructed, they are placed inside a composition called a Deployed MACRO. This composition is then placed in a repetition component to construct a Deployment Level Mode Automata. An interrepetition dependency and a defaultlink are utilized to make this mode automata equivalent to a synchronous mode automata.

In the special case when an elementary component only has one available IP to be included in all the possible configurations, it has no corresponding deployed mode switch component or DMSC. In that case, the deployed state graph and the constructed DMSCs are placed in a composition called a Deployed Composition. Afterwards, the single IP belonging to the related EC is placed with this composition in the Deployed MACRO component. Subsequently, normal composition of DMA is carried out. A DMSC illustrates a choice related to the different present modes. In case of no alternatives, it is not necessary to create the DMSC. Figure .10 represents the corresponding deployment level mode automata for the example present in Figure .8. As each of the three configurations contain the single IP C1 for the elementary component EC C, this IP will always be present in the final composition of any configuration irrespective of the changes in events and the respective states.

For mode automata at application level, its initial state is given by an application component that has input event ports and an output state port. Initially some events are generated and taken as input by that component in order to produce the initiate state. After that, the application component remains inactive due to the absence of the events arriving on its input ports. However, for deployment level mode automata, no structural information about application level is present except the information related to elementary components. Thus the initial state related to the deployed Gaspard state graph cannot be determined explicitly.

We thus introduce an internal component in the deployment level control model, responsible for relaying the initial state of the deployed Gaspard state graph or DGSG. This component is termed as a InitialStateComponent and contains a single output port of the enumerated States type with a shape value of {1}. This output port provides the user defined initial state of the deployed Gaspard state graph. Once a transition to another state occurs, the interrepetition dependency allows to provide the information about the previous state; and the target state is treated as the source state for the subsequent transition of the deployed Gaspard state graph.

Figure.10: An abstract overview of the Deployment Level Mode Automata

It is also necessary to address the issue related to the arrival of incoming events in the deployment level mode automata shown in Figure .10. In control model at the application level, the events are either produced randomly in the application itself due to an elementary component or taken as input from the external environment (for example user defined stimuli). But for mode automata at deployment level, the incoming events need to be linked directly to this level. At the RTL level, these events are considered as the non deterministic user specified inputs taken by means of a UART interface present in the reconfigurable system. A set of options is given to the user for configuration selection. These options are treated as input events. The user can thus choose among the different modeled configurations, depending upon different QoS criteria such as reconfiguration time and consumed FPGA resources via a Design Space Exploration (DSE) strategy.

To link the user specified inputs, or events, to the deployment level, the mode automata is modeled with n number of event ports, n being the number of possible configurations. Each event port has a shape value of {1,*}. The first dimension of this shape value indicates that only one event value arrives at a particular instant of time, while the second dimension indicates a temporal infinite repetition. These event ports are of the type Boolean. The event values serve to cause a transition in the deployment level mode automata.

The input event ports of the deployment level mode automata are not linked to any higher abstraction level of the application, but via model transformations, at the RTL level, are in fact taken as the input ports (those related to receiving UART signals) of the embedded processor. This processor is actually present in the processor sub system, of the highest hierarchical entity (i.e. top.vhd) of the PDR system. Figure .10 shows a complete overview of the deployment level control model, taking into account all the possible scenarios of elementary components and associated IPs.

C. Integrating event observer at the RTL level

As elaborated in the precedent section, the control events in our scenario are generally non deterministic in nature and depend upon the user input, while data computations are deterministic and arrive in a regular manner as per Gaspard semantics. Hence we need to create regularity between the control/data flows. The notion of an EventObserver is thus introduced at the RTL level in the highest hierarchical PDR system entity. This concept is not introduced at MARTE modeling level in order to distance the designer from event management details. Figure .11 shows an abstract overview of the top level entity of our PDR system. It should be reminded that the reconfigurable region, the corresponding implementations and the reconfigurable controller are generated via high level MARTE modeling.

Figure.11: A global overview of the our PDR system

The EventObserver consumes user inputs arriving at irregular time intervals and produces events at regular time intervals for the deployed Gaspard state graph. This component has input and output event ports EventIn and EventOut respectively, as well as the Clk and Rst ports for clock and reset signals. The EventIn port is connected to the top level UART Rx input port while the EventOut port is connected to the processor's UART Rx input port. An extract of the algorithm related to the EventObserver is presented below using an informal semantic:

- ---------------------- ------------------------The user input may arrive irregularly at any instant of time, where as an event value is need at each instant of time t. The EventObserver listens on its input port, and at each rise of clock, determines if an event is present or not. In the first case, the event is sent to the processor subsystem and in turn the reconfiguration controller. This causes a successful state transition (or a self transition) in the state graph. In the second case, if there is no user driven input event at time t, then the EventObserver generates a default event e d, causing a self transition in the state graph. This value can be viewed as a special value among the set of values corresponding to the all expression: which catches any event not specified in related transitions and causes a self transition in the state graph. If ξ is the set of all possible events and E is the set of events related to the different configurations, The overall relation is then expressed as: E = {e 1, e 2, e 3} , all = {ξ E} ∪ {e d}

The relations between the different events and states (configurations) in a deployed Gaspard state graph present in the controller are shown in Figure .12. A self transition does not switches the current executing configuration, while a transition to a different state causes the controller to switch to the corresponding configuration. While this notion introduces regularity in the arrival of control events, it is possible that a control event and the eventual configuration switch causes a disruption in the data flow of the application implemented as a reconfigurable hardware accelerator. It is thus critical to determine the precise moment to effectively switch a configuration. Our works could benefit from the notion of degree of granularity [START_REF] Labbani | Introducing control in the gaspard2 data-parallel metamodel: Synchronous approach[END_REF] that responds to the synchronization of the control/data flow.

Figure .12: Different representations of deployed Gaspard state graph: events and state relations V. CONCLUSION This paper presents a novel mode automata based control model in a MDE oriented MARTE compliant SoC co-design Gaspard framework. The integration of this control model in the IP deployment level of our framework makes it possible to model aspects of partial dynamic reconfiguration. Regularity between the control/data flow is also introduced at the RTL level. As a perspective, MDE model transformations are currently being developed to enable automatic code generation for final implementation of partial dynamic reconfiguration in a targeted FPGA. This will result in a complete MDE design flow to implement dynamically reconfigurable FPGAs using a high level flow.

Figure. 1 :

 1 Figure.1: An overview of the Gaspard framework

Figure. 2 :

 2 Figure.2: Example of a mode switch containing several modes M 1 , M 2 , ..., M n (i.e. the window tabs) .

Figure. 3 :

 3 Figure.3: Illustration of a state graph in Gaspard

Figure. 4 :

 4 Figure.4: An example of the Gaspard mode automata integrated at the application level in our framework

Figure. 5 :

 5 Figure.5: Deployment of an elementary component

Figure. 7 :

 7 Figure.7: Extended Gaspard deployment level

Figure. 7

 7 illustrates the extended version of the deployment related to a generic elementary component while omitting the enumerations; and Figure.8 represents an abstract overview of the deployment semantics.

Figure. 9 :

 9 Figure.9: The Deployed Mode Switch Component DMSC EC A related to the elementary component EC A

  -Sensitivity List (Clk, Event) if Clk is TRUE and Event then EventOut = Event; else if Clk is TRUE and not Event then EventOut = Default Value; end if

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ENERGY DEPRIVATION TRANSIENTLY ENHANCES RHYTHMIC INHIBITORY EVENTS IN THE CA3 HIPPOCAMPAL NETWORK IN VITRO

2

Abstract-Oxygen glucose deprivation (OGD) leads to rapid suppression of synaptic transmission.

Here we describe an emergence of rhythmic activity at 8 to 20 Hz in the CA3 subfield of hippocampal slice cultures occurring for a few minutes prior to the OGD-induced cessation of evoked responses. These oscillations, dominated by inhibitory events, represent network activity, as they were abolished by tetrodotoxin. They were also completely blocked by the GABAergic antagonist picrotoxin, and strongly reduced by the glutamatergic antagonist NBQX. Applying CPP to block NMDA receptors had no effect and neither did UBP302, an antagonist of GluK1containing kainate receptors. The gap junction blocker mefloquine disrupted rhythmicity.

Simultaneous whole-cell voltage-clamp recordings from neighboring or distant CA3 pyramidal cells revealed strong cross-correlation of the incoming rhythmic activity. Interneurons in the CA3 area received similar correlated activity. Interestingly, oscillations were much less frequently observed in the CA1 area. These data, together with the observation that the recorded activity consists primarily of inhibitory events, suggest that CA3 interneurons are important for generating these oscillations. This transient increase in inhibitory network activity during OGD may represent a mechanism contributing to the lower vulnerability to ischemic insults of the CA3 area as compared to the CA1 area.

Brain ischemia leads to a rapid increase in extracellular glutamate concentration, which triggers neurotoxic cell death mainly through the excessive activation of calcium-permeable NMDA receptors [START_REF] Choi | The role of glutamate neurotoxicity in hypoxic-ischemic neuronal death[END_REF]. Neurons react to the decrease in oxygen and glucose by initiating a series of adaptive responses to mitigate excitotoxicity. Both long term mechanisms involving alterations in gene transcription and cell metabolism [START_REF] Lipton | Ischemic cell death in brain neurons[END_REF], as well as immediate effects on electrophysiological properties [START_REF] Krnjević | Electrophysiology of cerebral ischemia[END_REF] serve to protect neurons, thereby allowing them to recover from ischemic insults of short duration. Among the best characterized of the immediate protective mechanisms are neuronal hyperpolarization [START_REF] Hansen | Anoxia increases potassium conductance in hippocampal nerve cells[END_REF][START_REF] Fujiwara | Effects of hypoxia on rat hippocampal neurones in vitro[END_REF][START_REF] Leblond | Hypoxic changes in hippocampal neurons[END_REF][START_REF] Luhmann | Hypoxia-induced functional alterations in adult rat neocortex[END_REF][START_REF] Jiang | Oxygen deprivation activates an ATP-inhibitable K_ channel in substantia nigra neurons[END_REF][START_REF] Fujimura | Contribution of ATPsensitive potassium channels to hypoxic hyperpolarization in rat hippocampal CA1 neurons in vitro[END_REF][START_REF] Erdemli | Potassium conductance causing hyperpolarization of CA1 hippocampal neurons during hypoxia[END_REF][START_REF] Tanabe | Apamin-sensitive conductance mediates the K_ current response during chemical ischemia in CA3 pyramidal cells[END_REF] and adenosinemediated inhibition of glutamatergic synaptic transmission [START_REF] Fowler | Adenosine antagonists delay hypoxia-induced depression of neuronal activity in hippocampal brain slice[END_REF][START_REF] Zeng | Effects of adenosinergic drugs on hypoxia-induced electrophysiological changes in rat hippocampal slices[END_REF]Gribkoff and Baumann, 1992;Katchmann and Hershkowitz, 1993;[START_REF] Zhu | Adenosine release mediates cyanide-induced suppression of CA1 neuronal activity[END_REF][START_REF] Pearson | Sustained elevation of extracellular adenosine and activation of A1 receptors underlie the post-ischaemic inhibition of neuronal function in rat hippocampus in vitro[END_REF]. The neuronal hyperpolarization enhances magnesium block of the NMDA channel, which impedes calcium influx, thereby reducing excitatory synaptic transmission by inhibiting glutamate release. In addition, it was recently shown that specific classes of neurons that are particularly resistant to ischemic cell death express mechanisms to down-regulate NMDA receptor function [START_REF] Avignone | Differential responses to NMDA receptor activation in rat hippocampal interneurons and pyramidal cells may underlie enhanced pyramidal cell vulnerability[END_REF][START_REF] Gee | NMDA receptors and the differential ischemic vulnerability of hippocampal neurons[END_REF][START_REF] Nakanishi | Neuroprotection by the NR3A subunit of the NMDA receptor[END_REF][START_REF] Zhang | Preconditioning neuroprotection in global cerebral ischemia involves NMDA receptor-mediated ERK-JNK3 crosstalk[END_REF]. Changes in membrane properties and in synaptic function are expected also to modify network activity in neuronal populations. Indeed, ischemia induced an epoch of high frequency discharge recorded from the hippocampus in anesthetized rats just prior to the suppression of synaptic activity [START_REF] Freund | Simultaneous recording of local electrical activity partial oxygen tension and temperature in the rat hippocampus with a chambertype microelectrode. Effects of anaesthesia ischemia and epilepsy[END_REF]. And in an in vitro preparation consisting of the whole hippocampus from neonatal rats, anoxia induced gamma oscillations originating in the CA3 area [START_REF] Dzhala | Neuronal mechanisms of the anoxiainduced network oscillations in the rat hippocampus in vitro[END_REF]. In hippocampal slice cultures older than three weeks, we observed that OGD induces a slower form of network oscillations primarily in the theta range. We characterized the basic properties of these oscillations by recording from individual hippocampal neurons and from cell pairs. We find that the rhythmic responses induced in the hippocampus at the onset of OGD are driven primarily by a powerful activation of the inhibitory network in the CA3 region.

EXPERIMENTAL PROCEDURES

All experiments used protocols approved by the Veterinary Department of the Canton of Zurich. Hippocampal slice cultures were prepared from 6 day old Wistar rats and maintained for 3-6 weeks in vitro using the roller drum technique as described [START_REF] Gähwiler | Organotypic slice cultures of neural tissue[END_REF]. Animals were killed by decapitation. Slice cultures were then transferred to a patch-clamp recording setup and superfused with artificial cerebrospinal fluid (ACSF) equilibrated with 95% O 2 /5% CO 2 containing (in mM): 124 NaCl, 2.5 KCl, 26 NaHCO 3 , 1.25 NaH 2 PO 4 , 10 glucose, 3 CaCl 2 , 2 MgCl 2 , pH 7.4. Neurons were visualized with DIC optics and whole-cell patch-clamp recordings were made with pipettes (2-4 MΩ) containing (in mM): 140 Cs-gluconate, 5 NaCl 10 HEPES, 10 phosphocreatine, 1 mM EGTA, 1 mM MgCl 2 , 0.1% biocytin, pH 7.2-7.4. Neurons were voltageclamped at -50 mV, a potential where the driving force allows visualization of both excitatory postsynaptic currents (EPSCs) and inhibitory postsynaptic currents (IPSCs). Signals were amplified with an Axopatch 200B amplifier (Axon Instruments, Foster City, CA), filtered at 2 kHz, stored and analyzed on a personal computer using pClamp8 or pClamp9 software (Axon Instruments). Series resistance (7-15 MΩ) was monitored regularly. Stimulation (200 μs) was applied with a glass pipette filled with artificial cerebrospinal fluid placed in the stratum lucidum.

OGD was induced by superfusing slice cultures with artificial cerebrospinal fluid equilibrated with 95% N 2 /5% CO2 in which glucose was substituted with 8 mM sucrose plus 2 mM 2deoxyglucose and 3 mM NaN 3 was added. Note that 8 mM sucrose is much below the typical concentrations of 500 mM sucrose, which are used to induce direct osmotic effects on transmission [START_REF] Rosenmund | Definition of the readily releasable pool of vesicles at hippocampal synapses[END_REF].

After recording, slices were fixed 4-12 hours in 4% paraformaldehyde in 0.1 M phosphate buffer pH 7.4. Slices were washed in 0.1 M phosphate buffer then equilibrated overnight with 30% sucrose 12% glycerol quick-frozen in liquid nitrogen cooled isopentane and stored at -80 °C until processed. After thawing, slice cultures were removed from the coverslips, washed in 0.1 M phosphate buffer and permeabilized in 0.1 M phosphate buffer, 0.4% Triton X-100 and 5% heatinactivated horse serum for 24 hours at 4 °C. Slices were then processed with streptavidinconjugated AlexaFluor-488 to reveal biocytin-filled neurons. To visualize the layers of the hippocampus, a primary antibody that recognizes mature neurons (neuronal nuclear antigen, mouse α-NeuN 1:400, Chemicon, CA, USA) was incubated for 24 hours at 4 °C in 0.1 M phosphate-buffered saline with 0.4% Triton X-100 and 2.5% heat-inactivated horse serum. Cultures were washed with 0.1 M PB 0.4% Triton X-100 (4 x 30 min) and incubated at 4 °C overnight with donkey α-mouse Alexa 546 (1:350, Molecular Probes) in 0.1 M PB, 0.4% Triton X-100 and 2.5% heat-inactivated horse serum. After extensive washing sections were air-dried, mounted with Fluorostab mounting medium (Bioscience Products AG, Switzerland), and stored in the dark at 4 °C. Image stacks were collected with a Leica SP2 confocal microscope and interneurons were identified by their morphology and an absence of dendritic spines.

Chemicals

Concentrated stock solutions of drugs were prepared in distilled water or dimethylsulfoxide as appropriate, stored at -20 o C in single-use aliquots, thawed and diluted in ACSF immediately before use. Picrotoxin and mefloquine were purchased from Sigma. NBQX, SYM2206, UBP302, DHPG, YM298198, and MPEP were purchased from Tocris (Bristol, UK). CPP was kindly provided by Novartis AG (Basel, Switzerland). Tetrodotoxin was purchased from Ascent Scientific (Bristol, UK).

Analysis

Two types of analyses were performed to describe the activity induced by OGD. In the first instance autocorrelation functions were used to classify the activity as rhythmic or not. When two cells were recorded simultaneously, cross-correlation functions were also calculated alongside the autocorrelations. Records were examined by eye and the autocorrelation and/or cross-correlation functions were calculated using Clampfit for the 3-4 most rhythmic-appearing 0.6 -2 s long segments during each condition and averaged. The period was calculated from the time of the second peak in the autocorrelation function (period). When the second peak of the autocorrelation function was at least 0.3 and several regularly spaced peaks appeared, the segment was classified as 'rhythmic'. The overall activity during a given condition was classified as rhythmic when at least 3 rhythmic segments could be identified during the recording. While performing this analysis we observed that the frequency of the activity typically increased initially and would gradually change during the recording. We therefore used a second method of analysis to show the time course of the change in frequency of IPSCs. This approach involved first a pre-filtering of the postsynaptic current responses followed by a classification of the detected transients into IPSCs versus EPSCs based on their shape (polarity, rise, and decay time). The first step consisted of a detrend operation to remove very slow fluctuations that can contaminate the baseline of the signals. This procedure was performed with a moving average filter (duration of the sliding window: 0.25 s). Non-IPSC events were then removed using a threshold operation in which signals of negative polarity are set to zero, and the amplitude of the remaining signals was squared to increase the signal-to-noise ratio. The squared signal was compared to a threshold . To minimize false negatives and false positives, an algorithm was employed that allowed the user to easily adjust the  threshold and to immediately view the generated graphed data. Thus, the  threshold was adjusted manually and data output was reviewed visually. The frequency of the IPSPs was then plotted against time using a sliding window with 1 second duration.

Statistical analysis

Data are presented as mean percentage of control ± SEM. Paired Student's t-tests were used to compare responses under the various conditions.

RESULTS

All experiments were performed with roller-drum hippocampal slice cultures where synaptic connectivity approaches that found in vivo [START_REF] Zimmer | Cellular and connective organization of slice cultures of the rat hippocampus and fascia dentata[END_REF]Frotscher and Gähwiler, 1998). Thus, broad band oscillatory activity is much more readily induced in this preparation [START_REF] Fischer | Simultaneous activation of gamma and theta network oscillations in rat hippocampal slice cultures[END_REF] than in acute hippocampal slices in which rhythmicity is usually restricted to a single band and requires application of very high concentrations of agonists [START_REF] Konopacki | Carbachol-induced EEG 'theta' activity in hippocampal brain slices[END_REF][START_REF] Macvicar | Local neuronal circuitry underlying cholinergic rhythmical slow activity in CA3 area of rat hippocampal slices[END_REF][START_REF] Williams | Properties of carbachol-induced oscillatory activity in rat hippocampus[END_REF][START_REF] Fisahn | Cholinergic induction of network oscillations at 40 Hz in the hippocampus in vitro[END_REF][START_REF] Gillies | A model of atropine-resistant theta oscillations in rat hippocampal area CA1[END_REF]. More recent work, however, has shown that network oscillations are better maintained in acute slices if an adequate oxygen supply is ensured by increasing the perfusion [START_REF] Wu | Size does matter: generation of intrinsic network rhythms in thick mouse hippocampal slices[END_REF], Hajos et al., 2009).

When pyramidal neurons in the CA3 region of the hippocampus were voltage-clamped at -50mV, stimulation of the stratum lucidum or dentate gyrus evoked mixed EPSC/IPSC responses dominated by IPSCs (Fig. 1A, a-d; [START_REF] Brown | Voltage-clamp analysis of mossy fiber synaptic input to hippocampal neurons[END_REF][START_REF] Lawrence | Interneuron diversity series: containing the detonationfeedforward inhibition in the CA3 hippocampus[END_REF]McBain, 2003, Mori et al., 2004). OGD gradually decreased evoked responses until they were completely blocked. Coincident with the depression of evoked responses, OGD induced rhythmic activity dominated by IPSCs in 65 of 69 CA3 PCs, which ceased when evoked responses were blocked (Fig. 1A). Hypoxia alone failed to induce oscillations. This activity was abolished by tetrodotoxin (0.5 µM) indicating that it was driven by action potential-dependent synaptic inputs from presynaptic neurons rather than reflecting intrinsic membrane oscillations (n = 4, Fig. 1B). Once initiated, the frequency of IPSCs recorded from CA3 pyramidal neurons remained relatively constant, until all activity stopped (ACF 0.382 ± 0.012, period 111.8 ± 4.5 ms, n = 69 Fig. 1C).

Spontaneous activity returned with some delay when normal oxygenated aCSF was perfused beginning immediately after the cessation of activity i.e. after 3-5 minutes. The outward potassium current initiated by OGD was not examined here, as patch pipettes were filled with a cesium-based recording solution.

Recording from pairs of CA3 neurons revealed that the activity induced by OGD was synchronous across the CA3 area (Fig. 2), and activity recorded from pairs of CA3 pyramidal neurons became highly correlated (CCF: 0.689 ± 0.055, n = 8, Fig. 2A). Recordings from 12 of 16 interneurons in the CA3 area also revealed rhythmic activity during OGD (ACF: 0.331 ± 0.025, period 112.4 ± 5.1 ms, n = 16) that was highly correlated with the activity recorded from other CA3 interneurons in 3 of 6 pairs (CCF 0.455 ± 0.090, n = 6) or the activity recorded from CA3 pyramidal neurons in 3 of 4 pairs (CCF 0.572 ± 0.075, n = 4, Fig. 2B). This finding strongly suggests that OGD-induced network activity occurs throughout the CA3 region of the slice cultures. When CA3 interneurons were recorded in current clamp mode, action potentials were not observed during the control period. OGD, however, led to a transient depolarization associated with action potential discharge (Fig. 2B) in 6 out of 7 cells (mean action potential frequency: 5.4 ± 1.8 Hz, peak frequency: 48.3 ± 17.2 Hz), consistent with the increase in IPSP frequency observed in CA3 cells during OGD.

In contrast, in recordings made from the CA1 region, rhythmicity was usually not present in the activity induced by OGD (Fig. 2C, ACF 0.247 ± 0.066, n = 10). Furthermore, when recordings were obtained simultaneously from a CA1 and a CA3 pyramidal cell, rhythmic activity was always seen in the CA3 cell but only in 2 of 7 CA1 pyramidal cells. OGD also induced transient rhythmic activity in only 2 of 6 neurons located in the dentate gyrus (not shown, ACF 0.209 ± 0.061, period 107.6 ± 13.6 ms, n = 6). These findings suggest that the activity originates in the CA3 neuronal network.

The OGD-induced activity absolutely required the activity of GABAergic interneurons as it was abolished in the presence of picrotoxin (300 µM) (Fig 3A). Antagonists of glutamate receptors, however, had diverse effects on the OGD-induced activity depending on the targeted subtype.

The AMPA/kainate receptor antagonist NBQX (20 µM), applied 2 minutes prior to OGD, dramatically reduced inward (excitatory) and outward (inhibitory) events. However, very small rhythmic currents remained in 2 of 4 neurons (Fig 3B ,ACF 12.9 ms, n = 5). The specific AMPA receptor antagonists GYKI 54266 (30 µM) and SYM 2206 (20 µM) altered the OGD-induced activity from continuous rhythmic outward currents to very short (< 1 second) stretches of rhythmic activity (GYKI ACF 0.314 ± 0.033, period 106.9 ± 10.9 ms, n = 5; SYM ACF 0.456 ± 0.060, period 187.3 ± 33.9 ms, n = 6) interspersed with very large amplitude bursts (Fig. 3D). The GluK1-specific kainate receptor antagonist UBP302 (1 µM) did not appreciably alter the OGD-induced oscillations (ACF 0.502 ± 0.046, period 100.1 ± 17.5 ms, n = 4) or at 5-10 µM (Fig. 3E, ACF 0.431 ± 0.052, period 128.9 ± 14.3 ms, n = 4).

Gap junctions between hippocampal interneurons [START_REF] Meyer | In vivo labeling of parvalbuminpositive interneurons and analysis of electrical coupling in identified neurons[END_REF][START_REF] Traub | Contrasting roles of axonal (pyramidal cell) and dendritic (interneuron) electrical coupling in the generation of neuronal network oscillations[END_REF][START_REF] Price | Neurogliaform neurons form a novel inhibitory network in the hippocampal CA1 area[END_REF][START_REF] Zsiros | Electrical coupling between interneurons with different excitable properties in the stratum lacunosummoleculareof the juvenile CA1 rat hippocampus[END_REF] and probably also between hippocampal pyramidal cells [START_REF] Schmitz | Axoaxonal coupling a novel mechanism for ultrafast neuronal communication[END_REF][START_REF] Traub | Contrasting roles of axonal (pyramidal cell) and dendritic (interneuron) electrical coupling in the generation of neuronal network oscillations[END_REF][START_REF] Mercer | Electrical coupling between pyramidal cells in adult cortical regions[END_REF] play a significant role in oscillatory behavior. We therefore tested the effects of a 20 minute application of mefloquine (25 µM), which blocks a number of connexins [START_REF] Cruikshank | Potent block of Cx36 and Cx50 gap junction channels by mefloquine[END_REF][START_REF] Yamamoto | Blockade by mefloquine of intercellular electrical coupling between vascular endothelial cells in the guinea-pig mesenteric arteries[END_REF] found in neuronal gap junctions. In the presence of mefloquine there was an increase in burst-like activity in CA3 neurons that for short segments (maximum about 0.6 seconds) was quite rhythmic in 2 of 4 neurons (ACF 0.304 ± 0.063, period 90.1 ± 15.6 ms, n = 4). OGD further increased the activity (Fig 3F,ACF 0.348 ± 0.027,period 73.3 ± 17.2 ms, n = 3), which again had primarily very short rhythmic segments.

The increase in extracellular glutamate concentrations associated with ischemia may enhance the activation of Group I metabotropic glutamate receptors (mGluRs), which are localized peri-or extra-synaptically (Lujan et al., 1996), and whose activation can induce synchronized oscillations [START_REF] Whittington | Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation[END_REF]. Indeed, we observed that the group I metabotropic glutamate receptor agonist (S)-DHPG at low concentrations (~100 nM) induced oscillations in the CA3 region of the slice cultures that were qualitatively similar to those observed with OGD (Fig. 3G 1 , ACF 0.433 ± 0.030, period 126.4 ± 11.4 ms, n = 8). OGD induces significant rises in extracellular glutamate [START_REF] Jabaudon | Acute decrease in net glutamate uptake during energy deprivation[END_REF][START_REF] Rossi | Glutamate release in severe brain ischaemia is mainly by reversed uptake[END_REF] which may therefore induce oscillations by acting on group I mGluRs. The non-competitive group I mGluR antagonists YM289198 (2 µM) and MPEP (5 µM) blocked the oscillations induced by DHPG (Fig. 3G 2 ACF 0.177 ± 0.023, n = 6). The same cocktail did not, however, block the OGD-induced oscillations (Fig. 3G 3 ACF 0.488 ± 0.044, period 100.6 ± 12.0 ms, n = 6). Thus, activation of mGluRs is sufficient to induce oscillations in slice cultures but is not necessary for the oscillations evoked by OGD.

DISCUSSION

This investigation performed with organotypic slice cultures shows that energy deprivation in the hippocampus results in a strong but transient increase in synaptic inhibitory activity in the CA3 area. The much lower prevalence of rhythmic inhibitory activity in CA1 pyramidal cells is likely due to the functional uncoupling of inhibitory interneurons from the CA1 network by anoxia [START_REF] Khazipov | Hippocampal inhibitory interneurons are functionally disconnected from excitatory inputs by anoxia[END_REF][START_REF] Congar | Direct demonstration of functional disconnection by anoxia of inhibitory interneurons from excitatory inputs in rat hippocampus[END_REF], and may therefore contribute to the greater susceptibility of CA1 pyramidal cells to excitotoxic cell death. The frequency of the network IPSCs in the CA3 area was between 8 and 20 Hz, a range which is somewhat higher than theta oscillations recorded from rodent in vivo (4 -12 Hz; [START_REF] Bland | The physiology and pharmacology of hippocampal formation theta rhythms[END_REF], possibly as a consequence of OGD. Simultaneous recordings from CA3 pyramidal cell pairs as well as from interneuron pairs showed that OGD led to highly correlated discharge of IPSPs, which was not apparent during control periods. This result suggests that individual interneurons impose rhythmicity on large numbers of neighboring cells, as has been demonstrated in the CA1 subfield [START_REF] Cobb | Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons[END_REF].

The extreme ramification of basket cell axons (Fig. 2B) is consistent with such an entrainment function. Synchronization may also involve electrical coupling among neurons [START_REF] Traub | Cellular mechanisms of neuronal population oscillations in the hippocampus in vitro[END_REF]. Numerous studies have shown electrical coupling among interneurons in the CA1 subfield [START_REF] Zhang | Electrotonic coupling between stratum oriens interneurones in the intact in vitro mouse juvenile hippocampus[END_REF][START_REF] Price | Neurogliaform neurons form a novel inhibitory network in the hippocampal CA1 area[END_REF][START_REF] Zsiros | Electrical coupling between interneurons with different excitable properties in the stratum lacunosummoleculareof the juvenile CA1 rat hippocampus[END_REF][START_REF] Zsiros | Propagation of postsynaptic currents and potentials via gap junctions in GABAergic networks of the rat hippocampus[END_REF]. Our data suggest that this may also be the case in the CA3 subfield. Mefloquine, at a concentration that blocks gap junctions, clearly disrupted the OGD-induced rhythmic discharge of IPSPs in CA3 pyramidal cells. The origin of the brief burst of IPSCs is presently unknown and probably reflects a gap junction-independent effect of mefloquine [START_REF] Zhou | Mefloquine enhances nigral gamma-aminobutyric acid release via inhibition of cholinesterase[END_REF]. Interestingly, OGD did not readily induce oscillations in CA1 pyramidal cells, and furthermore, the oscillations we observed in CA3 neurons were not propagated to CA1. This is in contrast to normal in vivo conditions, where the CA3 region represents a major input for theta rhythmicity in CA1 cells (reviewed in Buzsaki, 2002). Presumably, it is the breakdown in ionic gradients during OGD that leads to failure in axonal conduction and/or synaptic transmission of Schaffer collaterals.

The emergence of hippocampal oscillations largely reflects intrinsic properties of the synaptic circuitry, as shown in vivo [START_REF] Bragin | Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat[END_REF] and in vitro [START_REF] Fischer | Activation of intrinsic hippocampal theta oscillations by acetylcholine in rat septo-hippocampal cocultures[END_REF][START_REF] Gillies | A model of atropine-resistant theta oscillations in rat hippocampal area CA1[END_REF][START_REF] Wu | Size does matter: generation of intrinsic network rhythms in thick mouse hippocampal slices[END_REF]. Furthermore, the theta range of rhythmic activity matches the resonant frequency of hippocampal principal cells [START_REF] Leung | Theta-frequency resonance in hippocampal CA1 neurons in vitro demonstrated by sinusoidal current injection[END_REF]. A trigger is, however, necessary to bias the excitability of the network to shift into the oscillatory mode. Both an atropinesensitive form initiated by cholinergic inputs arising in the septum, as well as an atropineinsensitive form mediated by as yet unidentified transmitters, and typical for awake animals, can be distinguished (reviewed in Buzsaki, 2002). The oscillations observed here in hippocampal slice cultures correspond to the latter form, as this preparation lacks cholinergic inputs [START_REF] Gähwiler | Guidance of acetylcholinesterase-containing fibres by target tissue in co-cultured brain slices[END_REF][START_REF] Gähwiler | Development of the septohippocampal projection in vitro[END_REF]. Energy deprivation rapidly reverses the operational direction of glutamate transporters [START_REF] Jabaudon | Acute decrease in net glutamate uptake during energy deprivation[END_REF][START_REF] Rossi | Glutamate release in severe brain ischaemia is mainly by reversed uptake[END_REF] leading to accumulation of extracellular glutamate. Thus, we hypothesized that glutamate serves to trigger oscillatory activity initiated by OGD. We then attempted to determine which class of glutamate receptors is mediating the OGD-induced oscillations. We first blocked AMPA receptors, which greatly reduced oscillations, as described previously [START_REF] Macvicar | Local neuronal circuitry underlying cholinergic rhythmical slow activity in CA3 area of rat hippocampal slices[END_REF][START_REF] Williams | Properties of carbachol-induced oscillatory activity in rat hippocampus[END_REF][START_REF] Gillies | A model of atropine-resistant theta oscillations in rat hippocampal area CA1[END_REF]. As with the suppression of rhythmicity with tetrodotoxin, this finding is likely to reflect the requirement of an intact network rather than identifying AMPA receptors as the generators of theta. NMDA receptors have been implicated in the transmission of theta activity from the entorhinal cortex to the CA1 subfield in in vivo experiments (reviewed in Buzsaki, 2002). Furthermore, blocking NMDA receptors inhibited the propagation of oscillations from the CA3 to the CA1 subfield in slices [START_REF] Williams | Properties of carbachol-induced oscillatory activity in rat hippocampus[END_REF]. However, we observed no effect on oscillations in the CA3 subfield after NMDA receptor blockade, in keeping with earlier studies [START_REF] Macvicar | Local neuronal circuitry underlying cholinergic rhythmical slow activity in CA3 area of rat hippocampal slices[END_REF][START_REF] Williams | Properties of carbachol-induced oscillatory activity in rat hippocampus[END_REF]. Blocking GluK1 subunitcontaining kainate receptors also had no effect on rhythmicity. Oscillations in the theta range can be induced in the CA3 subfield by activating mGluRs (Fig. 3G this study; [START_REF] Cobb | Coincident activation of mGluRs and mAChRs imposes theta frequency patterning on synchronized network activity in the hippocampal CA3 region[END_REF]2003), although at higher concentrations mGluR agonists block theta rhythms because of endocannabinoid release [START_REF] Reich | Regulation of IPSP theta rhythm by muscarinic receptors and endocannabinoids in hippocampus[END_REF]. When we blocked postsynaptic mGluRs with noncompetitive antagonists, OGD-induced rhythmic activity was not reduced. Together, these data strongly suggest that activation of mGluRs alone does not trigger the oscillations observed with OGD. However, high concentrations of extracellular glutamate are likely to have an indirect action by inducing neurons or glia to release as yet unidentified neurotransmitters or neuropeptides that drive network oscillations. Thus, even though neurons initially hyperpolarize in response to energy deprivation [START_REF] Krnjević | Electrophysiology of cerebral ischemia[END_REF], we propose that a sufficient number of interneurons are transiently activated through glutamate-induced transmitter release to generate a transient phase of oscillatory activity. Previous studies in vivo [START_REF] Freund | Simultaneous recording of local electrical activity partial oxygen tension and temperature in the rat hippocampus with a chambertype microelectrode. Effects of anaesthesia ischemia and epilepsy[END_REF] and in the whole hippocampus of neonatal rats [START_REF] Dzhala | Neuronal mechanisms of the anoxiainduced network oscillations in the rat hippocampus in vitro[END_REF] reported that energy deprivation induced a transient burst of gamma oscillations, whereas in our study in slice cultures we observed oscillations at lower frequencies close to the theta range. As pointed out by Dzhala and colleagues (2001), the neonatal tissue they used may not support theta oscillations, which arise after day 8 in rodents [START_REF] Leblanc | Developmental aspects of hippocampal electrical activity and motor behavior in the rat[END_REF][START_REF] Leinekugel | Correlated bursts of activity in the neonatal hippocampus in vivo[END_REF]2002, but see Karlsson and[START_REF] Karlsson | Hippocampal theta in the newborn rat is revealed under conditions that promote REM sleep[END_REF]. Furthermore, it has been proposed that high levels of gamma oscillation may interfere with theta activity [START_REF] Traub | Cellular mechanisms of neuronal population oscillations in the hippocampus in vitro[END_REF]. Thus, slight shifts in network conditions may favor theta oscillations, or gamma oscillations may be more sensitive to anoxia [START_REF] Huchzermeyer | Gamma oscillations and spontaneous network activity in the hippocampus are highly sensitive to decreases in pO2 and concomitant changes in mitochondrial redox state[END_REF] than theta oscillations. Network properties will also differ among various in vitro preparations, which will make it necessary to test further these mechanisms in the in vivo situation.

In conclusion, this study in organotypic slice cultures shows that energy deprivation induces a transient increase in inhibitory network activity mainly in the CA3 region, which may contribute to the decreased susceptibility to ischemia of CA3 as compared to CA1 pyramidal cells.

  0.295 ± 0.035, period 140.4 ± 16.6 ms, n = 4). OGD-induced rhythmic activity persisted in 5 of 5 CA3 neurons in the presence of the specific NMDA receptor antagonist CPPene (20 µM) (Fig 3C ACF 0.411 ± 0.039, period 113.4 ±

Fig. 1 .

 1 Fig. 1. Effects of OGD in the CA3 region of organotypic slice cultures. A) Continuous voltage-

Fig. 2 .

 2 Fig. 2. Rhythmic activity induced by OGD occurs across the CA3 region. A) Paired voltage-

Fig. 3 .

 3 Fig. 3. OGD-induced oscillations require intact inhibitory transmission and are highly sensitive to

Acknowledgments-We thank D. Göckeritz-Dujmovic, Lubka Spassova, Hansjörg Kasper, S. Giger and R Schöb for excellent technical assistance. We are grateful to B. Gähwiler for suggestions and valuable discussions. This work was funded by the Swiss National Science Foundation.


Sandie Tuduri

Laure Crabbe

Hélène Tourrière

Arnaud Coquelle
email: arnaud.coquelle@inserm.fr

Philippe Pasero
email: philippe.pasero@igh.cnrs.fr

Does interference between replication and transcription contribute to genomic instability in cancer cells?

Keywords:

We have recently reported that topoisomerase 1 (Top1) cooperates with ASF/SF2, a splicing factor of the SR family, to prevent unscheduled replication fork arrest and genomic instability in human cells. Our results suggest that Top1 execute this function by suppressing the formation of DNA-RNA hybrids during transcription, these so-called R-loops interfering with the progression of replication forks. Using ChIP-chip, we have shown that γ-H2AX, a marker of DNA damage, accumulates at gene-rich regions of the genome in Top1-deficient cells. This is best illustrated at histone genes, which are highly expressed during S phase and display discrete γ-H2AX peaks on ChIP-chip profiles. Here, we show that these γ-H2AX domains are different from those induced by camptothecin, a Top1 inhibitor inducing double-strand DNA breaks throughout the genome. These data support the view that R-loops promote genomic instability at specific sites by blocking fork progression and inducing chromosome breaks.

Whether this type of transcription-dependent fork arrest contributes to the replication stress observed in precancerous lesions is an important question that deserves further attention.

Introduction

Genomic instability is a hallmark of cancer cells. It is most often manifested in chromosomal instability, which refers to changes in chromosome structure and number [START_REF] Venkitaraman | Chromosomal instability in cancer: causality and interdependence[END_REF][START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . A growing body of evidence indicates that genomic instability plays an active role in tumorigenesis by providing the genetic alterations that underlie the multistep process of cancer development [START_REF] Lengauer | Genetic instabilities in human cancers[END_REF][START_REF] Murga | Genomic instability: on the birth and death of cancer[END_REF] .

It has been recently reported that DNA damage arises in precancerous lesions as a consequence of aberrant oncogene activation. This would induce a constitutive DNA damage response that would select for p53 inactivation and would allow pretumoral cells to bypass anti-cancer barriers such as apoptosis and senescence [START_REF] Halazonetis | An Oncogene-Induced DNA Damage Model for Cancer Development[END_REF] . Replication stress has been proposed to be at the origin of spontaneous DNA damage in precancerous lesions [START_REF] Marusyk | Replicational stress selects for p53 mutation[END_REF] . However, the links between oncogene activation, replication defects and genomic instability remain poorly understood.

Replication forks frequently stall when they encounter DNA lesions or natural pause sites [START_REF] Tourriere | Maintenance of fork integrity at damaged DNA and natural pause sites[END_REF] .

Arrested forks are fragile structures that are prone to breakage and unscheduled recombination. Studies in model systems such as budding yeast have shown that transcription represents a prominent source of replication fork arrest and genomic instability, especially when DNA and RNA polymerases converge [START_REF] Deshpande | DNA replication fork pause sites dependent on transcription[END_REF][START_REF] Azvolinsky | Highly transcribed RNA polymerase II genes are impediments to replication fork progression in Saccharomyces cerevisiae[END_REF][START_REF] Prado | Impairment of replication fork progression mediates RNA polII transcription-associated recombination[END_REF][START_REF] Takeuchi | Transcription-dependent recombination and the role of fork collision in yeast rDNA[END_REF] . It is now well established that this instability is mainly caused by RNA-DNA hybrids (R-loops) that form when assembly of mRNAparticle complexes (mRNPs) is perturbed [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF][START_REF] Aguilera | The connection between transcription and genomic instability[END_REF] . Similar transcription-associated recombination (TAR) processes are also believed to occur in human cells [START_REF] Gottipati | Transcription-Associated Recombination Is Dependent on Replication in Mammalian Cells[END_REF] but their incidence in cancer development remains largely unexplored. In a recent report, we have shown that DNA topoisomerase I (Top1) suppresses interference between replication and transcription in mouse and in human cells [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . The mechanism by which Top1 plays this crucial role, the nature of the DNA lesions induced by TAR and their potential implication in the cancer process are discussed in this review.

Top1 regulates fork progression and pausing

Top1 is a ubiquitous enzyme that acts at the interface between replication, transcription and RNA maturation. Besides its well-characterized DNA relaxation activity [START_REF] Pommier | Topoisomerase I inhibitors: camptothecins and beyond[END_REF] , metazoan Top1 contains a protein kinase domain that has been implicated in the phosphorylation of splicing factors of the SR family [START_REF] Rossi | Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I[END_REF][START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF] . These activities qualify Top1 as a potential regulator of TAR. However, although Top1 prevents genomic instability in human cells [START_REF] Miao | Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses[END_REF] , its implication in the coordination between replication and transcription was never directly addressed.

Using a single-molecule approach, we have recently monitored the progression of individual replication forks in human and murine cells expressing low to undetectable levels of Top1 14 .

These include human colon carcinoma HCT116 cells stably transfected with a shRNA directed against Top1 18 and Top1-deficient murine B lymphoma-derived cells [START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF] .

Asynchronous cultures were pulse-labeled with BrdU and individual DNA fibers were stretched on silanized coverslips by DNA combing [START_REF] Michalet | Dynamic molecular combing: stretching the whole human genome for high-resolution studies[END_REF][START_REF] Tuduri | Defining replication origin efficiency using DNA fiber assays[END_REF] . This analysis revealed that forks are ~50% slower in Top1-deficient cells, which confirmed that topoisomerase 1 is required for normal fork progression. Interestingly, we also observed a concomitant increase in origin firing in Top1-deficient cells, indicating that cells compensate for slow forks by activating dormant origins [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] .

To determine whether forks pause or stall more frequently in the absence of Top1, control and Top1-deficient cells were labeled with successive pulses of IdU and CldU and progression of sister replication forks was analyzed by DNA combing. In control cells, sister forks progressing from a given origin move at a constant rate and generate symmetrical patterns of IdU and CldU incorporation. In contrast, we detected a 3-fold increase of the frequency of asymmetrical patterns in Top1-cells, indicating that ~25% of the forks pause or stall in the absence of Top1 [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . This increased rate of fork stalling accounts for the higher genomic instability of Top1-deficient cells and correlates with the accumulation of γ-H2AX foci in S phase, which are indicative of DNA damage [START_REF] Rogakou | Megabase chromatin domains involved in DNA double-strand breaks in vivo[END_REF] .

Top1 acts with splicing factors to prevent genomic instability in S phase

In contrast to higher eukaryotes, deletion of Top1 is not lethal in S. cerevisiae and does not significantly affect S-phase progression [START_REF] Bermejo | Top1-and Top2mediated topological transitions at replication forks ensure fork progression and stability and prevent DNA damage checkpoint activation[END_REF] . Using DNA combing, we found that yeast top1Δ cells also displayed slow forks and increased initiation rate as their mammalian counterparts [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . However, yeast top1Δ mutants do not show increased fork stalling or genomic instability.

These data suggest that a non-canonical function of Top1 that is not conserved in yeast prevents unscheduled fork arrest in mammalian cells. One attractive candidate for this function is the elusive kinase activity of Top1, which is found only in metazoa [START_REF] Rossi | Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I[END_REF] . In higher eukaryotes, Top1 phosphorylates the alternative splicing factor ASF/SF2 to promote the formation of messenger ribonucleoprotein particles (mRNPs) and the maturation of nascent RNAs [START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF] . Since mRNP biogenesis prevents the formation of R-loops [START_REF] Li | Inactivation of the SR Protein Splicing Factor ASF/SF2 Results in Genomic Instability[END_REF] and since R-loops are toxic for replication forks [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] , we reasoned that fork arrest in mammalian Top1-cells could reflect interference between replication and transcription.

In agreement with this model, we found that chemical inhibition of Top1 kinase with Diospyrin [START_REF] Hazra | Separation methods of quinonoid constituents of plants used in Oriental traditional medicines[END_REF] or depletion of the alternative splicing factor ASF/SF2 by RNA interference mimic the phenotype of Top1-deficient cells (Fig. 1). Conversely, inhibition of transcription elongation with Cordycepin or degradation of R-loops with RNase H suppressed replication defects and DNA damage in Top1-deficient cells (Fig. 1). Together, these data indicate that Top1 promotes the maintenance of genome integrity during S phase by preventing the formation of R-loops in an ASF/SF2-dependent manner [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . This is reminiscent of the function of the THO/TREX complex, which suppresses TAR in budding yeast by facilitating mRNP biogenesis during transcription [START_REF] Aguilera | The connection between transcription and genomic instability[END_REF] . Interestingly, a recent genome-wide siRNA screen performed by the Cimprich lab also identified several RNA processing factors as suppressors of genomic instability [START_REF] Paulsen | A genome-wide siRNA screen reveals diverse cellular processes and pathways that mediate genome stability[END_REF] . Together with earlier work from Manley and colleagues [START_REF] Li | Inactivation of the SR Protein Splicing Factor ASF/SF2 Results in Genomic Instability[END_REF] , these data suggest the existence of a relationship between defective mRNP biogenesis, replication fork arrest and genomic instability.

Active genes are enriched in γ-H2AX in the absence of Top1

Top1-deficient human and murine cells accumulate DNA breaks and γ-H2AX foci in S phase.

To test whether these breaks occur at specific sites in the genome, we monitored the distribution of this phosphorylated histone variant by ChIP-chip [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . We measured a 3-fold increase in the overall number of loci enriched in γ-H2AX in Top1-deficient cells relative to control cells. Statistical analysis also revealed a strong correlation between the distribution of γ-H2AX domains and the position of active genes in Top1-deficient cells. This is best illustrated with the 64 replication-dependent histone genes, which are highly transcribed during S phase and accumulate γ-H2AX proportionally to their level of expression [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . These data support the view that interference between replication and transcription increases in the absence of Top1 and induces DNA damage.

It has been reported that double-strand DNA breaks (DSBs) induce the formation of large γ-H2AX domains, encompassing several megabases in length [START_REF] Rogakou | Megabase chromatin domains involved in DNA double-strand breaks in vivo[END_REF][START_REF] Stucki | GammaH2AX and MDC1: anchoring the DNA-damage-response machinery to broken chromosomes[END_REF] . In contrast, the γ-H2AXenriched regions observed in Top1-deficient cells were several orders of magnitude smaller than those found at DSBs 14 . These small domains are reminiscent of replication pause sites in budding yeast (our unpublished results and [START_REF] Szilard | Systematic identification of fragile sites via genomewide location analysis of [gamma]-H2AX[END_REF] ). We therefore propose that the γ-H2AX regions observed in Top1-deficient cells correspond to transient DSBs that are rapidly repaired or to stalled replication forks inducing a localized phosphorylation of H2AX by the ATR kinase.

Although we cannot rule out the possibility that genomic instability in Top1-deficient cells comes from persistent DSBs, we also believe that rearrangements also arise at stalled forks without formation of DSBs, as it was shown to be the case in other systems [START_REF] Lee | A DNA Replication Mechanism for Generating Nonrecurrent Rearrangements Associated with Genomic Disorders[END_REF][START_REF] Payen | Segmental Duplications Arise from Pol32-Dependent Repair of Broken Forks through Two Alternative Replication-Based Mechanisms[END_REF][START_REF] Hastings | A microhomology-mediated break-induced replication model for the origin of human copy number variation[END_REF][START_REF] Paek | Fusion of nearby inverted repeats by a replication-based mechanism leads to formation of dicentric and acentric chromosomes that cause genome instability in budding yeast[END_REF][START_REF] Mizuno | Nearby inverted repeats fuse to generate acentric and dicentric palindromic chromosomes by a replication template exchange mechanism[END_REF] .

To further characterize the nature of γ-H2AX foci that accumulate in the absence of Top1, we exposed shTop1 and shCtrl cells to sublethal doses of camptothecin (CPT), a Top1 inhibitor that induces DSBs during DNA replication by blocking replication forks and the distribution of γ-H2AX-enriched regions was mapped by ChIP-chip on chromosome 1 and 6 as described previously [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . As expected, CPT exposure induced a sharp accumulation of γ-H2AX in control cells, and to a lesser extent in shTop1 cells (Fig. 2B). However, this treatment did not significantly increase the number of localized γ-H2AX domains in control cells (Fig. 2A,C), indicating that CPT-induced DSBs are randomly distributed throughout the genome. In contrast, CPT exposure induced a four-fold increase of γ-H2AX-enriched regions in shTop1 cells (Fig. 2A,C). We assume that this increase is due to further depletion of the residual Top1 pool in shTop1 cells.

Interestingly, although γ-H2AX was further enriched at active genes in shTop1 cells upon CPT treatment (Fig 2D ), it was no longer detected at histone genes (Fig. 2E and3). The expression of replication-dependent histone genes is temporally and functionally linked to DNA replication by transcriptional and posttranscriptional mechanisms [START_REF] Marzluff | The human and mouse replicationdependent histone genes[END_REF][START_REF] Holmes | Coordinate Control and Selective Expression of the Full Complement of Replication-dependent Histone H4 Genes in Normal and Cancer Cells[END_REF] . It is also rapidly downregulated in response to DNA damage [START_REF] Su | DNA damage induces downregulation of histone gene expression through the G1 checkpoint pathway[END_REF] , which would explain why γ-H2AX levels drop at histone genes while they rise at neighboring non-histone genes (Fig. 3A). Together with our previous results [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] , these data strongly support the view that replication fork stalling in Top1deficient cells depends on gene expression.

Unsolved issues and future directions

The mechanism by which Top1 and ASF/SF2 suppress replication stress at active genes is currently unclear. Although the kinase activity of Top1 is required to prevent fork stalling, our data indicate that the DNA relaxation activity of Top1 is also involved in this process [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . It has been recently reported that Top1 suppresses class switch recombination (CSR) at immunoglobulin genes [START_REF] Kobayashi | AID-induced decrease in topoisomerase 1 induces DNA structural alteration and DNA cleavage for class switch recombination[END_REF] . CSR is induced by transcription and requires the formation of Rloops [START_REF] Roy | Mechanism of R-loop formation at immunoglobulin class switch sequences[END_REF] . Induction of DSBs during CSR also depends on AID, an enzyme that downregulates Top1 expression [START_REF] Kobayashi | AID-induced decrease in topoisomerase 1 induces DNA structural alteration and DNA cleavage for class switch recombination[END_REF] . Interestingly, AID is induced by DNA damage and is implicated in translocations at non-immunoglobulin genes [START_REF] Lin | Nuclear receptor-induced chromosomal proximity and DNA breaks underlie specific translocations in cancer[END_REF][START_REF] Robbiani | AID produces DNA double-strand breaks in non-Ig genes and mature B cell lymphomas with reciprocal chromosome translocations[END_REF] . Whether Top1 is also involved in AIDinduced translocations is an important question that remains to be addressed.

Another important question concerns the link between Top1 and common fragile sites (CFSs).

CFSs are regions of the genome that are particularly susceptible to break when replication is impeded and that contribute to gene amplification and chromosomal rearrangements [START_REF] Coquelle | Expression of fragile sites triggers intrachromosomal mammalian gene amplification and sets boundaries to early amplicons[END_REF][START_REF] Durkin | Chromosome Fragile Sites[END_REF] . We have found that FRA3B, FRA16D and FRAXB, three major human CFSs, are frequently broken in human Top1-deficient cells [START_REF] Tuduri | Topoisomerase 1 suppresses replication stress and genomic instability by preventing interference between replication and transcription[END_REF] . We believe that this increased fragility is not solely due to slower replication forks but also to altered mRNPs biogenesis at CFSs. The bestcharacterized CFSs are associated with very large genes [START_REF] Helmrich | Common fragile sites are conserved features of human and mouse chromosomes and relate to large active genes[END_REF] , which is consistent with the view that breakage is induced by a TAR-related process. To test this hypothesis, the role of ASF/SF2 on CFS expression should be addressed in normal and Top1-deficient cells. If Top1 and ASF/SF2 act in the same pathway to prevent CFS expression, we expect the effect of their co-depletion not to be additive.

Finally, it has been proposed that overexpression of oncogenes in the early stages of the cancer process interferes with the proper assembly of pre-RCs and reduces the number of licensed replication origins [START_REF] Lengronne | The yeast CDK inhibitor Sic1 prevents genomic instability by promoting replication origin licensing in late G(1)[END_REF][START_REF] Ekholm-Reed | Deregulation of cyclin E in human cells interferes with prereplication complex assembly[END_REF] . Since TAR depends on the relative orientation of replication and transcription, changes in the position of active origins are expected to affect the direction of fork progression relative to genes and therefore increase TAR. It is therefore tempting to speculate that transcription-associated replication stress acts synergistically with oncogeneinduced alteration of the replication program to promote genomic instability in cancer cells.

Figure 1 :

 1 Figure 1: Model for the role of Top1 in the regulation of replication/transcription interference. In control cells (Top1+), topoisomerase1 cooperates with ASF/SF2 to prevent the formation of DNA-RNA hybrids (R-loops). In Top1-cells, R-loops are formed and lead to replication fork stalling, phosphorylation of H2AX and genomic instability. Treatment of Top1+ cells with diospyrin (an inhibitor of Top1 kinase activity) or with a siRNA targeting ASF/SF2 mimics a Top1-phenotype. Treatment of Top1-cells with cordycepin (a transcription inhibitor) or transfection with RNaseH to degrade R-loops restores a wild type phenotype.

Figure 2 :

 2 Figure 2: γ-H2AX is enriched at active genes in Top1-HCT116 cells. (A) Untreated (-CPT) or CPT-treated (40 µM, 1 hour; +CPT) HCT116 cells expressing control (shCtrl) or Top1 (shTop1) siRNAs were harvested by Trypsin treatment, washed with PBS, and fixed with 1% formaldehyde in PBS during 10 minutes at room temperature. Chromatin was immunoprecipitated with a phospho-specific antibody that recognizes γ-H2AX and DNA was hybridized on Affymetrix high-density tiling arrays (35 bp resolution) covering human chromosomes 1 and 6, as described previously 14 . γ-H2AX enrichment is represented as heat maps, yellow areas corresponding to high enrichment. Local gene density (exons) is indicated above chromosome maps. R-bands: light grey, G-bands: dark grey. The position of the three major histone genes clusters is indicated in red. (B) Western blot analysis of the global amount of γ-H2AX in shCtrl and shTop1 HCT116 cells exposed or not to CPT. Equal amount chromatin samples used for ChIP-chip were analyzed. (C) Number of statistically-enriched γ-H2AX regions (P<0.01) on chromosome 1 and 6 of shTop1 and shCtrl cells treated or not with CPT. (D) Overlap between γ-H2AX loci and annotated genes in shTop1 and shCtrl cells exposed to CPT. Red: Observed overlap between γ-H2AX loci and annotated genes (+/-1 kb).Black: Distribution of expected overlaps if γ-H2AX loci were randomly distributed. 500 random distributions were generated with the TAS v1.1 software (Affymetrix). (E) Proportion of γ-H2AX-enriched histone genes in shTop1 and shCtrl cells exposed or not to CPT.

Figure 3 :

 3 Figure 3: Camptothecin suppresses the accumulation of γ-H2AX specifically at replication-dependent histone genes. Heat map of the location of γ-H2AX-enriched domains at 500 kb regions encompassing histone gene clusters in shTop1 (A) or shCtrl (B)

Acknowledgements

We thank Jamal Tazi, Yves Pommier, Chiara Conti, Ana Jauch and Charles Theillet for their contribution to this project. We are grateful to Johann Soret for reagents and helpful discussions and to Daniel Durocher for communicating results prior to publication. We also thank Véronique Pantesco, John De Vos and Aubin Thomas for help with ChIP-chip analysis and bioinformatics. Work in the PP lab is supported by Fondation Recherche Médicale (Equipe FRM), ANR and INCa. Work in AC lab is supported by the Ligue contre le Cancer (comité Hérault), ARC and INCa. LC thanks EMBO for a long-term fellowship.