Alexander Antonio Oliva
email: alexander.oliva@inria.fr

Erwin Aertbeliën
email: erwin.aertbelien@kuleuven.be

Joris De Schutter
email: joris.deschutter@kuleuven.be

Paolo Robuffo Giordano

Franc ¸ois Chaumette
email: francois.chaumette@inria.fr

Towards Dynamic Visual Servoing for Interaction Control and Moving Targets

In this work we present our results on dynamic visual servoing for the case of moving targets while also exploring the possibility of using such a controller for interaction with the environment. We illustrate the derivation of a feature space impedance controller for tracking a moving object as well as an Extended Kalman Filter based on the visual servoing kinematics for increasing the rate of the visual information and estimating the target velocity for both the cases of PBVS and IBVS with image point features. Simulations are carried out to validate the estimator performance during a Peg-in-Hole insertion task with a moving part. Experiments are also conducted on a real redundant manipulator with a low-cost wrist-mounted camera. Details on several implementation issues encountered during implementation are also discussed.

I. INTRODUCTION

Visual Servoing is a consolidated control technique that allows to precisely position a vision sensor with respect to an observed object in the scene. In the past decades, most research efforts focused on the modeling of the existing relationships between descriptive features in the image and the motion of the sensor at kinematic level [START_REF] Espiau | A new approach to visual servoing in robotics[END_REF], [START_REF] Chaumette | Image moments: a general and useful set of features for visual servoing[END_REF], and in the development of control strategies to guide the chosen visual features towards the desired ones as summarized in [START_REF] Chaumette | Visual servo control. i. basic approaches[END_REF], [START_REF]Visual servo control. ii. advanced approaches [tutorial[END_REF]. Although much research has been conducted in this sense, little attention has been devoted to the use of second order models linking the features accelerations to the robot torques (via the robot dynamical model), with some early attempts dating back more than two decades [START_REF] Espiau | A new approach to visual servoing in robotics[END_REF], [START_REF] Zhang | Visual servoing with dynamics: control of an unmanned blimp[END_REF] up to some more recent contributions [START_REF] Vandernotte | Dynamic sensor-based control[END_REF], [START_REF] Fusco | A comparison of visual servoing from features velocity and acceleration interaction models[END_REF]. As it is well known, explicitly taking into account the robot dynamics allows to design controllers with superior performance, especially for what concerns the regulation of forces and interaction with the environment. For instance, in many industrial applications the interaction tasks are executed in static or quasi-static conditions and can be solved at kinematic level. However if one wishes to reduce the execution time, faster motions are required making the control task essentially dynamic [START_REF] Vukobratović | Is dynamic control needed in robotic systems, and, if so, to what extent?[END_REF].

Most of the cited works mostly focused on the motion of the robot in free space without taking into account possible physical interactions with the environment. Indeed while [START_REF] Zhang | Visual servoing with dynamics: control of an unmanned blimp[END_REF] only compensates for the air drag forces acting on the blimp, in [START_REF] Vandernotte | Dynamic sensor-based control[END_REF], dynamic visual-servoing (DVS) is used to control solely the unconstrained directions of an engraving task within a hybrid control scheme [START_REF] Nelson | Robotic manipulation using high bandwidth force and vision feedback[END_REF] while a pure force controller acts on the constrained directions. The goal of this paper is to study the possibility to use DVS for interaction control, since, as we have shown in previous work [START_REF] Oliva | A general visual-impedance framework for effectively combining vision and force sensing in feature space[END_REF], a DVS controller under interaction with the environment is essentially an impedance controller in feature space, which is an indirect force control method [START_REF] Siciliano | Robotics: Modeling, Planning and Control[END_REF]. In [START_REF] Oliva | A general visual-impedance framework for effectively combining vision and force sensing in feature space[END_REF] we proposed an alternative to the impedance control in feature space -which we could not implement given the limitations discussed (and addressed) in this paper -by defining an admittance in feature space. The main limitation of this solution is however that, in addition to requiring an external sensor to measure the interaction forces, all collisions occurring upstream of the sensor cannot be detected and the manipulator cannot counteract for accommodating these forces.

A contribution of this work is, therefore, to overcome these difficulties and propose a true DVS in feature space that can also take into account the possible (unknown) motion of the observed target object. This is achieved by modeling the motion of the target in a DVS setting and by estimating its unknown motion via a suitable Extended Kalman Filter (EKF). The EKF is also instrumental for virtually increasing the data rate of the employed camera by providing highrate estimations of the quantities needed by the employed torque-level controller. The derivations are specialized for the cases of Image-Based Visual Servoing (IBVS) and Position-Based Visual Servoing (PBVS) by pointing out the respective similarities and differences.

II. PRELIMINARIES

Let us consider the fixed world frame of reference, named Σ w and with the z-axis pointing upwards, to be coincident with the robot base frame Σ b . The manipulator consists of a succession of links and actuated joints whose configuration is given by the joint vector q ∈ R n . The end-effector frame is denoted with Σ e . A camera (frame Σ c ) in an eye-in-hand configuration is mounted on the robot wrist. Finally, an object (frame Σ o ) is visually tracked via the camera. For IBVS, the coordinates of image point features are directly extracted from the image and the associated depths are assumed provided by another pose reconstruction module. For PBVS, the object's pose in frame Σ c is instead reconstructed from the visual input. Any required geometrical transformation between the above-defined reference frames is supposed to be known, except for those concerning the object frame.

The dynamic model of a robotic arm can be written using the Lagrange formulation in the joint space as [START_REF] Siciliano | Robotics: Modeling, Planning and Control[END_REF] B(q)q + c(q, q) + g(q) + τ e = τ

(1)

where q, q ∈ R n are respectively the generalized joint velocities and accelerations, B(q) ∈ R n×n is the symmetric and positive definite inertia matrix, c(q, q) ∈ R n is the vector of centrifugal and Coriolis effects, g(q) ∈ R n is the configuration dependent vector of gravitational forces, τ e = e J e e h e ∈ R n is the joint torque vector corresponding to the external wrench e h e ∈ R 6 acting on the end-effector frame and e J e ∈ R 6×n is the robot Jacobian, being both expressed in end-effector frame. Finally, τ ∈ R n is the vector of joint actuation torques. For the sake of notation simplicity, we omit the time dependencies in the treatment when possible.

Let us now consider a vector s ∈ R k of k "features" which in the IBVS case can be, e.g., the coordinates of the points in the image plane and in the PBVS case the position and orientation of the observed object. The kinematic relationship between the motion of the visual features and the relative twist c v ∈ R 6 , which is the difference between camera ( c v c ) and object ( c v o ) twists in the camera frame, is given by the following differential relation

ṡ = L s c v = L s ( c v c -c v o ) = L s c v c -ṡo (2)
being L s ∈ R k×6 the well-known interaction matrix [START_REF] Espiau | A new approach to visual servoing in robotics[END_REF] (in this work we will consider that it has full rank, thus constraining the 6 degrees of freedom (DoF) of the camera motion). The second term in the right-hand side represents the contribution to the feature velocity due to the (assumed unknown) target motion ṡo = L s c v o .

III. VISUAL-IMPEDANCE CONTROLLER IN FEATURE SPACE WITH MOVING TARGET

In the following we recall the derivation of the visualservo (VS) dynamics and how the motion of visual features relates to the robot joint motion at acceleration level. This relationship will then be used, together with the VS kinematics, to express the dynamic model of the manipulator in feature space.

A classical VS task consists in regulating some visual features s to reach a desired value s * . In a previous work [START_REF] Oliva | A general visual-impedance framework for effectively combining vision and force sensing in feature space[END_REF], we have detailed the derivation of an impedance controller in feature space for a static target. In this section we summarize the various steps of [START_REF] Oliva | A general visual-impedance framework for effectively combining vision and force sensing in feature space[END_REF] but by considering the more general case of tracking a moving target with a nonnegligible velocity. For the case of an eye-in-hand system, Eq. ( 2) can be rewritten to relate the velocity of the features with the velocities of the robot joints through its Jacobian as ṡ = L s c T e e J e qṡo (3) being c T e ∈ R 6×6 the twist-transformation matrix that transforms the camera twist expressed in the end-effector frame into the camera frame. By time differentiation one obtains the expression of the features acceleration s = Ls c T e e J e q + L s c Ṫe e J e q+ L s c T e e Je q + L s c T e e J e qso (4)

in which a term relative to the target motion appears.

We can rewrite (4) in a more compact form as

s = J s q + h q -so (5)
where J s = L s c T e e J e denotes the so called Feature Jacobian [START_REF] Chaumette | Visual servoing[END_REF], [START_REF] Fusco | A comparison of visual servoing from features velocity and acceleration interaction models[END_REF] and

h q = ( Ls c T e e J e + L s c T e e
Je ) q.

Note that, being the transformation between the camera and the end-effector constant, for a mounted camera one has c Ṫe = 0. Now, rearranging (1) as

q = B(q) -1 (τ -b(q, q) -e J e e h e ) (6)
in which we regroup in b(q, q) = c(q, q) + g(q), and after replacing ( 6) into ( 5), we find the dynamic equation governing the features motion

J s B(q) -1 τ = s + so -h q +J s B(q) -1 b(q, q) + J s B(q) -1e J e e h e . (7)
By renaming some terms in [START_REF] Fusco | A comparison of visual servoing from features velocity and acceleration interaction models[END_REF] as

f s = J s B(q) -1 τ b s = J s B(q) -1 b(q, q) f sext = J s B(q) -1e J e e h e
one obtains the manipulator dynamic model in feature space:

s + so -h q + b s + f sext = f s . (8)
As can be seen from the previous equation, the system is not endowed of inertia (it behaves as a mechanical system with unitary mass/inertia). At this stage we suppose to not have any force measurement, but an estimate of the feature velocity and acceleration due to the target motion ( ṡo , so ) is supposed to be available (Sect. IV will detail how this estimation is obtained). We choose a feature space control input u able to compensate for any of the dynamic terms in ( 8)

u = α -h q + so + b s which re-injected into (8) results in α = s + f sext (9)
in which α ∈ R k represents a resolved acceleration in feature space and is the new control input variable. A common choice for α is a PD controller with acceleration feedforward:

α = s * + D s ės + K s e s . ( 10
)
where D s and K s are positive definite k × k matrices representing the relative per unit of mass inertia (p.u.m.i.) virtual damping and stiffness of the impedance controller.

The resulting joint space controller, recalling the error definition e s = s * -s , ės = ṡ * -ṡ and Eq. ( 3), is then

u τ = (J s B(q) -1 ) † s * + so -h q + K s (s * -s) +D s ( ṡ * -J s q + ṡo ) + J s B(q) -1 b(q, q) (11)
where the new terms so and D s ṡo compensate for the target's motion. Replacing Eq. ( 10) into ( 9) yields to the closed-loop system ës + D s ės + K s e s = f sext where f sext ∈ R k is the vector of p.u.m.i. virtual forces (accelerations) acting on the features due to the external forces acting on the robot end-effector.

IV. EXTENDED KALMAN FILTER FOR TARGET TRACKING AND TARGET MOTION ESTIMATION

In the previous section, we derived a controller that linearizes and decouples the dynamics of the system via feedback linearization but, even though most of the terms in the controller can be easily computed, those related to the target's own motion are usually unknown and must thus be estimated. Since we do not assume, for the sake of generality, a particular model for the target motion, we treat it as a disturbance of the nominal motion of the features due to the motion of the camera as in [START_REF] Chaumette | Image moments: a general and useful set of features for visual servoing[END_REF], in which part of the feature velocity is given by the (unknown) target velocity ṡo = L s c v o . To estimate the target motion we now describe two instances of an EKF used to estimate the target own motion for the two cases of image points and 3D pose error as visual features respectively. Note that both filters could be used in a classical VS control scheme to compensate for the target motion while here we exploit the entire state of the filters to implement the control law [START_REF] Siciliano | Robotics: Modeling, Planning and Control[END_REF] for (i) virtually increasing the visual measurements frequency (i.e., by using the state predictions as virtual measurements), and (ii) compensating for the target own motion and, thus, significantly reducing the tracking error.

A. EKF for IBVS with image points features

The goal of the filter is to both increase the data rate of the camera and to estimate the velocity of the moving target. The feature vector s will then be part of the filter state with a dynamics following the model equation [START_REF] Chaumette | Visual servo control. i. basic approaches[END_REF]. For each image point f = (f x , f y ), the associated Interaction matrix [START_REF] Espiau | A new approach to visual servoing in robotics[END_REF]

L s (f, Z) = -1 Z 0 fx Z f x f y -(1 + f 2 x ) f y 0 -1 Z fy Z 1 + f 2 y -f x f y -f x
requires the knowledge of the depth of the feature Z which is also estimated by the filter as long as the trajectories are "exciting" enough. The depth dynamic is [START_REF] Luca | On-line estimation of feature depth for image-based visual servoing schemes[END_REF]

Ż = L Z (f, Z) c v = 0 0 -1 -f y Z f x Z 0 c v.
Furthermore, to implement controller [START_REF] Siciliano | Robotics: Modeling, Planning and Control[END_REF], the feature acceleration contribution due to the motion of the target is also needed. The continuous-time state of the filter, stacking four image points is then

x(t) = [s(t) z(t) ṡo (t) so (t)] = [x 1 (t) x 2 (t) x 3 (t) x 4 (t)] ∈ R 28
, and expliciting the filter dynamics ẋ(t) = f (x(t), u(t)) + w t one has: [START_REF] Chaumette | Visual servoing[END_REF] in which u = q is the plant input, L s † is the Moore-Penrose pseudo-inverse of the Interaction matrix, and w t is a vector of additive Gaussian noise w t ∼ N (0, Q k ) . In the general case one does not have a model of the target motion, therefore we assume the dynamics of the acceleration of the features due to the target motion so (t) to be approximately constant (corrupted by some added noise), i.e., ...

ẋ =     L s (x 1 , x 2 ) c T e e J e u -x 3 L Z (x 1 , x 2 )( c T e e J e u -L s † (x 1 , x 2 )x 3 ) x 4 0     + w t
s o (t) = w 4t .
Clearly if a motion model were available, it could be used, adjusting opportunely the filter dynamics.

To update the process state estimate with the (known) dynamics and its uncertainty, represented by the covariance matrix P , we firstly need to linearize and discretize it at the control period ∆T :

Ā = ∂f (x, u) ∂x x=x k ,u=u k (13)
A k = e Ā∆T ≈ I + Ā∆T x k+1|k = x k|k + f (x k|k , u k )∆T P k+1|k = A k P k|k A k + Q k (14)
The state estimation improves during the measurement update phase based on the measurements y k . In our case, the measurement is the set of image point features in the image plane, leading then to a linear measurement equation in the filter state that is already in discrete time:

y k = s k+1 + W s k+1 = C k+1 x k+1|k + W s k+1
with W s k+1 a vector of discrete-time white noise with autocorrelation matrix R k+1 , while C k+1 = [I 8 O 8×20 ] is the measurement sensitivity matrix.

S k+1 = (C k+1 P k+1|k C k+1 + R k+1 ) (15)
K k+1 = P k+1|k C k+1 S -1 k+1 x k+1|k+1 = x k+1|k + K k+1 (y k -C k+1 x k+1|k ) P k+1|k+1 = (I -K k+1 C k+1 )P k+1|k (16)

B. EKF for PBVS

The filter state for Pose-Based Visual Servoing (PBVS) is very similar to the previous case but without the depth dynamics. The feature s is a pose error vector and the state is

x(t) = [s(t) ṡo (t) so (t)] = [x 1 (t) x 2 (t) x 3 (t) ] ∈ R 18 with dynamics ẋ =   L s (x 1 ) c T e e J e u -x 2 x 3 0   + w t .
In order to update the state estimate with the process equations, one can follow the same steps as in ( 13)- [START_REF] Marchand | Visp for visual servoing: a generic software platform with a wide class of robot control skills[END_REF].

The "measurement" provided by the camera in the PBVS case is the relative pose of the observed object w.r.t. the camera frame, expressed as the homogeneous transformation matrix c T o (y k ). This leads to a discrete-time, non-linear and implicit measurement equation w.r.t. both the measure and filter state:

η k+1 = h(x k+1|k , y k+1 ). (17)
The measurement equation ( 17) is a constraint equation (h(x, y) = 0) and to compute it, we first define the "closure" equation: cd T c (x k+1|k ) c T o (y k+1 ) cd T o -1 = I, being cd T o the desired pose of the camera w.r.t. the object (which is of course known by design). The pose vector associated to the left-hand side of the closure equation must be zero, and this relationship represents the measurement equation of the form [START_REF] Khatib | A unified approach for motion and force control of robot manipulators: The operational space formulation[END_REF]. The measurement update equations differ from those in ( 15)-( 16) as follows:

H k+1 = ∂h(x, y) ∂x x=x k+1|k ,y=y k+1 D k+1 = ∂h(x, y) ∂y x=x k+1|k ,y=y k+1 S k+1 = (H k+1 P k+1|k H k+1 + D k+1 R k+1 D k+1 ) K k+1 = -P k+1|k H k+1 S -1 k+1 x k+1|k+1 = x k+1|k + K k+1 η k+1 P k+1|k+1 = P k+1|k + K k H k P k+1|k .
Once the filter converges, one can obtain the target velocity from the estimated disturbance state as c v o (t k ) = L s † x 3 (t k ) as we did in the depth dynamics of the EKF for IBVS (see second component of ( 12)).

V. SIMULATIONS

In order to validate the tracking performance of the proposed EKFs and study the feasibility of using DVS in a task involving contacts with the environment, we simulate a Peg-in-Hole task with a moving target using FrankaSim 1 , a co-simulation environment implemented in ViSP [START_REF] Marchand | Visp for visual servoing: a generic software platform with a wide class of robot control skills[END_REF] and Coppeliasim [START_REF] Rohmer | Coppeliasim (formerly v-rep): a versatile and scalable robot simulation framework[END_REF]. With FrankaSim it is possible to carry out dynamic simulations with a Panda robot, a 7-joint lightweight manipulator, and process different sensor information. The simulation was performed in synchronous mode with a time step of 1 ms using Vortex physics engine, since it is capable to deal with non-convex shapes. A vision sensor was mounted on the robot wrist and we programmatically drop and delay the grabbed frames in order to simulate a 30 fps (33 ms) camera rate with 10 ms of feature extraction delay. The visual features are extracted from an AprilTag of the family 36h11 using the ViSP detector (AprilTag pose or its corners). A 5 × 5 Gaussian mask is applied on the grabbed images to smooth the AprilTags' borders making the detection process more realistic. This is a convenient way to simulate noise because despite for points features it is easy to add Gaussian noise around the detected pixel, the uncertainties on the estimated pose depends on the distance of the object from the camera [START_REF] Larsen | Incorporation of time delayed measurements in a discrete-time kalman filter[END_REF]. White noise of the magnitude of the real robot was also added to the measured joint velocities q with standard deviation σ = 0.021 rad/s. The robot control loop runs at 1 kHz as for the real one.

A. Task Description and Controllers implementation

The simulated task consists in inserting a peg in the hole of a work-piece both cylindrical with diameters of 9 and 10 mm respectively. The piece is placed on a moving platform whose lowest joints turn at 0.5 rad/s in opposite directions allowing the upper part to undergo a purely translational motion, while the last joint of the platform performs a sinusoidal velocity motion of amplitude 0.2 rad/s and frequency 1/π Hz (see Fig. 1

and accompanying video).

Although the task constrains all the 6 degrees of freedom (DoF) of the operational space, our robot is a redundant manipulator and to prevent the robot joints to move along directions that lie in the kernel of the task Jacobian J s , the null-space directions must be damped (or a suitable secondary task must be considered). Controller [START_REF] Siciliano | Robotics: Modeling, Planning and Control[END_REF] is then modified into the more computationally efficient form with the addition of a damping term in the null space of the task:

u τ = (J s B(q) -1 ) † s * + D s ( ṡ * -L s c
T e e J e q + ṡo ) + K s e s + soh q + e s + c(q, q) + P ⊥ τ N [START_REF] Simetti | A novel practical technique to integrate inequality control objectives and task transitions in priority based control[END_REF] in which the gravity term does not appear anymore since it is already compensated by the robot (in both the real and simulated cases), and P ⊥ is a Null space projector that applies the secondary damping torques τ N = -k d q on ker(J s ).

Assuming the manipulator is in a non-singular configuration, in the PBVS case the task Jacobian has dimension 6 × 7 with rank(J s ) = 6 and a Null space projector based on the dynamically consistent Inertia weighted pseudo-inverse P ⊥ = (I -J s Js ) can be computed [START_REF] Khatib | A unified approach for motion and force control of robot manipulators: The operational space formulation[END_REF], where Js = B -1 J s (J s B -1 J s ) -1 . On the other hand, in the IBVS case the task Jacobian is 8 × 7 with rank(J s ) = 6 and more general methods must be used to find an orthogonal basis of ker(J s ), e.g., resorting to a Singular Value Decomposition (SVD) (J = U ΣV ) [START_REF] Simetti | A novel practical technique to integrate inequality control objectives and task transitions in priority based control[END_REF].

B. Simulation Results

We ran several simulations for both the proposed filters in which the estimates were used in the controller [START_REF] Simetti | A novel practical technique to integrate inequality control objectives and task transitions in priority based control[END_REF] to accomplish the task. The controller in the IBVS case exhibited large sensibility to depth errors and despite the good convergence observed for the depth estimation, we assumed for the experiments to have a "measurement" of the depth to overcome the persistency of excitation issue, since once the camera is above the target the trajectory does not excite the Z dynamics enough. The velocity estimation has a very good performance for both filters (see Fig. 2) but it is not perfect, and a small tracking error remains; an integral term was added to controller (18) when the feature error was sufficiently small to compensate for any final error and correctly realize the insertion task (see Figs. 3 and4). With the proposed process model, the filter was able to properly reconstruct the disturbance (target velocity), thus succeeding in completing the task. Apart from the very beginning, which is affected by the transient behaviour of the filter convergence and the effects of a rapidly decaying exponential u τ (0)e -µt added to the torque command to guarantee a smooth start, the features trajectories show a nice exponential decrease. Simulations suggest that DVS controllers can be suitably used to deal with interactions tasks.

VI. EXPERIMENTS An experiment for tracking a moving target on a turntable was performed on a Franka Emika Panda robot equipped with a wrist mounted RealSense D435 camera operating at 60 fps. Given the space limits, we report here only the results of the DVS for the IBVS case, which is more complex than its PBVS counterpart given both the sensitivity w.r.t. the feature depths and the difficulties related to the rank deficiency of the task Jacobian (more details below). For the PBVS case and some interaction experiments, please refer to the accompanying video in which the results of this work can be truly appreciated.

A. Implementation issues

Most works have presented their results on DVS in simulation and tackling just few of the real world issues like measurement noise [START_REF] Vandernotte | Dynamic sensor-based control[END_REF], [START_REF] Fusco | A comparison of visual servoing from features velocity and acceleration interaction models[END_REF], or instead of generating the torque command from the features acceleration, a velocity signal command is obtained from numerical integration and sent to the robot, as in [START_REF] Fusco | Integrating features acceleration in visual predictive control[END_REF] for an MPC controller. In this work we aimed at pushing forward the state of the art by tackling several implementation aspects of a real implementation not covered in previous works.

1) Data rate: The main bottleneck in a DVS controller implementation is certainly the low data rate coming from the vision sensor and the latency of the computer vision algorithms for the features extraction. The main contribution of this work are the proposed EKFs which increases the data rate and help to alleviate the negative effects of delayed measurements. If this problem is not addressed, it is practically impossible to implement such a control scheme without resorting to expensive high-speed cameras.

2) Ill-condition of the task Jacobian: The computation of the control command [START_REF] Simetti | A novel practical technique to integrate inequality control objectives and task transitions in priority based control[END_REF] requires to pseudo-invert the task Jacobian J = J s B -1 which in practice turns out to be strongly ill-conditioned. This problem is due to the double effect of the ill-condition of the inertia matrix [START_REF] Maciejewski | Dealing with the ill-conditioned equations of motion for articulated figures[END_REF] which is then combined with the image Jacobian, resulting in a magnified singular values ratio. This issue is particularly pronounced when the disparity between the masses and inertia tensors of the individual links increases. Featherstone [START_REF] Featherstone | An empirical study of the joint space inertia matrix[END_REF] has empirically found that the the condition number of the inertia matrix can asymptotically grow with O(n 4 ) of the number of bodies, which is our case as we are servoing a 7-joints manipulator. A common solution for dealing with illconditioned equations is through a regularization term such as in a Damped Least-squares (DLS) law [START_REF] Simetti | A novel practical technique to integrate inequality control objectives and task transitions in priority based control[END_REF]. However, we found that with a DLS-based regularization, rotations about xand y-axis were far less "stiff" than the other directions while using an SVD-based regularization with a Gaussian function of the singular value as regularization term, g( i ) = m exp(-2 i 2σ 2 ) with i the i-th singular value, the controller achieves better performance.

3) Joint friction: In [START_REF] Gaz | Dynamic identification of the Franka Emika Panda robot with retrieval of feasible parameters using penalty-based optimization[END_REF], the dynamic friction for this robot has been identified and we used the released library to compensate for it. Unfortunately, we lack a model for dry/static friction, so small commanded torques do not result in actual robot motion, limiting the positioning performance close to the goal position; we have found this threshold to be ∼ 0.5 Nm for our robot. This phenomenon particularly affects the last joint since the controlled torques are often below this value. The integral term on the features error we add before alleviate this issue, allowing to reduce the remaining steady state error. Further increasing the proportional gain could in fact leads to an unstable behaviour.

4) Illumination conditions: Despite evident when working with vision sensors, experiments are often conducted under poor illumination conditions with consequent performance degradation. In our case, it is of paramount importance to guarantee and maintain a continuous and constant feature detection rate to avoid the data fusion of too much delayed visual measurements, leading to catastrophic results. A good illuminated environment stabilizes the feature extraction with ViSP to ∼ 8 ms while in regular conditions it can oscillate from 8 to 20 ms. Spotlights were then placed around the experimental platform also trying to avoid obscuring the target or blinding the camera with reflections.

B. Results

The proposed EKF proves to be effective both in increasing the data rate of the camera, providing an accurate estimate of the features (and depth) at higher frequency, and in alleviating the effects due to delays in the feature extraction process as well as in providing an accurate estimate of the target velocity. In Fig. 6 we can see how the feature error remains limited despite the highly dynamic motion (velocities up to 40 [cm/s] and almost 2 [rad/s] with high accelerations) manually applied to the turntable. Once the target is kept at rest, the feature errors slowly converges to zero. This is due to the high dry friction in the joints that the integral term has to overcome. Dry friction remains the main issue limiting the tracking accuracy of our implementation while SVD-based regularization managed to cope with the strong ill-conditioning of the task Jacobian. VII. CONCLUSIONS In this paper we presented our preliminary results on DVS for interaction control and moving targets. The proposed EKFs effectively track the target motion and provide high rate visual information allowing for the implementation on a real platform with very good performance for the motion tracking. Several real-world implementation issues have been discussed alongside the solutions adopted. We recognize in the dry friction the main cause limiting the tracking performances of our implementation, especially at low speeds. In future works we plan to integrate friction models to improve the performance and to consider passivity-based techniques to further increase the system stability to robustly use such controllers in dynamic interaction tasks.

Fig. 1 :

 1 Fig. 1: Simulation setup: The Panda robot is in the initial configuration of the experiment. World and base Σw ≡ Σ b , camera Σc, end-effector Σe and target Σo frames are drawn. An AprilTag is attached on the workpiece and tracked by the camera. The lower plate of the platform rotates at ω1 = 0.5 rad/s, the second plate at ω2 = -ω1 while the last joint motion is f (t) = 0.2sin(2t).

Fig. 2 :

 2 Fig. 2: Peg-in-Hole simulation: target linear (top) and angular (bottom) velocity estimates in camera frame vs ground-truth data collected from the simulation environment. The choice of a constant acceleration model for the target motion is well evident from the plotted curbs. Note how the angular velocity about the y axis c wo y follows the target motion about the same direction (f (t) = 0.2 * sin(2t)). This figure reports the results of the PBVS experiment, but similar results were achieved in the IBVS case (see accompanying video).

Fig. 3 :

 3 Fig. 3: IBVS Peg-in-Hole simulation: Initial (left) and final (right) camera views and robot configurations. The features errors are plotted. On the right image it is possible to appreciate, in green, the features trajectories in the image plane. Convergence is successfully attained.

16 Fig. 4 :

 164 Fig. 4: PBVS Peg-in-Hole simulation: Initial (left) and final (right) camera views and robot configurations. Features position (top) and orientation (bottom) errors. On the right image it is possible to appreciate, in green, the trajectory of the target corners in the image plane. Convergence is successfully attained.

Fig. 5 :

 5 Fig. 5: Setup: Panda robot equipped with a RealSense D435 operating at 60 fps. The center of the target is located at 22 cm from the center of the turntable, which is manually operated.

Fig. 6 :

 6 Fig.6: Estimated target linear (Top) and angular (middle) velocities in camera frame compared to the feature errors (bottom). The circular motion produces a linear velocity component tangential to the circle and from a frame on top of the target it produces a linear and an angular velocity in camera frame along the y-axis and about the z-axis respectively.

http://wiki.ros.org/visp ros

This work was supported by the Région Bretagne, France.


Eleonora Aquilini

Marta Mendonça Cova

Shrawan Kumar Mageswaran

Nicolas Dos

Santos Pacheco

Daniela Sparvoli

Diana Marcela Penarete-Vargas

Rania Najm

Arnault Graindorge

Catherine Suarez

Marjorie Maynadier

Laurence Berry-Sterkers

Serge Urbach

Pilar Ruga Fahy

Amandine N Guérin

Boris Striepen

Jean-François Dubremetz

Yi-Wei Chang

Aaron P Turkewitz

Maryse Lebrun

Affiliations

An Alveolata secretory machinery adapted to parasite host cell invasion

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

Manuscript Text: 2499 words Apicomplexa are unicellular eukaryotes and obligate intracellular parasites, including Plasmodium, the causative agent of malaria and Toxoplasma, one of the most widespread zoonotic pathogens. Rhoptries, one of their specialized secretory organelles, undergo regulated exocytosis during invasion [START_REF] Dubremetz | Rhoptries are major players in Toxoplasma gondii invasion and host cell interaction[END_REF] . Rhoptry proteins are injected directly into the host cell to support invasion and subversion of host immune function [START_REF] Boothroyd | Kiss and spit: the dual roles of Toxoplasma rhoptries[END_REF] . The mechanism by which they are discharged is unclear and appears distinct from those in bacteria, yeast, animals or plants.

Here we show that rhoptry secretion in Apicomplexa shares structural and genetic elements with the exocytic machinery of ciliates, their free-living relatives. Rhoptry exocytosis depends on intramembranous particles in the shape of a rosette embedded into the plasma membrane of the parasite apex. Formation of this rosette requires multiple Non-discharge (Nd) proteins conserved and restricted to Ciliata, Dinoflagellata, and Apicomplexa, that together constitute the superphylum Alveolata. We identified Nd6 at the site of exocytosis in association with an apical vesicle. Sandwiched between the rosette and the tip of the rhoptry, this vesicle appears as a central element of the rhoptry secretion machine. Our results describe a conserved secretion system that was adapted to provide defense for free-living unicellular eukaryotes and host cell injection in intracellular parasites.

Apicomplexan parasites are invasive and defined by the presence of an apical complex used to recognize and gain entry into host cells. It includes two secretory organelles: micronemes and rhoptries [START_REF] Carruthers | Sequential protein secretion from three distinct organelles of Toxoplasma gondii accompanies invasion of human fibroblasts[END_REF] . Microneme proteins are secreted to the parasite surface and mediate motility, host cell recognition and invasion [START_REF] Frenal | Gliding motility powers invasion and egress in Apicomplexa[END_REF] . Rhoptry proteins are injected directly into the host cell [START_REF] Boothroyd | Kiss and spit: the dual roles of Toxoplasma rhoptries[END_REF] , where they anchor the machinery propelling the parasite into the host cell [START_REF] Besteiro | The moving junction of apicomplexan parasites: a key structure for invasion[END_REF] , facilitate nutrient import [START_REF] Ito | An essential dual-function complex mediates erythrocyte invasion and channel-mediated nutrient uptake in malaria parasites[END_REF][START_REF] Counihan | Plasmodium falciparum parasites deploy RhopH2 into the host erythrocyte to obtain nutrients, grow and replicate[END_REF][START_REF] Nguitragool | Malaria parasite clag3 genes determine channel-mediated nutrient uptake by infected red blood cells[END_REF] , interfere with the immune response, and modulate gene expression to promote infection [START_REF] Hakimi | Toxoplasma Effectors Targeting Host Signaling and Transcription[END_REF] . Rhoptry secretion requires a trigger in the form of microneme proteins binding to host cell receptors [START_REF] Singh | Distinct external signals trigger sequential release of apical organelles during erythrocyte invasion by malaria parasites[END_REF][START_REF] Kessler | Microneme protein 8--a new essential invasion factor in Toxoplasma gondii[END_REF] . Importantly, rhoptry proteins not only cross the plasma membrane of the parasite (exocytosis) but also that of the host [START_REF] Dubremetz | Rhoptries are major players in Toxoplasma gondii invasion and host cell interaction[END_REF][START_REF] Nichols | Secretion from the rhoptries of Toxoplasma gondii during host-cell invasion[END_REF] . Rhoptry exocytosis factors identified to date [START_REF] Kessler | Microneme protein 8--a new essential invasion factor in Toxoplasma gondii[END_REF][START_REF] Coleman | A Member of the Ferlin Calcium Sensor Family Is Essential for Toxoplasma gondii Rhoptry Secretion[END_REF][START_REF] Suarez | A lipid-binding protein mediates rhoptry discharge and invasion in Plasmodium falciparum and Toxoplasma gondii parasites[END_REF] are mostly specific to Apicomplexa, suggesting that these cells depend on unique lineage-restricted secretory mechanisms. Consistent with this, no eukaryotic SNAREs, the main drivers for fusing vesicles to target membrane in eukaryotic system, have so far been associated with rhoptry exocytosis. Apicomplexa also lack genes encoding prokaryotic secretion systems [START_REF] Tseng | Protein secretion systems in bacterial-host associations, and their description in the Gene Ontology[END_REF] . Thus, how rhoptry effectors are delivered into the host cytoplasm remains unclear.

To explore the mechanisms of rhoptry secretion, we searched for examples of regulated secretion in organisms phylogenetically closely related to Apicomplexa [START_REF] Gubbels | Evolution of apicomplexan secretory organelles[END_REF] . All Alveolata bear alveolar sacs beneath the plasma membrane, which give the superphylum its name. They contain elaborate membrane-bounded secretory organelles with shared evolutionary origin [START_REF] Ito | An essential dual-function complex mediates erythrocyte invasion and channel-mediated nutrient uptake in malaria parasites[END_REF] but different morphologies and functions. Known as trichocysts in the Ciliata Paramecium, they function in defense-docking at the plasma membrane and discharging in response to predation [START_REF] Plattner | Trichocysts-Paramecium's Projectile-like Secretory Organelles: Reappraisal of their Biogenesis, Composition, Intracellular Transport, and Possible Functions[END_REF] . Exocytic membrane fusion occurs at plasma membrane sites consisting of rosettes of 8-9 intramembranous particles (IMPs) [START_REF] Plattner | Membrane specializations in the form of regular membrane-to-membrane attachment sites in Paramecium. A correlated freeze-etching and ultrathin-sectioning analysis[END_REF][START_REF] Knoll | Quenched flow analysis of exocytosis in Paramecium cells: time course, changes in membrane structure, and calcium requirements revealed after rapid mixing and rapid freezing of intact cells[END_REF] . Previous electron microscopy (EM) studies have described similar rosettes at the apex of several apicomplexan parasites [START_REF] Dubremetz | Freeze fracture study of the pellicle of an eimerian sporozoite (Protozoa, Coccidia)[END_REF][START_REF] Porchet | Freeze fracture study of Toxoplasma and Sarcocystis infective stages (author's transl)][END_REF][START_REF] Porchet-Hennere | Are rhoptries of Coccidia really extrusomes?[END_REF][START_REF] Dubremetz | Exocytic events during cell invasion by Apicomplexa[END_REF] . Though suggested to be involved in exocytic fusion [START_REF] Dubremetz | Freeze fracture study of the pellicle of an eimerian sporozoite (Protozoa, Coccidia)[END_REF] , the function of the IMPs rosette has never been experimentally addressed in Apicomplexa.

Analysis of Paramecium tetraurelia mutants defective in both trichocyst exocytosis and rosette assembly [START_REF] Lefort-Tran | Control of exocytotic processes: cytological and physiological studies of trichocyst mutants in Paramecium tetraurelia[END_REF][START_REF] Beisson | Genetic analysis of membrane differentiation in Paramecium. Freeze-fracture study of the trichocyst cycle in wild-type and mutant strains[END_REF] led to identification of nd (non-discharge) genes [START_REF] Gogendeau | Nd6p, a novel protein with RCC1-like domains involved in exocytosis in Paramecium tetraurelia[END_REF][START_REF] Froissard | ND9P, a novel protein with armadillo-like repeats involved in exocytosis: physiological studies using allelic mutants in Paramecium[END_REF][START_REF] Skouri | Genetic approach to regulated exocytosis using functional complementation in Paramecium: identification of the ND7 gene required for membrane fusion[END_REF][START_REF] Froissard | Novel secretory vesicle proteins essential for membrane fusion display extracellular-matrix domains[END_REF] 1). To determine if similar factors could be involved in exocytosis of secretory organelles in Apicomplexa, we first searched for Nd homologs in the tree of life. Genome mining for nd genes and phylogenetic analyses revealed that nd6 and nd9 are conserved in Ciliata, but also in Dinoflagellata, Chromerida and Apicomplexa (Extended data Fig. 1 and Extended Data 1). We also found Nd9 homolog in the Perkinsozoa, a sister group of Dinoflagellata. Altogether, this analysis suggested a conserved evolution and common function of nd genes across Alveolata.

To define the parasite localization of Nd proteins, we tagged nd6 (TGGT1_248640) and nd9 (TGGT1_249730) at the endogenous loci in Toxoplasma gondii, an experimentally tractable apicomplexan (Extended data Fig. 2). Both proteins displayed a punctuate signal throughout the cytoplasm, but in addition TgNd6 accumulated at the apical tip of the parasite (Fig. 1a,b). A closer look at the apical tip by immuno-EM localized TgNd6 at the site of rhoptry exocytosis, in association with the parasite plasma membrane and an underlying membranous spheroid known as the "apical vesicle (AV)" (Fig. 1c). Despite previously observed by EM in Toxoplasma [START_REF] Porchet | Freeze fracture study of Toxoplasma and Sarcocystis infective stages (author's transl)][END_REF][START_REF] Paredes-Santos | Dynamics and 3D organization of secretory organelles of Toxoplasma gondii[END_REF] and suggested to be linked to rhoptries [START_REF] Porchet-Hennere | Are rhoptries of Coccidia really extrusomes?[END_REF][START_REF] Paredes-Santos | Dynamics and 3D organization of secretory organelles of Toxoplasma gondii[END_REF] , the composition and function of the AV remain unknown.

Because TgNd6 and TgNd9 were predicted to be fitness-conferring genes [START_REF] Sidik | A Genome-wide CRISPR Screen in Toxoplasma Identifies Essential Apicomplexan Genes[END_REF] , in order to investigate their function we generated inducible knockdown mutants using an auxininducible degron for TgNd6 32 and tetracycline-induced repression for TgNd9 [START_REF] Meissner | Role of Toxoplasma gondii myosin A in powering parasite gliding and host cell invasion[END_REF][START_REF] Sheiner | A systematic screen to discover and analyze apicoplast proteins identifies a conserved and essential protein import factor[END_REF] (Extended data Fig. 2). Parasites conditionally depleted of TgNd6 or TgNd9 showed reduced plaque formation on fibroblast monolayers, indicating the inability of both mutants to efficiently complete the lytic cycle (Extended Data Fig. 3a). Tgnd6-iKD and Tgnd9-iKD mutants showed no detectible defects in their intracellular replication, egress, conoid protrusion, motility, or host cell attachment (Extended Data Fig. 3b-g). In contrast, invasion was severely impaired for both mutants (Fig. 1d).

To understand the mechanistic basis of this invasion defect, we evaluated microneme and rhoptry secretion in the Tgnd6-iKD and Tgnd9-iKD mutants. Release of the micronemal protein AMA1 into the supernatant was unimpeded by the absence of TgNd6 or TgNd9 (Fig. 1e). In contrast, rhoptry secretion was significantly impaired by the loss of TgNd6 or TgNd9 as revealed by quantifying the release of rhoptry protein ROP1 into host cells using immunofluorescence assays (IFA) [START_REF] Lamarque | Plasticity and redundancy among AMA-RON pairs ensure host cell entry of Toxoplasma parasites[END_REF] (Extended Data Fig. 3h), or the delivery of Cre recombinase fused to the rhoptry protein toxofilin into the nucleus of a suitable reporter cell [START_REF] Koshy | Toxoplasma secreting Cre recombinase for analysis of host-parasite interactions[END_REF] (Fig. 1f). This was not due to defects in rhoptry biogenesis, as the mutants showed normal organelle formation and apical positioning, as judged by IFA and EM (Extended Data Fig. 4).

Analysis by freeze fracture EM of the apex of TgNd6-iKD mutants showed no significant reduction in the presence of the apical rosette (Fig. 1g). In contrast, T. gondii depleted of TgNd9 displayed a strong decrease in cells with a rosette at the parasite apex (Fig. 1g). This last result link rosette formation with rhoptry secretion and support an evolutionarily conserved mechanism of regulated exocytosis in Alveolata.

To determine whether other Apicomplexa require nd genes for rhoptry function, we analysed Nd9 in Plasmodium falciparum, the causative agent of the deadliest form of malaria. We confirmed that P. falciparum intracellular merozoites possess a fusion rosette of 8 IMPs (Fig. 2a). As nd9 is also predicted to have a fitness cost in P. falciparum [START_REF] Koshy | Toxoplasma secreting Cre recombinase for analysis of host-parasite interactions[END_REF] , we used the rapamycin-inducible dimerizable Cre recombinase (DiCre) system [START_REF] Collins | Robust inducible Cre recombinase activity in the human malaria parasite Plasmodium falciparum enables efficient gene deletion within a single asexual erythrocytic growth cycle[END_REF] to conditionally excise the low expressed Pfnd9 gene (PF3D7_1232700) (Extended Data Fig. 5a-d). DiCre mediated ablation of Pfnd9 resulted in substantial reduction in parasite proliferation (Fig. 2b), which was due to the inability of Pfnd9-iKO mutants to reinvade host cells while their intracellular development and egress were unaffected (Fig. 2b,c; Extended Data Fig. 5e). As in T. gondii, microneme secretion was unaltered in Pfnd9-iKO parasites (Fig. 2d) but rhoptry secretion was affected (Fig. 2e).

To gain a more complete understanding of the molecular composition of the rhoptry secretion machinery, we searched for Nd interacting proteins in T. gondii. TgNd9 displays two Armadillo repeats while TgNd6 shows homology with GDP/GTP exchange factors (GEFs) known to activate GTPases (Fig. 3a). We used TgNd9 for immunoprecipitation (IP) experiments as we found TgNd6 to be largely insoluble (Extended data Fig. 2g). Mass spectrometry analysis revealed robust interaction of TgNd9 with TgNd6 and with TgFER2 (TGGT1_260470), a member of the ferlin calcium sensor family, known to be essential for Toxoplasma rhoptry secretion [START_REF] Coleman | A Member of the Ferlin Calcium Sensor Family Is Essential for Toxoplasma gondii Rhoptry Secretion[END_REF] (Fig. 3a and Supplementary Table 2). The TgNd9 IP also enriched TGGT1_222660, a protein harboring Armadillo repeats and Leucine Rich Repeats, named hereafter TgNdP1 (Nd Partner 1) and TGGT1_316730 (TgNdP2: Nd Partner 2), a protein with a C2 calcium lipid binding domain (Fig. 3a). Both genes are broadly shared among Alveolata (Extended Data Fig. 1c,d and Supplementary Data 1) and predicted to be fitness-conferring in Toxoplasma 31 and Plasmodium [START_REF] Zhang | Uncovering the essential genes of the human malaria parasite Plasmodium falciparum by saturation mutagenesis[END_REF] . In contrast, TgNd9-IP interactor TGGT1_277840, a GTPase, is restricted to Apicomplexa and Dinoflagellata, and TGGT1_253570 is only found in the apicomplexan subgroup Coccidia. Importantly, all these proteins are also recovered when using reverse IP with tagged-TgNdP1 protein (Supplementary Table 3).

To learn more about the new conserved partners of TgNd9, we generated T. gondii lines in which TgNdP1 or TgNdP2 was tagged by an epitope and could be ablated conditionally (Extended Data Fig. 6). Both proteins appear as punctate cytoplasmic staining (Fig. 3b) but TgNdP2-like TgNd6-also appears as a dot at the apical tip of the parasite, although consistently with lower intensity. Depletion of TgNdP1 or TgNdP2 resulted in a profound growth defect (Extended Data Fig. 7a) that we linked to impairment of host cell invasion and rhoptry secretion (Fig. 3c,d and Extended Data Fig. 7). Again, loss of rhoptry secretion went hand-in-hand with loss of the rosette in Tgndp1-iKD and Tgndp2-iKD parasites (Fig. 3e).

To further validate our conservation data and broaden our discoveries back to Ciliata, we generated knockouts for orthologues of both NdP1 (TTHERM_01287970, Ttn∆ndp1) and NdP2 (TTHERM_00498010; Ttn∆ndp2) in the free-living Ciliata Tetrahymena thermophila (Extended Data Fig. 8a,b). The homologous organelles to Paramecium trichocysts in Tetrahymena are called mucocysts and are non-essential for laboratory growth. We found that Ttn∆ndp1 and Ttn∆ndp2 cells were defective in mucocyst secretion, which was triggered by exposure to dibucaine [START_REF] Briguglio | Lysosomal sorting receptors are essential for secretory granule biogenesis in Tetrahymena[END_REF] (Fig. 3f). We further showed that the impairment of exocytosis was not due to defects in mucocyst biogenesis, given that mucocyst maturation (as measured by processing of mucocyst pro-proteins) and trafficking (monitored by IFA) remained unaltered (Extended Data Fig. 8c,d). Taken together, we identified a complex of proteins essential for organellar exocytosis and rosette assembly, conserved across Alveolata.

The exact position of the rosette relative to the apical tip of the rhoptry and the enigmatic AV remained elusive, since freeze-fracture technique-used to image the rosette on the membrane-does not capture the internal structures at the same time. To overcome this limitation, we imaged the Toxoplasma apex by cryo-electron tomography (cryo-ET)-a technique combining the advantages of 3D imaging with molecular resolution to reveal ultrastructure in situ in its native biological context. We were able to simultaneously visualize and define three linked elements -1) the rosette (dark blue), 2) the AV (magenta), and 3) the apical tip (cyan) of the rhoptry (orange) (Fig. 4a andb, Extended Fig. 9). The rosette showed an 8-fold rotational symmetry around a central axis (Fig. 4c andd) and extended under the parasite plasma membrane (light blue) to interact with the AV (Fig. 4c). Thus, the rosette is tightly sandwiched between the AV and the plasma membrane and extensively interacts with both membranes. The AV in turn sits over the rhoptry tip (Fig. 4a andb). The contiguity of all elements from the rhoptry tip to the plasma membrane supports the idea that the rosette and the AV are integral part of the rhoptry secretion machinery. Interestingly, the connection between AV and rosette is sometimes observed even in the absence of a docked rhoptry (Fig. 4e) suggesting that they assemble independently of rhoptry docking. Supporting this hypothesis, when we imaged the Toxoplasma ARO mutant in which rhoptries fail to dock and are dispersed in the cytoplasm [START_REF] Mueller | The Toxoplasma protein ARO mediates the apical positioning of rhoptry organelles, a prerequisite for host cell invasion[END_REF] , both the rosette (seen by freeze-fracture; Fig. 4f) and the AV (seen by EM; Fig. 4g) were still present at the apex of the parasite. Altogether, these results positioned the rosette and AV at the heart of the exocytic machinery in Apicomplexa and implied that rhoptries do not make contact and fuse directly with the plasma membrane.

Our work breaks ground on the molecular and structural mechanisms for rhoptry exocytosis in Apicomplexa. We defined the apical rosette of IMPs as the site for rhoptry exocytosis in Apicomplexa, and we characterized an Alveolata-specific Nd complex necessary for the assembly of the rosette. In Paramecium, physiological studies predicted PtNd6 to be active at the plasma membrane [START_REF] Lefort-Tran | Control of exocytotic processes: cytological and physiological studies of trichocyst mutants in Paramecium tetraurelia[END_REF] while PtNd9 appeared to be a diffusible cytoplasmic component interacting with both trichocyst and plasma membranes [START_REF] Beisson | Control of membrane fusion in exocytosis. Physiological studies on a Paramecium mutant blocked in the final step of the trichocyst extrusion process[END_REF][START_REF] Bonnemain | Interactions between genes involved in exocytotic membrane fusion in Paramecium[END_REF] .

However, both the localization and identity of partners in Ciliata remained unknown. We localized TgNd9 to the cytoplasm and TgNd6 to the site of exocytosis in Toxoplasma. We found both proteins to form a complex that included TgNdP1 and TgNdP2, which we demonstrated to be essential for organelle exocytosis in both Ciliata and Apicomplexa. This complex further includes proteins with C2 domains (TgFER2 and TgNdP2), a homolog of the membrane fusion Ferlin family (FER2) [START_REF] Coleman | A Member of the Ferlin Calcium Sensor Family Is Essential for Toxoplasma gondii Rhoptry Secretion[END_REF] , a GTPase (TGME49_277840), and a putative GEF protein (TgNd6). These proteins and their domains yield a regulatory model in which calcium signaling and nucleotide binding and hydrolysis constitute key steps that control rosette assembly and/or organelle discharge (more discussion in Supplementary text).

In this study, we also shed new light on the enigmatic AV. Described in Toxoplasma [START_REF] Porchet-Hennere | Are rhoptries of Coccidia really extrusomes?[END_REF][START_REF] Paredes-Santos | Dynamics and 3D organization of secretory organelles of Toxoplasma gondii[END_REF] , it is also visible in early micrographs from other apicomplexan parasites [START_REF] Dubremetz | Freeze fracture study of the pellicle of an eimerian sporozoite (Protozoa, Coccidia)[END_REF][START_REF] Varghese | Fine structure of the endogenous stages of Eimeria labbeana. I. The first generation merozoites[END_REF][START_REF] Sheffield | Electron microscope study of the proliferative form of Besnoitia jellisoni[END_REF] (Extended Data Fig. 10). We show that the AV is on one side tightly connected with the plasma membrane via the rosette, and from the other sits on the tip of the rhoptry, precluding a direct link and fusion between the rhoptry and the plasma membrane. In Ciliata, where no comparable AV is present, the docking and fusion of the trichocysts to the plasma membrane shapes the rosette. Diverging from this scenario, we also showed that the docking of the rhoptries in Toxoplasma is dispensable for rosette formation (Fig. 4e-g), which suggests that the assembly of the rosette in Toxoplasma might be instead induced by docking of the AV.

While Paramecium trichocysts are discharged into the environment to thwart predators, apicomplexan rhoptries translocate their contents directly into the cytoplasm of host organisms to infect and parasitize. The difference in terms of presence or absence of the AV (Fig. 4h) suggests that the AV is an adaptation in Apicomplexa for parasitism and cell invasion. Its presence may reflect additional complexity of the secretory machinery in apicomplexans, in which exocytosis must be coupled with injection of rhoptry content through the barrier of a host cell membrane. In support of this hypothesis, a similar vesicle is present at the apex of Perkinsus marinus (previously named Dermocystidium marinum) [START_REF] Perkins | Zoospores of the Oyster Pathogen, Dermocystidium marinum. I. Fine Structure of the Conoid and Other Sporozoan-Like Organelles[END_REF] .

This organism possesses rhoptries and invades and parasitizes the cells of oysters.

Phylogenetically, Perkinsus falls between Dinoflagellata and Apicomplexa and is viewed as a basal taxon and example of early adaptation to parasitism.

The rosette and the set of Nd proteins highlight a common ancestry for the fusion machinery linked to secretory organelles in two groups of protists that diverged hundreds of millions of years ago and have adopted radically different lifestyles. The architecture and molecular composition of the rhoptry system that enables the intracellular parasitism of Apicomplexa is now revealed. Future studies combining high resolution structural biology approaches with the genetics and cell biology of Toxoplasma will allow a full understanding of how this fascinating mechanism unfolds and is regulated. Such an understanding may find application in designing strategies against malaria, cryptosporidiosis and toxoplasmosis.

Methods

Parasite Immunofluorescence microscopy

Immunofluorescence assays (IFAs) on intracellular T. gondii parasites were conducted as previously described [START_REF] El Hajj | Molecular signals in the trafficking of Toxoplasma gondii protein MIC3 to the micronemes[END_REF] . Briefly, cell monolayers were washed and fixed in 4% paraformaldehyde (PFA) in Phosphate Saline Buffer (PBS) for 20 min. After three washes with PBS, cells were permeabilized with 0.1% (v/v) Triton X-100 or with saponin 0.1% (v/v) (for invading parasites) in PBS for 5 min, blocked with 10% fetal bovine serum in PBS (PFBS) for 45 min, incubated with primary Abs diluted in 2% PFBS, washed three times, and then incubated with secondary antibody. The coverslips were mounted onto microscope slides using Immunomount (Calbiochem).

For IFAs on P. falciparum, thin blood smears of highly synchronized DMSO and rapamycintreated PfNd9-iKO schizonts were air-dried, fixed in 4% (w/v) PFA and permeabilized in 0.1% (v/v) Triton X-100. After blocking in 1.5% BSA, samples were probed for 1 h with the suitable antibodies: rabbit anti-PfAMA1 47 antibody (1:1000); mouse anti-PfMSP1 48 antibody (1:1000); mouse anti-PfRAP2 49 antibodies (1:500). The secondary antibodies used were Alexa Fluor 488 and 594-conjugated antibodies against mouse or rabbit IgG (highly crossadsorbed) both diluted 1:4000 (Molecular Probes). Samples were then counterstained with Hoechst and slides mounted with Vectashield® antifade mounting medium.

Except when specified, observations were performed with a Zeiss Axioimager Z1 epifluorescence microscope equipped with a Zeiss Axiocam MRm CCD camera and 100X/1.4 Oil Plan Apochromat objective. Images were processed using Zen Blue 2.3 pro (Zeiss) software. Imaging of TgNd6, TgNd9, TgNdP1 and TgNdP2 co-localization with rhoptry protein TgARO (Fig. 1 and Fig. 3) was performed on a ZEISS confocal LSM880, equipped with an Airyscan detector and a 63X/1.4 Oil Plan Apochromat objective. Zen Black (Zeiss) was used for image airyscan processing. Z-stack images and z-projection images were denoised, adjusted in brightness and contrast, and colored with the program Fiji [START_REF] Schindelin | Fiji: an open-source platform for biological-image analysis[END_REF] .

Adjustments for brightness and contrast were applied uniformly on the entire images and when required, matching pairs of images were recorded with the same exposure time and processed identically. All optical images were collected at the Montpellier Ressources Imagerie facility of the University of Montpellier (MRI, www.mri.cnrs.fr).

Invasion assays

Toxoplasma plaque assays, replication assays, and two-color invasion assays were performed as previously described [START_REF] Lamarque | Plasticity and redundancy among AMA-RON pairs ensure host cell entry of Toxoplasma parasites[END_REF][START_REF] Cerede | Synergistic role of micronemal proteins in Toxoplasma gondii virulence[END_REF] . Briefly, to synchronize invasion, freshly egressed tachyzoites (5 × 10 6 ), pre-treated for 48 h ± ATc (Tgnd9-iKD and Tgndp1-iKD) or 24 h ± IAA (Tgnd6-iKD and Tgndp2-iKD), were harvested and settled on ice for 20 min on HFF monolayer grown on coverslips in 24-well plates. Invasion was allowed for 5 min at 38 °C and stopped by fixation with 4% PFA in Hank's Balanced Salt Solution (HBSS) for 20 min at room temperature. To detect extracellular parasites, immuno-detection was performed with the mouse mAb T4 1E5 anti-SAG1 antibody (dilution 1:1000) in 2% FCS/HBSS, without previous permeabilization.

After permeabilization with 0.01% saponin for 15 min, a second IFA was performed using rabbit anti-ROP1 antibody (dilution 1:1000) to label the parasitophorous vacuole of intracellular parasites. Extracellular and intracellular parasites were counted by microscopic examination of at least 20 fields per coverslip (n=3 coverslips). Graphs show the mean of three independent invasion assays.

Microneme secretion assay

Microneme secretion in T. gondii was assayed by monitoring the release into the culture medium. Freshly egressed T. gondii tachyzoites, pre-treated for 48 h ± ATc (Tgnd9-iKD and Tgndp1-iKD) or 24 h ± IAA (Tgnd6-iKD and Tgndp2-iKD), were harvested by centrifugation at 600 g, and washed twice in intracellular buffer (5 mM NaCl, 142 mM KCl, 1 mM MgCl2, 2 mM EGTA, 5.6 mM glucose and 25 mM HEPES, pH 7.2), prewarmed to 37°C. Parasites were resuspended in DMEM (supplemented with 2 mM glutamine) ± propranolol 500 µM, and incubated at 37°C for 20 min to induce microneme secretion. Parasites were pelleted at 1000 g for 5 min at 4°C, washed once in PBS and stored at -20°C. Supernatants were centrifuged at 2000 g for 5 min, at 4°C and used as ESA (excreted/secreted antigen). Pellets and ESA samples were analysed for micronemal protein (AMA1) by Western blot.

Microneme secretion in P. falciparum was assayed by monitoring the surface translocation of AMA1. Highly synchronized DMSO and rapamycin-treated Pfnd9-iKO schizonts were exposed to 10 µM E64 to block egress. IFAs were then performed with anti-PfAMA1 antibodies to analyse AMA1 translocation to the merozoite surface.

Rhoptry secretion assays

To evaluate the efficiency of rhoptry secretion in T. gondii, we expressed in the Tgnd-iKD and Tgndp-iKD lines a rhoptry secretion reporter protein consisting of the rhoptry protein toxofilin fused with Cre-recombinase, a nuclear localization signal (NLS), and a myc tag, collectively called Secreted Cre, Epitope-tagged (SeCrEt) [START_REF] Koshy | Toxoplasma secreting Cre recombinase for analysis of host-parasite interactions[END_REF] . The reporter strains generated were called Tgnd9-iKD_SeCrEtUPRT, Tgndp1-iKD_SeCrEtUPRT, and Tgnd6-iKD_SeCrEtHXGPRT. Ds Red cells, constitutively expressing DsRed and able to switch to eGFP expression upon Cre-mediated recombination, were used as reporter cells [START_REF] Koshy | Toxoplasma secreting Cre recombinase for analysis of host-parasite interactions[END_REF] . DsRed Cells were maintained at < 50% confluence (regardless of the flask size) and plated the day before parasite infection, at a density of 2 x 10 5 cells /ml (in T25 flasks). Tachyzoites pretreated for 48 h ± ATc (Tgnd9-iKD and Tgndp1-iKD) or 24 h ± IAA (Tgnd6-iKD) were collected and used to infect the DsRed cells at a multiplicity of infection (MOI) of 3. Additionally, rhoptry secretion has been also quantified by classical e-vacuole assay [START_REF] Lamarque | Plasticity and redundancy among AMA-RON pairs ensure host cell entry of Toxoplasma parasites[END_REF] .

Freshly egressed parasites, pre-treated for 48 h ± ATc (Tgnd9-iKD and Tgndp1-iKD) or 24 h ± IAA (Tgnd6-iKD), were preincubated with 1 µM of cytochalasin D (cytD) for 10 min and then incubated with HFF cells in the presence of cytD for 15 min. IFAs were then performed with anti-ROP1 (rhoptry secretion) and anti-SAG1 (parasite surface), and the number of ROP1 stainings per field was determined by microscopic examination of at least 20 fields per coverslip (n = 3 coverslips).

To quantify rhoptry secretion in P. falciparum, the same amount of DMSO and rapamycintreated Pfnd9-iKO purified schizonts were arrested with 1.5 µM C2 for 4 h before being washed twice in order to allow them to egress. Parasites were then incubated for 30 min in complete medium with 1 µM cytD in the presence of red blood cells (RBCs). IFAs were then performed with anti-PfRAP2 antibodies to visualise rhoptry secretion events ('spits' of RAP2 export into the RBC). The number of secretion events were counted over the total of RBCs by microscopic examination of 11 fields (~3000 events analysed).

Freeze-fracture 14

Cells used for the freeze-fracture were harvested, pelleted and fixed in 2.5% glutaraldehyde in 0.1 M phosphate buffer at room temperature for 2 h. Following primary fixation, the samples were rinsed in the same buffer, and kept overnight in 30% glycerol 0.1 M phosphate solution. The cells were then quickly frozen by immersion in liquid nitrogen under vacuum.

The frozen samples were fractured in a BAL-TEC BAF 060 apparatus; subsequently the fracture face was shadowed by evaporating platinum 45° (3.2nm) and Carbon 90° (25nm).

The replicas were then washed in 6.5% sodium hypochlorite, rinsed in a chloroform (2/3) -(1/3) solution, rinsed in distilled water and mounted on copper grids. In order to interpret the replicas obtained in freeze-fracture technique one must keep in mind that the portion of the lipid bilayer associated with the exterior of the cell is termed the E-face, or extracellular face.

The portion associated with the interior of the cell is termed the P-face, or protoplasmic face [START_REF] Branton | Freeze-etching nomenclature[END_REF] .

In vitro growth assay of P. falciparum parasite asexual development

The growth capability of Pfnd9-iKO mutant parasites was assessed by comparing it with the p230p DiCre parental line [START_REF] Knuepfer | Generating conditional gene knockouts in Plasmodium -a toolkit to produce stable DiCre recombinaseexpressing parasite lines using CRISPR/Cas9[END_REF] ± rapamycin in biological triplicates. DMSO (vehicle control) and rapamycin-treated cultures were synchronized at ring stages by sorbitol synchronization and parasitaemia adjusted to 0.2% to follow growth over 3 cycles. Parasites around 30 hpi were collected at each cycle to determine parasitaemia by counting Giemsa-stained parasites on at least 1000 RBCs per culture.

P. falciparum induced egress and time-lapse microscopy

P. falciparum egress was imaged using C2 compound to tightly synchronize egress. DMSO and rapamycin-treated Pfnd9-iKO parasites were highly synchronized and blocked 40 hpi (mature schizonts) with 1.5 µM C2 compound to prevent egress. Four hours later parasites were washed twice with warm RPMI 1640 medium (Gibco), to remove C2, and placed into a 35 mm Dish (MatTek). The parasites suspension was sealed by adhering a 22 x 22 mm square coverslip to the Dish and the preparation was introduced on a temperature-controlled microscope stage at 37°C. Bright field images were collected 5 min after washing off the C2 at 1 s intervals over a total of 15 min using a ZEISS AxioObserver Microscope fitted with a coolsnap HQ2 digital camera and 63X/1.4 Oil Plan Apochromat objective. Images were exported to MOV movies using ZEN 2 (blue edition) software. Number of egress events in 15 min were normalized as the percentage compared to 100% in the control.

Tetrahymena dibucaine assay to quantify mucocyst secretion

Tetrahymena cells were grown to stationary phase (10 6 cells/ml) in 25 ml SPP (2% proteose peptone, 0.1% yeast extract, 0.2% dextrose and 0.003% ferric-EDTA supplemented with 250 μg/ml penicillin G, 250 μg/ml streptomycin sulfate, and 0.25 μg/ml amphotericin B fungizone) for 48 h, and then concentrated by centrifugation into a lose 1.5ml pellet. Cells were stimulated with 2.5 mM dibucaine, vigorously mixed for 30 s and diluted to 15 ml with 10 mM Hepes, pH 7.5, and 5 mM CaCl2. After gently mixing, the culture was centrifuged at 1,200 g for 2 min, resulting in the formation of a cell pellet/flocculent bilayer. Quantification of mucocysts secretion has been done by weighing the flocculent layer overlying the pellet of cells.

Transmission electron microscopy

Tgnd9-iKD and TgARO-iKD, together with the Δku80-TATi parental strain, were treated with ATc for 72 h. Tgnd6-iKD parasites, and Δku80-Tir1 parental strain were treated with IAA for 24 h. Extracellular parasites were collected in the culture medium after natural egress and fixed by adding and equal volume of phosphate buffer 0.1 M containing 5% of glutaraldehyde for one hour at RT. Parasites were then centrifuged and resuspended in 1 ml of fresh buffer with 2.5% glutaraldehyde for 2 h before being kept at 4°C until further processing. Each of the following steps were performed in suspension followed by centrifugation steps in a tabletop microcentrifuge. Sample were post-fixed with 1% OsO4 and 1.5% potassium ferricyanide in 0.1 M phosphate buffer for 1 hour at RT, washed in water and then incubated in 2% uranyl acetate in water overnight at 4°C. Tachyzoites were then dehydrated in growing concentration of acetonitrile, followed by impregnation in Epon118: acetonitrile 50:50 for 2 hours, 90:10 for 2 additional hours and then overnight in pure epon.

Pellets were polymerized in fresh Epon for 48 h at 60°C. 70 nm ultrathin sections were cut with a Leica ultracut (Leica microsystems), counterstained with uranyl acetate and lead citrate.

Immunoelectron microscopy

Tgnd6-HA3 parasites, infected fibroblast monolayers were trypsinized and fixed with equal volume of 8% formaldehyde (FA) in phosphate buffer overnight at 4°C and resuspended in 4% fresh FA until further processing. Cells were then incubated with 0.1 % glycine in phosphate buffer, pelleted and embedded in 12% gelatin, cut in small blocks (< 1 mm) and infused in 2.3 M sucrose on a rotating wheel for 24 h at 4°C. Gelatin blocks were mounted on specimen pins and frozen in liquid nitrogen. Cryo-sectioning was performed on a Leica UC7 cryo-ultramicrotome, 70 nm cryosections were picked-up in a 1:1 mixture of 2.3 M sucrose and 2% methylcellulose in water and stored at 4°C. For on-grid immunodetection, grids were floated on 2% gelatin in PBS for 30 min at 37°C to remove methylcellulose/sucrose mixture, then blocked with 1% skin-fish gelatin (SFG, Sigma) in PBS for 5 min. Successive immunolabeling steps were performed on drops as follows: 1) rat monoclonal anti-HA antibodies (clone 3F10, Roche) in 1% BSA, 2) rabbit polyclonal anti-rat IgG antibody (Sigma) in 1% BSA, 3) Protein A-gold (UMC Utrecht) in 1% BSA. Four washes (2 min each) with 0.1% BSA were performed between steps. After Protein A treatment, grids were washed four times for 2 min each with PBS, fixed 5 min in 1% glutaraldehyde in water, and washed six times for 2 min each with distilled water. Grids were then incubated with 2% methylcellulose: 4% uranyl acetate 9:1 on ice in the dark for 15 min, picked-up on a wire loop and air-dried.

All chemicals were from Electron Microscopy Sciences (USA), solvents were from Sigma.

Observations and image acquisition were performed on a Jeol 1200 EXII transmission electron microscope at the Electron Microscopy Platform of the University of Montpellier (MEA; http://mea.edu.umontpellier.fr). Transmission electron microscopy images were processed with Fiji for contrast optimization and the Image J plugin was used to make the EM panels.

Cryo-electron-tomography (Cryo-ET)

Freshly isolated Toxoplasma gondii cells were suspended in HBSS along with fiducials (10 nm colloidal gold from Ted Pella for alignment of tilt series). A 4 µl drop of the suspension was applied onto EM grids, excess liquid blotted away, and plunge frozen in a liquid ethane/propane mixture (pre-cooled using liquid nitrogen) using a EM GP2 automatic plunger (Leica Microsystems, Wetzlar, Germany) [START_REF] Iancu | Electron cryotomography sample preparation using the Vitrobot[END_REF] . The blotting chamber was set to 95-100% relative humidity at 37°C and blotting was done either from the sample side of the grid or from the back using Whatman filter paper #1. Plunge-frozen grids were subsequently loaded into autogrid cartridges (ThermoFisher). EM cartridges containing frozen grids were stored in liquid nitrogen and maintained at ≤-170°C throughout storage, transfer and cryo-ET imaging.

Cryo-ET data collection was performed on a ThermoFisher Krios G3i 300 keV field emission gun cryo-TEM equipped with a 6k x 4k K3 direct electron detector (Gatan, Inc.) at the Beckman Center for Cryo-Electron Microscopy in the Singh Center for Nanotechnology, University of Pennsylvania. The camera was operated in electron counting mode to enable motion correction. An energy filter (Gatan Imaging Filter, Gatan, Inc.) with a slit width of 20 eV was used to increase the contrast of the projection images. Additionally, a Volta phase plate was used to boost the image contrast at defoci of negative 2-3 µm. A magnification of 33,000X with corresponding pixel sizes of 2.65 Å was used for imaging. SerialEM software [START_REF] Mastronarde | Automated electron microscope tomography using robust prediction of specimen movements[END_REF] was used for all imaging. Cells were first assessed at lower magnifications for suitability of ice thickness and outer membrane integrity (the parasites sometimes tend to vesiculate their outer membrane possibly in response to blotting). Once the target cells were identified and marked, anchor maps were used to revisit these locations and collect tilt-series in an automated fashion. Each tilt-series was collected from negative 60° to positive 60° with an increment of 2° in an automated fashion using the low dose functions of tracking and focusing. The cumulative dose of each tilt-series ranged between 100 and 150 e -/Å 2 . Once acquired, tilt-series were binned twice or four times before aligned using the 10 nm colloidal gold as fiducials and reconstructed into tomograms by our in-house automated computation pipeline utilizing the IMOD software package [START_REF] Kremer | Computer visualization of threedimensional image data using IMOD[END_REF] .. For presentation purposes, tomograms were favorably oriented in three dimensions before averaging a few slices around the slice of interest (to enhance contrast) using the slicer window in IMOD. In a few cases (Figs. 4b-e, contrast was further enhanced using the non-linear anisotropic diffusion filter in IMOD.

Immunopurification and mass spectrometry analysis

Immunopurification was performed using anti HA magnetic beads (Pierce R88836) as previously described [START_REF] Lebrun | The rhoptry neck protein RON4 relocalizes at the moving junction during Toxoplasma gondii invasion[END_REF] . Purified proteins were loaded on a SDS-PAGE and digested in gel (2 bands per sample) as previously described [START_REF] Skorupa | Angiogenin induces modifications in the astrocyte secretome: relevance to amyotrophic lateral sclerosis[END_REF] . Samples were loaded onto a 25 cm reversed phase column (75 mm inner diameter, Acclaim Pepmap 100® C18, Thermo Fisher Scientific) and separated with an Ultimate 3000 RSLC system (Thermo Fisher Scientific) coupled to a Q Exactive HFX (Thermo Fisher Scientific). MS/MS analyses were performed in a datadependent mode. Full scans (375 -1,500 m/z) were acquired in the Orbitrap mass analyzer with a resolution of 60,000 at 200 m/z. For the full scans, 3e6 ions were accumulated within a maximum injection time of 60 ms. The twelve most intense ions with charge states ≥ 2 were sequentially isolated (1e5) with a maximum injection time of 45 ms and fragmented by HCD (Higher-energy collisional dissociation) in the collision cell (normalized collision energy of 28%) and detected in the Orbitrap analyzer at a resolution of 30,000.

Raw spectra were processed using the MaxQuant 59 using standard parameters with label-free quantification (LFQ) and match between runs [START_REF] Cox | Andromeda: a peptide search engine integrated into the MaxQuant environment[END_REF] . MS/MS spectra were matched against the UniProt Reference proteomes of T. gondii and Human (respectively Proteome ID UP000001529 and UP000005640) and 250 frequently observed contaminants as well as reversed sequences of all entries (MaxQuant contaminant database). Statistical analysis were done using Perseus on LFQ data [START_REF] Tyanova | The Perseus computational platform for comprehensive analysis of (prote)omics data[END_REF] .

Amino

  24 hours post-invasion (hpi) infected DsRed cells were trypsinized and examined by fluorescenceactivated cell sorting (FACS) to assess the percentage of Ds-Red (rhoptry secretion impairment) and GFP (successful rhoptry secretion) expressing cells. Number of GFP positive cells were normalized as the percentage compared to 100% in the control (-IAA or -ATc).

  acid sequence alignments and phylogenetic analyses, cell and parasite culture, parasite cloning strategies, parasite transfections, parasite immunoblots, T. gondii plaque assays, T. gondii intracellular growth assay, T. gondii immunofluorescence-based induced egress assay, T. gondii conoid extrusion assay, T. gondii gliding assays, T. gondii attachment assay, Ciliata culture conditions, Ciliata biolistic transformation, generation of Tetrahymena knockout strain, Ciliata immunoblots, Ciliata immunofluorescence microscopy, statistical analysis, reagents and antibodies are found in Supplementary Methods.

Fig. 2 |

 2 Fig. 2 | PfNd9 is essential for rhoptry secretion in P. falciparum.

Acknowledgements

We thank Sebastian Lourido for the pU6-Universal plasmid, Artur Scherf for anti-PfRAP2 antibodies, Soldati-Favre for providing us the anti-ARO antibodies and TgARO-iKD strain; Michael Blackman for antibodies (anti-PfAMA1 and anti-PfMSP1), pDC2-Cas9-hDHFRyFCU plasmid, and p230p DiCre strain; John Boothroyd for mAb CCL2 anti-TgAMA1; Ross Waller for RNG1-GFP plasmid; Anita Koshy for the toxofilin-Cre plasmid and Helen Blau's lab for the Cre reporter DSred cell line. We thank Oliver Billker for providing us with Compound 2. We thank Veronique Richard and Frank Godiard for their technical assistance on the electron microscopy service of the University of Montpellier. We are also grateful to Elodie Jublanc and the imaging facility MRI at the University of Montpellier, part of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04, «Investments for the future»), and Christophe Duperray of the MRI-Cytometry at the Institute for Regenerative Medicine and Biotherapy for their assistance and technical support. We would like to thank Julien Marcetteau for the graphical design of Fig. 1b and Fig. 4. We acknowledge the use of instruments at the Electron Microscopy Resource Laboratory and the Beckman Center for Cryo-Electron Microscopy at the University of Pennsylvania. Dr Maryse Lebrun is an INSERM researcher. This work was supported by the Laboratoire d'Excellence (LabEx) (ParaFrap ANR-11-LABX-0024), the ANR (ANR-16-CE15-0010-01), and the Fondation pour la Recherche Medicale (Equipe FRM DEQ20170336725) to M.L.; by the FACCTS (France and Chicago Collaborating in the Sciences) to A.P.T and M.L. ;by NIH GM105783 to A.P.T. ;by a David and Lucile Packard Fellowship for Science and Engineering (2019-69645) to Y.-W.C and by EMBO fellowship number ALTF 58-2018 to A.N.G..

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon request.

Materials and Correspondence

Correspondence and requests for materials should be addressed to M.L.

Author contributions

Competing interests

The authors declare no competing financial interests. Supplementary Information is available for this paper. Rhoptry exocytosis in Apicomplexa is one of the critical steps of host cell invasion and therefore fundamental for parasitism. In Apicomplexa, but not in Ciliata, an apical vesicle (AV) of unknown function is present between the tip of the rhoptry and the plasmalemma, plausibly involved in the injection of rhoptry proteins into the host cell. PM, plasma membrane; PVM, parasitophorous vacuole membrane; AS, alveolar sac [in Ciliata], which is homologous to the IMC (inner membrane complex) of Apicomplexa.


Exercise targeted on maximal lipid oxidation has long-term weight-reducing effects but improves insulin secretion and lipid oxidation before reducing fat mass E. Varlet-Marie, Jean-Frédéric Brun, J. Myzia, G. Bui, A. Chérif, E. Javernaud, L. Hammoudi, Jacques Mercier, E. Raynaud To cite this version: E. Varlet-Marie, Jean-Frédéric Brun, J. Myzia, G. Bui, A. Chérif, et al.. Exercise targeted on maximal lipid oxidation has long-term weight-reducing effects but improves insulin secretion and lipid oxidation before reducing fat mass. Science & Sports, In press, 10.1016/j.scispo.2021.06.004. hal-03654005