Xiaojiang Peng
email: xiaojiang.peng@inria.fr

Cordelia Schmid
email: cordelia.schmid@inria.fr

Multi-region two-stream R-CNN for action detection

Keywords: Action detection, faster R-CNN, multi-region CNNs, two stream R-CNN

We propose a multi-region two-stream R-CNN model for action detection in realistic videos. We start from frame-level action detection based on faster R-CNN [1], and make three contributions: (1) we show that a motion region proposal network generates high-quality proposals, which are complementary to those of an appearance region proposal network; (2) we show that stacking optical flow over several frames significantly improves frame-level action detection; and (3) we embed a multi-region scheme in the faster R-CNN model, which adds complementary information on body parts. We then link frame-level detections with the Viterbi algorithm, and temporally localize an action with the maximum subarray method. Experimental results on the UCF-Sports, J-HMDB and UCF101 action detection datasets show that our approach outperforms the state of the art with a significant margin in both frame-mAP and video-mAP.

Introduction

Action recognition in videos has many realistic applications such as surveillance, human computer interaction, and content-based retrieval. Most research efforts have concentrated on action classification [START_REF] Wang | Action recognition with improved trajectories[END_REF][START_REF] Jain | Representing videos using mid-level discriminative patches[END_REF][START_REF] Peng | Action recognition with stacked Fisher vectors[END_REF][START_REF] Wang | Action recognition with trajectory-pooled deepconvolutional descriptors[END_REF], where a class label is assigned to an entire video. However, given a video stream, actions occur at precise spatiotemporal extents. Action detection aims at determining these location, which has attracted increasing attention recently [START_REF] Tian | Spatiotemporal deformable part models for action detection[END_REF][START_REF] Wang | Video action detection with relational dynamicposelets[END_REF][8][START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. It is a challenging problem due to large intra-class variations, background clutter and in particular the large spatio-temporal search space.

Several previous works address only temporal localization [START_REF] Gaidon | Temporal localization of actions with actoms[END_REF][START_REF] Oneata | Efficient action localization with approximately normalized Fisher vectors[END_REF][START_REF] Escalera | Chalearn looking at people challenge 2014: Dataset and results[END_REF], i.e. they only provide the start and end time of an action. State-of-the-art results are obtained with a temporal sliding window and dense trajectory features [START_REF] Oneata | Efficient action localization with approximately normalized Fisher vectors[END_REF]. For spatio-temporal detection, several recent works extend 2D object detection models to 3D ones. For example, Tian et al. [START_REF] Tian | Spatiotemporal deformable part models for action detection[END_REF] extend the 2D deformable part model [START_REF] Felzenszwalb | A discriminatively trained, multiscale, deformable part model[END_REF] to a 3D deformable part model and Wang et al. [START_REF] Wang | Video action detection with relational dynamicposelets[END_REF] extend poselets [START_REF] Bourdev | Poselets: Body part detectors trained using 3D human pose annotations[END_REF] to a dynamic poselet model. More recent works first detect actions at a frame level by using Convolutional Neural Networks (CNNs) features and then either link them or track some selected detections to obtain video action detections [8,[START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. Thanks to the excellent performance of CNNs for object detection, these frame-level based approaches achieve state-of-the-art performance. This suggests that the quality of the frame-level action detections impacts directly the quality of action detection in videos.

Thus, a crucial point is how to improve the frame-level action detection. Weinzaepfel et al. [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF] improve frame-level action detection by using a better proposal algorithm, i.e. EdgeBoxes [START_REF] Zitnick | Edge boxes: Locating object proposals from edges[END_REF]. Gkioxari et al. [START_REF] Gkioxari | Contextual action recognition with R*CNN[END_REF] boost R-CNN based action detection in still images by adding contextual features. Indeed, these two approaches indicate two important issues for frame-level detection: 1) highquality proposals help CNNs to extract action representations precisely; and 2) the action representation is vital for detection.

In this paper we focus on the frame-level based action detection method, and aim to advance the state-of-the-art with respect to these two key aspects: frame-level action proposal and action representation. Frame-level action proposal. One of the bottleneck for object detection based on region proposals is the accurate localization of these proposals [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF][START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF][START_REF] Gidaris | Object detection via a multi-region and semantic segmentation-aware CNN model[END_REF]. To address this issue for action detection, we first evaluate three proposal methods for frame-level action localization on RGB data: selective search (SS) [START_REF] Uijlings | Selective search for object recognition[END_REF], EdgeBoxes (EB) [START_REF] Zitnick | Edge boxes: Locating object proposals from edges[END_REF], and region proposal network (RPN) [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF]. We show that the RPN approach on appearance information achieves consistently better results than the others with higher inter-section-over-union (IoU) score. Furthermore, we extend the appearance RPN to motion RPN trained on optical flow data. We observe that motion RPN obtains high quality proposals, which are shown to be complementary to appearance RPN. Action representation. Action representation is crucial for good performance, see for example [START_REF] Jhuang | Towards understanding action recognition[END_REF][START_REF] Chéron | P-CNN: pose-based CNN features for action recognition[END_REF]. Here, we propose an improved action representation inspired by the two-stream CNNs for action classification [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF] and multi-region CNNs [START_REF] Gidaris | Object detection via a multi-region and semantic segmentation-aware CNN model[END_REF]. First, we stack multiple frame optical flows for the faster R-CNN model which significantly improves the motion R-CNN. Second, we select multiple body regions (i.e., upper body, lower body and border region) for both appearance and motion R-CNN, which boosts the performance of frame-based action detection.

In summary, this paper introduces a multi-region two-stream R-CNN model for action detection with state-of-the-art results on UCF-Sports, J-HMDB and UCF101 datasets. Our contributions are as follows: (1) we introduce a motion RPN which generates high-quality proposals and is complementary to the appearance RPN. [START_REF] Wang | Action recognition with improved trajectories[END_REF] We show that stacking optical flows significantly improves frame-level detections. (3) We embed a multi-region scheme in the faster R-CNN model which is shown to improve the results.

The remained of this paper is organized as follows. In Sec.2, we review related work on action recognition and region CNNs. We introduce the two-stream R-CNN with the motion RPN and stacked optical flows in Sec.3. Our multi-region embedded R-CNN is described in Sec.4 and the temporal linking and localization in Sec.5. We present experimental results in Sec.6.

Related work

Action recognition and Convolutional Neural Networks (CNNs) have been extensively studied in recent years [START_REF] Aggarwal | Human activity analysis: A review[END_REF][START_REF] Lecun | Deep learning[END_REF]. This section only covers the approaches directly related to our method. Action classification and detection. For action classification, most methods focus on how to represent the entire video [START_REF] Wang | Action recognition with improved trajectories[END_REF][START_REF] Jain | Representing videos using mid-level discriminative patches[END_REF][START_REF] Peng | Action recognition with stacked Fisher vectors[END_REF][START_REF] Wang | Action recognition with trajectory-pooled deepconvolutional descriptors[END_REF]. Popular video representations are bag-of-visual-words (BoW) [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] and its variants which aggregate local video features [START_REF] Wang | Action recognition with improved trajectories[END_REF], CNN representations [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF][START_REF] Karpathy | Largescale video classification with convolutional neural networks[END_REF], and slow feature representations [START_REF] Sun | DL-SFA: deeply-learned slow feature analysis for action recognition[END_REF]. Wang et al. [START_REF] Wang | Action recognition with improved trajectories[END_REF] use Fisher Vectors [START_REF] Perronnin | Improving the Fisher kernel for large-scale image classification[END_REF] and dense trajectories with motion compensation. Peng et al. [START_REF] Peng | Action recognition with stacked Fisher vectors[END_REF] combine this approach with stacked Fisher Vectors. Simonyan et al. [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF] design the two-stream CNNs based on RGB data and optical flow. Karpathy et al. [START_REF] Karpathy | Largescale video classification with convolutional neural networks[END_REF] explore several approaches for fusing information over time based on appearance CNN. Wang et al. [START_REF] Wang | Action recognition with trajectory-pooled deepconvolutional descriptors[END_REF] extract two-stream CNNs along dense trajectories.

For action detection, [START_REF] Gaidon | Temporal localization of actions with actoms[END_REF][START_REF] Oneata | Efficient action localization with approximately normalized Fisher vectors[END_REF][START_REF] Laptev | Retrieving actions in movies[END_REF][START_REF] Yuan | Discriminative subvolume search for efficient action detection[END_REF] use local features to represent actions and rely on a sliding window scheme for either temporal or spatio-temporal localization. Rodriguez et al. [START_REF] Rodriguez | Action MACH a spatio-temporal maximum average correlation height filter for action recognition[END_REF] and Derpanis et al. [START_REF] Derpanis | Efficient action spotting based on a spacetime oriented structure representation[END_REF] conduct global template matching. Tran et al. [START_REF] Tran | Video event detection: From subvolume localization to spatiotemporal path search[END_REF] use a BoW representation and implement the optimal spatio-temporal path for action detection. Tian et al. [START_REF] Tian | Spatiotemporal deformable part models for action detection[END_REF] extend the 2D deformable part model [START_REF] Felzenszwalb | A discriminatively trained, multiscale, deformable part model[END_REF] to 3D space-time volumes for action localization. Wang et al. [START_REF] Wang | Video action detection with relational dynamicposelets[END_REF] apply dynamic poselets and a sequential skeleton model to jointly detect actions and poses. Region CNN for detection. Region CNN (R-CNN) [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF] has achieved a significant improvement for object detection in static image. This approach first extracts region proposals using selective search [START_REF] Uijlings | Selective search for object recognition[END_REF] and rescales them to a fixed size, and then uses a standard CNN network [START_REF] Krizhevsky | ImageNet classification with deep convolutional neural networks[END_REF] to train and extract features. The features are subsequently fed into a SVM classifier with hard negative mining and a bounding box regressor. SPP-net improved it by removing the limitation of a fixed input size with a spatial pyramid pooling strategy [START_REF] He | Spatial pyramid pooling in deep convolutional networks for visual recognition[END_REF]. Fast R-CNN speeds up the R-CNN by introducing a RoI pooling scheme and training classifier and bounding box regressor simultaneously [START_REF] Girshick | Fast R-CNN[END_REF]. Faster R-CNN further accelerates the fast R-CNN by replacing the selective search proposal method with a region proposal network [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF]. Spyros et al. [START_REF] Gidaris | Object detection via a multi-region and semantic segmentation-aware CNN model[END_REF] added multi-region and segmentation-aware CNN features to make the R-CNN representation more discriminative. Inspired by R-CNN, Gkioxari and Malik [8] extract proposals by using the selective search method on RGB frames and then applied the original R-CNN on per frame RGB and optical flow data for frame-level action detection, and finally link detections by the Viterbi algorithm [START_REF] Viterbi | Error bounds for convolutional codes and an asymptotically optimum decoding algorithm[END_REF] to generate action tubes. Weinzaepfel et al. [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF] replaced the selective search method by EdgeBoxes [START_REF] Zitnick | Edge boxes: Locating object proposals from edges[END_REF] for proposal extraction, and performed tracking on selected frame-level detections. Our work differs from the above mentioned approaches in four ways: 1) we generate rich proposals from both RGB and optical flow data by using region proposal networks; 2) we use stacked optical flows to enhance the discriminative capacity of motion R-CNN; 3) we further improve the performance by embedding a multi-region scheme in faster R-CNN; and 4) we build an end-to-end multiregion two-stream CNN model for frame-level action detection.

3 End-to-end two-stream faster R-CNN Figure 1 gives an overview of our two-stream faster R-CNN (TS R-CNN) approach. Stacking optical flow has shown to be effective for CNN based action classification [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF]. We believe this can also be the case for R-CNN based action detection. Our TS R-CNN takes as input an RGB frame f t and several optical flow maps extracted for frame f t and its neighboring frames (we take half of the frames before time t and half of them after). The network then processes them with several convolutional and max pooling layers, independently in the appearance and the motion stream. For each stream, the last convolutional layer is fed into an appearance or motion region proposal network and a region of interest (RoI) pooling layer. Here we introduce a RoI fusion layer, which merges the proposals from both the appearance RPN and the motion RPN. Both the appearance and the motion RoI pooling layer take all the RoIs and perform max-pooling for each of them with a H × W grid. For each stream, these fixedlength feature vectors are then fed into a sequence of fully connected layers that finally branch into a softmax layer and a bounding box regressor. The final detection results from both streams can be combined by several methods which will be evaluated in Sec.6.3. Best performance is obtained by simply combining the softmax scores. Training and testing. We train each of the two-stream faster R-CNNs separately. For both streams, we fine-tune the VGG-16 model [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] pre-trained on the ImageNet dataset [START_REF] Russakovsky | ImageNet large scale visual recognition challenge[END_REF]. One frame optical flow data is transformed to a 3 channel image by stacking the x-component, the y-component and the magnitude of the flow as in [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. In case of multiple optical flow maps, where the input channel number is different from that of VGG-16 net, we just duplicate the VGG-16 filters of the first layer multiple times. We use the ground-truth bounding boxes of the middle frame for training. For testing, we first combine the learned appearance and motion R-CNN models into one model by adding a RoI fusion layer, see Figure 1. We then put frame-flow pairs to the end-to-end model and average the softmax scores from both streams as the final action region detection scores. The bounding box regressor is applied to corresponding RoIs of each stream (see the red and blue solid bars in Figure 1). The concatenation of these boxes is the final detection result. Evaluation of our action proposals. To show the quality of our motion RPN (RPN-m), we compare it to several other proposal methods. Figure 2 compares the recall over intersection-over-union (IoU) for different proposal methods described in the following. Selective search (SS) [START_REF] Uijlings | Selective search for object recognition[END_REF] generates regions by using a bottom-up grouping scheme with features from color, texture and box sizes. We keep the default setting and obtain 2k proposals. EdgeBoxes (EB) [START_REF] Zitnick | Edge boxes: Locating object proposals from edges[END_REF] are obtained based on the observation that the number of contours entirely contained in a bounding box is indicative of the objectness. Again we use the default setting and obtain 256 proposals. The RPN method first generates several anchor boxes for each pixel with multiple scales and ratios, and then scores and regresses them with the learned features. For training RPN, positive objectness labels are obtained for those anchors that have high IoU overlap with ground-truth boxes. For the comparison, we keep RPN 300 proposals and use one scale with a fixed minimum side of 600 pixels. We also extend the RPN method to optical flow and report results for single flow and stacked flows.

Figure 2 shows that RPN-a method consistently outperforms SS and EB, i.e. it obtains best results when using RGB frames. Interestingly, on UCF-Sports it obtains perfect detections (IoU=1) 25% of the time (i.e., recall=0.25 for IoU=1). For fair comparison with SS and EB (both are non-tuned methods for action datasets), we show the results of RPN pre-trained on ImageNet as RPN-ImageNet in Figure 2. It also consistently outperforms SS and EB on both datasets. Moreover, the motion RPN with a single frame optical flow also provides very good action proposals. They are better than RPN-a on UCF-Sports, but worse on J-HMDB. This can be explained by more significant motion occurring on UCF-Sports compared to J-HMDB, which contains a number of daily activities without significant motion, such as "brush hair", "pour" and "wave". The recall increases with 5 stacked flows (RPN-m5) and decreases with 10 stacked flows (RPN-m10). A possible explanation is that stacking optical flows makes the representation more discriminative, but that there is a saturation for a higher number of frames due to the non-aligned temporal boxes. Combining the proposals from both appearance and motion RPN achieves the best performance and outperforms SS and EB by a significant margin.

Multi-region two-stream faster R-CNN

The multi-region two-stream faster R-CNN (MR-TS R-CNN) architecture is illustrated in Figure 3. It is built on the two-stream faster R-CNN by embedding a multi-region generation layer between the RPNs and the RoI pooling layer. Given proposals from both appearance RPN and motion RPN, the multi-region layer generates 4 RoIs for each RPN proposal. We describe the 4 types of regions relevant for action representation in the following. Original regions are the original RPN proposals. A network along this channel is guided to capture the whole action region. The network is exactly the same as the TS R-CNN. The bounding box regressor is only applied on this channel. "Upper half " and "bottom half " regions are the upper and bottom halfs of the RPN proposals, see second and third rows in the multi-region layer in Figure 3. Instead of left/right/upper/bottom half regions used for objects in [START_REF] Gidaris | Object detection via a multi-region and semantic segmentation-aware CNN model[END_REF], we only use the upper/bottom half regions due to the mostly symmetric vertical structure of bodies in action videos. Networks based on these parts are not only robust w.r.t occlusions but also more discriminative for action categories for which body part features are dominant. For example, "golf" and "swing baseball" are easier to recognize by only the upper half region, while "climb stairs" and "kick ball" by only the bottom half region. "Border " regions are rectangular rings around the original proposals. Given a RPN proposal, we generate the inner box of a border region by scaling the proposal by a factor of 0.8 and the outer box by a factor of 1.5. For the appearance stream, a network along this channel is expected to jointly capture the appearance border of human and nearby objects which may be helpful for action recognition. For motion stream, this channel has high probability to focus on the motion boundary region which was demonstrated to be very useful for hand-crafted features [START_REF] Wang | Action recognition with improved trajectories[END_REF].

Training. The two-stream network for original regions is copied from the one presented in the previous section. For training the two-stream networks of the other regions, we fine-tune the network of the original regions separately for each region. In particular, we only tune the fully connected layers, and fix all the convolutional layers as well as the RPN to ensure that all the region networks share the same proposals. Regarding the "Border" region two-stream network, we introduce a mask-supported RoI pooling layer which sets the activations inside the inner box to zero similar to [START_REF] Dai | Convolutional feature masking for joint object and stuff segmentation[END_REF][START_REF] Gidaris | Object detection via a multi-region and semantic segmentation-aware CNN model[END_REF]. After training the region networks, we combine them by further training another softmax layer based on the softmax layers of multi-region two-stream networks, see Figure 3. Note that the multiregion R-CNNs share all the conv layers and hence the computation cost during testing increases only by a factor of 1.8.

Linking and temporal localization

Based on the above described method, we obtain frame-level action detections. In order to achieve video-level detection, we apply linking similar to [8] and temporal localization based on the maximum subarray algorithm [START_REF] Bentley | Programming pearls: algorithm design techniques[END_REF].

Given two regions R t and R t+1 from consecutive frames t and t + 1, we define the linking score for an action class c by

s c (R t , R t+1 ) = {s c (R t ) + s c (R t+1 ) + β ov(R t , R t+1 )} • ψ(ov), (1)
where s c (R i ) is the class score of region R i , ov is the intersection-over-union overlap of the two regions and β is a scalar. ψ(ov) is a threshold function defined by ψ(ov) = 1 if ov is larger than τ , ψ(ov) = 0 otherwise. We experimentally observe that our linking score is better than the one in [8] and more robust due to the additional overlap constraint. After computing all the linking scores of an action, we obtain video-level action detections by determining the optimal path iteratively with the Viterbi algorithm. We finally score a video-level action detection

R = [R 1 , R 2 , ..., R T ] by s c (R) = 1 T T i=1 s c (R i ).
In order to determine the temporal extent of an action detection within a video track, one can apply a sliding window approach with multiple temporal scales and strides as [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. Here we rely on an efficient maximum subarray method.

Given a video-level detection R, we aim to find a detection from frame s to frame e which satisfies the following objective,

s c (R (s,e) ) = argmax (s,e) { 1 L (s,e) e i=s s c (R i ) -λ |L (s,e) -L c | L c }, (2)
where L (s,e) is the track length and L c is the average duration of class c on the training set. We propose to approximately solve this objective by three steps:

(1) subtract from all the frame-level action scores the video-length action score s c (R), (2) find the maximum subarray of the subtracted array by using Kadane's algorithm [START_REF] Bentley | Programming pearls: algorithm design techniques[END_REF], (3) extend or shorten the optimal range to L c . Our solution searches the track only once. For each video-length action detection, we only keep the best extent as spatio-temporal detection. Note that the threes-step heuristic is an approximation to equation ( 2), and step (3) sets the length of the optimal tube from step (2) to the average length to avoid degenerate solutions.

Experiments

In this section, we first present the details of datasets and the evaluation metrics and describe the implementation details. We then evaluate our method comprehensively and compare to the state of the art.

Datasets and evaluation metrics

In our experiments, we evaluate action detection on three datasets: UCF-Sports, J-HMDB and UCF-101. We briefly review them in the following and present the metrics used for evaluation. UCF-Sports [START_REF] Rodriguez | Action MACH a spatio-temporal maximum average correlation height filter for action recognition[END_REF] contains 150 short videos of 10 different sport classes. Videos are truncated to the action and bounding boxes annotations are provided for all frames. We use the standard training and test split defined in [START_REF] Rodriguez | Action MACH a spatio-temporal maximum average correlation height filter for action recognition[END_REF]. J-HMDB [START_REF] Jhuang | Towards understanding action recognition[END_REF] consists of 928 videos for 21 different actions such as brush hair, swing baseball or jump. Video clips are restricted to the duration of the action. Each clip contains between 15 and 40 frames. Human silhouettes are annotated for all frames. The ground-truth bounding boxes are inferred from the silhouettes. There are 3 train/test splits and evaluation averages the results over the three splits. UCF-101 [START_REF] Soomro | UCF101: A dataset of 101 human actions classes from videos in the wild[END_REF] is dedicated to action classification with more than 13000 videos and 101 classes. For a subset of 24 labels and 3207 videos, the spatio-temporal extents of the actions are annotated. All experiments are performed on the first split only. In contrast to UCF-Sports and J-HMDB where the videos are truncated to the action, UCF-101 videos are longer and the localization is both spatial and temporal. Evaluation metrics. We use three metrics in our experiments: (i) frame-AP, the average precision of detection at the frame level as in [8]; (ii) video-AP, the average precision at the video level as in [8,[START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. We fix the IoU threshold to [0.2, 0.5] for frame-AP and video-AP measurement on the UCF-Sports and J-HMDB, and [0.05, 0.1, 0.2, 0.3] on UCF101.

Implementation details

We implement our method based on the Caffe open source toolbox1 . Optical flow is estimated using the online code from Brox et al. [START_REF] Brox | High accuracy optical flow estimation based on a theory for warping[END_REF]. For both appearance and motion R-CNN, we use the same setting except that motion R-CNN uses 128, 128, 128 as the mean data values. Similar to [START_REF] Girshick | Fast R-CNN[END_REF], we use a single sample, i.e. either a single image or a stacked optical flow map annotated with ground-truth boxes at every training iteration. When fine-tuning the VGG-16 model, we only update layers from conv3 1 and up as observed to be efficient in [START_REF] Girshick | Fast R-CNN[END_REF]. For action region proposal network training, we set the regions with IoU larger than 0.7 as positive regions, and the regions with IoU less than 0.3 as negative regions.

For the classification part of faster R-CNN, we use 256 proposals with a quarter of them as positive bounding boxes from the RPN, where the IoU of a positive box is larger than 0.5 and of a negative between 0.1 and 0.5. When training the two-stream R-CNN on UCF-Sports and J-HMDB, we initialize the learning rate to 10 -3 , decrease it to 10 -4 after 50K iterations, and stop training after 70K iterations. When training the multi-region two-stream R-CNN, we only fine-tune the fully-connected layers of the TS R-CNN model and set the learning rate to 10 -4 , change it to 10 -5 after 7K iterations, and stop after 10k iterations. We double the mentioned iterations on the UCF101 dataset empirically since it is a much larger dataset. The grid of RoI pooling layer is fixed to 7 × 7. The dropout rates of fully connected layers are set to 0.5 in all cases. The threshold τ of function ψ(ov) is fixed to 0.2 empirically.

Evaluation of multi-region two-stream faster R-CNN

In this section, we first evaluate our method for frame-level detection with respect to four aspects: RGB/flow stacking, stream combination, multi-scale training and testing and multi-region scheme. We then present the spatio-temporal detection at the video level and the action classification results based on detection. RGB and optical flow faster R-CNN with several frames. We compare appearance and motion faster R-CNN with one or multiple frames for framelevel detection (mean AP), see figure 4. For this evaluation, the training/testing scale is fixed to 600 which corresponds to the shorter side of an input image. We can observe that appearance R-CNN extracted for one frame (RGB-1) outperforms motion R-CNN extracted for one frame (Flow-1) on both UCF-Sport and J-HMDB. Increasing the number of frames for the appearance model (RGB-5) does not improve the performance. However, using 5 frames for flow significantly improves the performance for motion R-CNN, i.e. we gain 10.27% on UCF-Sports and 17.39% on J-HMDB split 1. This is mainly due to the fact that motion information from one frame is not discriminative enough, see a similar observation for action classification in [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF]. Stacking more flows (Flow-10) decreases the result significantly on UCF-Sports, and slightly on J-HMDB partly due to the degraded proposals as mentioned in Sec. 3. We observe that the decrease in performance is more important on the strongly moving actions such as "Diving", "Swinging at the high bar", "Kicking", and "Jump". This can be explained by the fact the stacking does not align the actors and hence the detected bounding boxes are more imprecise. In summary, Flow-5 performs best and is complementary to RGB-1. We discuss different combination schemes next. Two streams combination. We explore two schemes for combining the appearance and motion R-CNN: box-level non maximum suppression (NMS) and score fusion with a RoI fusion layer (our end-to-end pipeline, see Figure 1). The NMS method perform detection for appearance and motion R-CNN separately, and then fuses all the detected bounding boxes from both streams with NMS. As shown in Figure 4, for the fusion of RGB-1 and Flow-5 streams, the score fusion (indicated by "Ours") obtains 2.02% improvement over the NMS fusion on J-HMDB and performs on par on UCF-Sports. Compared to the NMS fusion, the score fusion uses both appearance and motion information for bounding box scoring which is more discriminative. In the remained of this paper, we use score fusion for the combination of appearance and motion R-CNNs, and use "RGB-1 + Flow-5" for two-stream R-CNN by default.

Multi-scale training and testing. An action can occur on arbitrary scales.

Here we explore robustness to scale changes by using multi-scale training and testing. We fix the scale to 600 for single scale training/testing, and to {480, 14.1% for "Golf" on UCF-Sports and 8.7% for "swingBaseball" on J-HMDB over the Org model. Also, the Bottom half model gains 5.16% for "Swing 1" on UCF-Sports and 7.9% for "climbStairs" on J-HMDB. Linking and temporal localization. We evaluate our linking and temporal localization methods for both the two stream R-CNN model and its multi-region version. Table 4 shows the video mAP results with IoU threshold of δ on UCF-Sports, J-HMDB, and UCF101 datasets. Both of our approaches obtain excellent video-level performance on these datasets which is mainly due to the highquality frame-level detections. We obtain 94.82% and 70.88% on UCF-Sports and J-HMDB (split 1) with our linking method, respectively. The corresponding numbers are 94.81% and 68.97 when implementing the linking method of [8].

Results improve on J-HMDB, but are the similar for UCF-Sports, where de- tections are near perfect. Multi-region TS R-CNN consistently outperforms the original TS R-CNN model on J-HMDB and UCF101, and performs similarly on UCF-Sports. The lack in improvement on UCF-Sports might be explained by the mistakes in the spatio-temporal annotation, which explains why the classification performance actually does improve for UCF-Sports. Note that we perform temporal localization only on UCF101. We also evaluate the performance without temporal localization, i.e., we assume that the action spans the entire clips.

As most of the action classes cover almost the entire clips, the average gain is 3.0% with MR-TS R-CNN model for an IoU threshold of 0.2. However, it is significant for some of the short actions, i.e., for "Basketball", "BasketballDunk", "TennisSwing", and "VolleyballSpiking", we gain 23.5%, 5.8%, 20.0%, and 8.8%, respectively.

Classification by detection. Similar to [8], our approach can be also extended to action classification of the videos. We leverage the best action track (i.e., the track with maximum action score) in a video to predict the action label. Table 5 reports the average class accuracy on UCF-Sports and J-HMDB. Both of our models achieve outstanding performance, with the multi-region version improving the results in both cases. In particular, our MR-TS R-CNN model obtains 95.74% and 71.08% on UCF-Sports and J-HMDB, respectively. The results are significantly better than those of the IDT method [START_REF] Wang | Action recognition with improved trajectories[END_REF] and the pose-based CNN method [START_REF] Chéron | P-CNN: pose-based CNN features for action recognition[END_REF] which perform only classification. This suggests that classification can be improved by precise localization and detection-aware features.

Comparison to the state of the art

We conclude the experimental evaluation with a comparison to the state of the art in Table 6. In both frame-level and video-level mAP, our TS R-CNN already outperforms the state-of-the-art results on all three datasets. In particular, our MR-TS R-CNN approach outperforms the state of the art by 12.6%, 12.7% and 29.89% in frame-mAP, 4.3%, 12.4% and 26.06% in video-mAP on UCF-Sports, J-HMDB and UCF101, respectively. Both [8] and [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF] also make use of framelevel action detection with R-CNN. Weinzaephel et al. [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF] select the top two frame-level detections for each class from the entire video and then track with them based on class-level and instance-level scores. Compared to [8] and [START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF], our method benefits from two key points: (1) the high-quality proposals from both appearance and motion RPN and (2) the discriminative frame-level action representation based on stacked optical flows and multiple parts.

Conclusion

This paper introduces a multi-region two-stream R-CNN action detection approach, which takes full advantage of three recent methods, namely faster R-CNN, two-stream CNNs with optical flow stacking and multi-region CNNs. We propose a novel framework for action detection which builds on these methods. It significantly outperforms the state of the art [8,[START_REF] Weinzaepfel | Learning to track for spatio-temporal action localization[END_REF]. In our experiments on UCF101, we observed that a limitation lies in handling low-quality videos and small bounding boxes, which will be addressed in future work.

Fig. 1 :

 1 Fig. 1: Overview of our two-stream faster R-CNN.

Fig. 2 :

 2 Fig. 2: Comparision of frame-level proposals on UCF-Sports and J-HMDB split 1: selective search proposals (SS), EdgeBoxes proposals (EB), RPN-ImageNet, RPN-a, RPN-m and the fusion of RPN-a and RPN-m proposals. RPN-mk indicates k frame optical flows. Left: UCF-Sports. Right: J-HMDB split 1.

Fig. 3 :

 3 Fig. 3: Overview of the multi-region two-stream faster R-CNN architecture.

Fig. 4 :

 4 Fig. 4: Evaluation of different frames types (RGB and flow), number of frames (x=1, 5, 10) used for detection and combination strategies (NMS or score combination-ours). Left: UCF-Sports. Right: J-HMDB split 1.

Fig. 5 :

 5 Fig. 5: Examples of action detection with different region R-CNN models. We only show the top two detections after performing NMS. Overlayed yellow regions indicate the regions invisible for the part R-CNN models.

Figure 5

 5 illustrates this with a few examples. Actions in row (a) and (b) are better detected by Upper half R-CNN model, while row (c) and (d) by Bottom half R-CNN model. We can observe that the detected boxes and their scores vary significantly between the different models. By focusing on the bottom part, the example of "climb stairs" (row d, column 3) gets a high confidence detection due to the discriminative cue of stairs and legs.

Table 1 :

 1 Evaluation of different training and testing scales. All detections from different scales are combined by the NMS, and RGB-1 and Flow-5 streams are combined by score averaging. We report results for UCF-Sports and J-HMDB, split 1.

			RGB-1	Flow-5	RGB-1 + Flow-5
	Test scales	Train scales UCF-Sports J-HMDB UCF-Sports J-HMDB UCF-Sports J-HMDB
	{600}	{600} {480, 600, 800}	65.30 68.07	38.05 38.71	74.24 73.62	46.71 47.74	--	--
	{480, 600, 800}	{ 600} {480, 600, 800}	68.47 69.29	39.90 40.02	76.77 75.81	47.05 48.60	-82.30	-56.60

Table 2 :

 2 Per-class frame-AP of individual regions and multi-region two-stream faster R-CNN on UCF-Sports.

Table 4 :

 4 Video mAP on UCF-Sports, J-HMDB and UCF101 (split 1) 2 with varying IoU thresholds. UCF101(w/o) assumes that the action spans the entire clip, i.e., does not perform temporal localization.

		UCF-Sports J-HMDB UCF101 (with temporal loc) UCF101 (w/o)
	δ	0.2	0.5 0.2 0.5 0.05 0.1	0.2	0.3	0.2
	TS R-CNN	94.82 94.82 71.1 70.6 78.12 76.81 71.78 64.51	69.78
	MR-TS R-CNN 94.83 94.67 74.3 73.09 78.76 77.31 72.86 65.70	69.56

Table 5 :

 5 Classification results on UCF-Sports and J-HMDB by detection.

		FAT [8] IDT+FV [2] P-CNN (w/o GT) [21] TS R-CNN MR-TS R-CNN
	UCF-Sports	-	88.0	-	91.49	95.74
	J-HMDB	62.5	65.9	61.1	70.52	71.08

Table 6 :

 6 Comparison to the state of the art on three datasets 2 . The IoU threshold δ for frame-mAP is fixed to 0.5. 71.1 70.6 78.12 76.81 71.78 64.51 Our MR-TS R-CNN 94.8 94.7 74.3 73.1 78.76 77.31 72.86 65.70

			UCF-Sports J-HMDB		UCF101 (split 1)
		δ	0.2	0.5	0.2 0.5 0.05 0.1	0.2	0.3
	video-mAP	Gkioxari et al. [8]	-	75.8	-53.3	-	-	-	-
		Weinzaepfel et al. [9] -	90.5 63.1 60.7 54.3 51.7 46.8 37.8
		Yu et al. [44]	-	-	-	-	49.9 42.8 26.5 14.6
	Our TS R-CNN Gkioxari et al. [8] 94.8 frame-mAP 68.1 Weinzaepfel et al. [9] 71.9	36.2 45.8			-35.84
		Our TS R-CNN		82.3	56.9			64.77
		Our MR-TS R-CNN	84.5	58.5		65.73

Thoth team, Inria, Laboratoire Jean Kuntzmann, Grenoble, France.

https://github.com/rbgirshick/py-faster-rcnn

The UCF101 results are updated, as there was a problem with the annotations used initially. Note that the results improved by a significant margin.

Acknowledgments. This work was supported in part by the ERC advanced grant ALLEGRO, the MSR-Inria joint project, a Google research award, a Facebook gift, the Natural Science Foundation of China (No. 61502152) and the Open Projects Program of National Laboratory of Pattern Recognition. We gratefully acknowledge the support of NVIDIA with the donation of GPUs used for this research.


Cosmin Saveanu

Jean-Claude Rousselle

Pascal Lenormand

Abdelkader Namane

Alain Jacquier

Micheline Fromont-Racine

The p21-activated protein kinase inhibitor Skb15 and its budding yeast homologue are 60S ribosome assembly factors

Ribosome biogenesis is driven by a large number of pre-ribosomal factors that associate to and dissociate from the pre-ribosomal particles along the maturation pathway. We have previously shown that budding yeast Mak11, whose homologues in other eukaryotes were described as modulating a p21-activated protein kinase function, accumulates in Rlp24 associated pre-60S complexes when their maturation is impeded in S. cerevisiae. The functional inactivation of Mak11 interfered with the 60S ribosomal RNA maturation, led to a cell cycle delay in G1 and blocked Rpl25-GFP in the nucleoli of yeast cells, indicating an early role of Mak11 in ribosome assembly. Surprisingly, Mak11 inactivation also led to a dramatic destabilization of Rlp24. The suppression of the thermosensitive phenotype of a mak11 mutant by RLP24 overexpression and a direct in vitro interaction between Rlp24 and Mak11 suggest that Mak11 acts as an Rlp24 cofactor during early steps of 60S ribosomal subunit assembly. Moreover, we found that Skb15, the Mak11 homologue in S. pombe, also associated with pre-ribosomes and affected 60S biogenesis in fission yeast. It is thus likely that the previously observed phenotypes for MAK11 homologues in other eukaryotes are secondary to the main function of these proteins in ribosome formation.

Introduction

Eukaryotic ribosome biogenesis begins in the nucleolus with the association of ribosomal and pre-ribosomal proteins to a nascent rRNA precursor transcribed by the RNA Polymerase I. This precursor consists, in yeast, of the sequences for mature 18S, 5.8S and 25S ribosomal RNAs, separated by two ITS (Internal Transcribed Spacers) and flanked by additional 5' end and 3' end sequences. The rRNA precursor, as a large ribonucleoprotein particle, is next matured in well-ordered processing steps. Many auxiliary factors are required all along the pathway but except for a few enzymatic functions, the molecular roles of these factors remain unclear. A cleavage in the region separating the precursor of the 18S rRNA and the 5.8S and 25S rRNA leads to the generation of pre-60S and pre-40S intermediate particles. The third RNA component of the large ribosomal subunit, the 5S rRNA, associates in a precursor form with the pre-60S particles and both pre-60S and pre-40S particles are further processed and exported to the cytoplasm, where final maturation events take place and generate the ribosomal subunits [START_REF] Venema | Ribosome synthesis in Saccharomyces cerevisiae[END_REF][START_REF] Warner | Nascent ribosomes[END_REF].

Studies with budding yeast allowed the identification and functional characterization of a surprisingly large number of proteins and RNAs that participate in the maturation of eukaryotic ribosomes. Since 2001, generic purification methods such as TAP, in association with developments in mass spectrometry, allowed the identification of about 200 preribosomal factors [START_REF] Gavin | Proteome survey reveals modularity of the yeast cell machinery[END_REF][START_REF] Gavin | Functional organization of the yeast proteome by systematic analysis of protein complexes[END_REF][START_REF] Ho | Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry[END_REF][START_REF] Krogan | High-definition macromolecular composition of yeast RNA-processing complexes[END_REF]. In contrast to the ribosomal proteins, which associate to the pre-particles and remain associated to the mature ribosomes, the pre-ribosomal factors associate transiently to the precursors (for reviews see [START_REF] Fromont-Racine | Ribosome assembly in eukaryotes[END_REF][START_REF] Tschochner | Pre-ribosomes on the road from the nucleolus to the cytoplasm[END_REF]. When ribosome biogenesis is blocked, the changes in purified pre-ribosomal complexes composition may indicate the order of protein association, dissociation or subcomplex formation during the pathway (see for example [START_REF] Harnpicharnchai | Composition and functional characterization of yeast 66S ribosome assembly intermediates[END_REF][START_REF] Miles | Ytm1, Nop7, and Erb1 form a complex necessary for maturation of yeast 66S preribosomes[END_REF][START_REF] Saveanu | Sequential protein association with nascent 60S ribosomal particles[END_REF]. Such experiments can also establish the requirement of a given factor for subsequent association of other proteins to the precursors.

We identified Mak11 in complexes purified in association with the pre-60S essential protein Rlp24 and have shown that the amount of co-purified Mak11 starkly increased when another essential pre-60S factor, the Nog1 GTPase, was depleted [START_REF] Saveanu | Sequential protein association with nascent 60S ribosomal particles[END_REF]. Several known pre-60S factors (Nop7, Tif6, Erb1, Nop2) were identified in association with Mak11 in a large-scale experiment [START_REF] Ho | Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry[END_REF]. Recent purification of a large number of macromolecular complexes in yeast further confirmed the presence of Mak11 in predicted pre-60S complexes [START_REF] Gavin | Proteome survey reveals modularity of the yeast cell machinery[END_REF][START_REF] Krogan | High-definition macromolecular composition of yeast RNA-processing complexes[END_REF]. Moreover, the Mak11 fusion with GFP was shown to localize to the nucleus and concentrated in the nucleolus of yeast cells [START_REF] Huh | Global analysis of protein localization in budding yeast[END_REF]. MAK11's name (MAintenance of Killer) comes from its identification in genetic screens looking for mutations that would affect the maintenance of the M1 toxin-encoding dsRNA, satellite of the L-A dsRNA yeast virus [START_REF] Toh | Chromosomal superkiller mutants of Saccharomyces cerevisiae[END_REF][START_REF] Wickner | Twenty-six chromosomal genes needed to maintain the killer double-stranded RNA plasmid of Saccharomyces cerevisiae[END_REF][START_REF] Wickner | Mak mutants of yeast: mapping and characterization[END_REF]. The RNAs used for the synthesis of the viral proteins have neither a typical 5' cap nor a poly(A)-tail [START_REF] Wickner | Double-stranded RNA viruses of Saccharomyces cerevisiae[END_REF]. Many of the isolated mak mutants showed polysome profiles typical for 60S ribosomal subunit biogenesis impairment [START_REF] Ohtake | Yeast virus propagation depends critically on free 60S ribosomal subunit concentration[END_REF]. It is likely that the identification of 60S ribosomal subunit biogenesis factors among the mak genes was due to the fact that normal 60S ribosomal subunit levels, in contrast to 40S subunit levels, are required for the translation of uncapped and non-polyadenylated RNAs like those encoding viral proteins [START_REF] Sachs | The poly(A) binding protein is required for poly(A) shortening and 60S ribosomal subunit-dependent translation initiation[END_REF][START_REF] Searfoss | Linking the 3' poly(A) tail to the subunit joining step of translation initiation: relations of Pab1p, eukaryotic translation initiation factor 5b (Fun12p), and Ski2p-Slh1p[END_REF]. Several mutations discovered during the mak screens affected genes like mak7, mak8 or mak18, later identified to be coding for ribosomal proteins (Rpl8a, Rpl3 and Rpl42b respectively) [START_REF] Ohtake | Yeast virus propagation depends critically on free 60S ribosomal subunit concentration[END_REF]. Other mutations affected genes coding for proteins recently shown to be physically associated with pre-60S particles by large-scale complex purification studies [START_REF] Gavin | Proteome survey reveals modularity of the yeast cell machinery[END_REF][START_REF] Krogan | Global landscape of protein complexes in the yeast Saccharomyces cerevisiae[END_REF].

Recently, the requirements of Mak21/Noc1 [START_REF] Edskes | Mak21p of Saccharomyces cerevisiae, a homolog of human CAATT-binding protein, is essential for 60 S ribosomal subunit biogenesis[END_REF][START_REF] Milkereit | Maturation and intranuclear transport of pre-ribosomes requires Noc proteins[END_REF], Mak5 [START_REF] Zagulski | Mak5p, which is required for the maintenance of the M1 dsRNA virus, is encoded by the yeast ORF YBR142w and is involved in the biogenesis of the 60S subunit of the ribosome[END_REF] and Mak16 [START_REF] Pellett | Mak16p is required for the maturation of 25S and 5.8S rRNAs in the yeast Saccharomyces cerevisiae[END_REF] for 60S ribosomal subunit formation were described. One of the mutations described in the genetic screen and having effects on 60S levels affected MAK11, a gene shown to be essential for viability and suspected to be required for 60S subunit biogenesis [START_REF] Ohtake | Yeast virus propagation depends critically on free 60S ribosomal subunit concentration[END_REF].

Based on the available data, we predicted Mak11 to be an essential factor involved in nuclear maturation of 60S ribosomal subunits. Puzzlingly, while Mak11 putative orthologues exist in many eukaryotes, both the S. pombe homologue Skb15 [START_REF] Kim | The kelch repeat protein, Tea1, is a potential substrate target of the p21-activated kinase, Shk1, in the fission yeast, Schizosaccharomyces pombe[END_REF][START_REF] Kim | Genetic and molecular characterization of Skb15, a highly conserved inhibitor of the fission yeast PAK, Shk1[END_REF] and the human homologue hPip1 [START_REF] Xia | Regulation of the p21-activated kinase (PAK) by a human Gbeta -like WD-repeat protein, hPIP1[END_REF] It was surprising that Mak11 homologues were described as modulators of PAK activity affecting central signaling pathways in other eukaryotes, while physical association and functional data strongly suggested that Mak11 is a novel factor involved in 60S ribosomal subunit formation in S. cerevisiae. There were two explanations for this discrepancy: either Mak11 was not the homologue of Skb15 and hPip1 or the previously observed effects were secondary to the function of these factors in ribosome biogenesis. We thus tried to understand the role of Mak11 in ribosome biogenesis in S. cerevisiae and tested the putative ribosome biogenesis function for S. pombe Skb15.

We show here that the essential function of Mak11 is linked to an early, nucleolar step of 60S ribosomal subunit biogenesis, and we propose that it serves as an Rlp24 cofactor during the assembly of early pre-60S particles in S. cerevisiae. The ribosome biogenesis function of Mak11 was conserved during evolution since we could show that Skb15 is required for 60S formation in S. pombe. We conclude that previously observed effects of Mak11 homologues are likely to be secondary to the primary effects of these factors on ribosome biogenesis.

Materials and methods

Strains and plasmids.

The strains used in this study are listed in Table 1. Chromosomal insertions were obtained by homologous recombinaison using PCR fragments (1). pCM190-RLP24 was obtained by subcloning the RLP24 sequence from the plasmid pGEX4-T-RLP24 (47) using BamHI and NotI into pCM190 [START_REF] Gari | A set of vectors with a tetracycline-regulatable promoter system for modulated gene expression in Saccharomyces cerevisiae[END_REF]. The MAK11 open reading frame was cloned after PCR amplification in the pDONR 201 vector using the Gateway system (Invitrogen). The resulting entry clone was used to generate the pTG189-MAK11 plasmid. pTG189, a Gateway compatible TAP vector was obtained from pCM189 ( 14) by cloning the Nterminal TAP cassette (43) downstream of the tetracyclin regulated promoter and upstream a RfA Gateway cassette. A Gateway compatible vector derived from pET32a (Novagen), a gift from E.

Bertrand (IGM, Montpellier, France), was used to obtain a vector allowing (His) 6 -Mak11 expression in bacteria.

Nmt1-Skb15 and SP870 S. pombe strains were obtained from S. Marcus (University of Alabama, USA). The Skb15-TAP strain was obtained by homologous recombination in the Pb185 strain (gift from B. Arcangioli, Institut Pasteur, Paris, France) using a method adapted from [START_REF] Wang | Strategies for gene disruptions and plasmid constructions in fission yeast[END_REF]. Long recombination arms were generated by two successive PCR reactions from genomic DNA using oligonucleotides: A-BamHI ACG GGA TCC AGG CAA ATC TGT CTA CCC TGT TG; B-XmaI TGA AAA GGA CGA AGC ATG CCC CCGGG; C-SalI GGT CGA CCT TAA TAG GGA AAG GAC GGG and D-BamHI TGC TAG ATG AGC TAT TTG CCA CGG GAT CCA GG. The product of the second PCR reaction, was cloned in pCRBlunt (Invitrogen) and next subcloned in the pFA6a-CTAP-MX6 vector (52) using SalI and XmaI restriction enzyme sites. The resulting plasmid was linearized by digestion with BamHI and used to transform strain Pb185. Clones resistant to G418 were tested by immunoblotting for the presence of the fusion protein.

Tandem Affinity Purification and in vitro binding assay

Complex purifications were performed as described in [START_REF] Rigaut | A generic protein purification method for protein complex characterization and proteome exploration[END_REF] with a few modifications, starting with 4 liters of yeast culture. Buffers contained 0.1M NaCl. Eluted proteins were precipitated with methanol/chloroform, separated on a 5-20% polyacrylamide gradient-SDS gel and identified either by immunoblot or mass spectrometry. Mass spectrometry protein identification was done using MALDI-TOF as described [START_REF] Leger-Silvestre | The ribosomal protein Rps15p is required for nuclear exit of the 40S subunit precursors in yeast[END_REF]. For RNA associated determination, only the first step of purification was performed, in presence of vanadyl ribonucleoside complexes as RNAse inhibitor. The associated RNAs were extracted twice with phenol/chloroform. To investigate the association of different proteins with purified complexes, TEV eluates were separated on denaturing polyacrylamide gels, transferred to nitrocellulose membranes (Bio-Rad) and probed with rabbit polyclonal antibodies against Nog2 (46), Nog1 and Rlp24 (47), Nsa2 (30) Arx1 [START_REF] Lebreton | A functional network involved in the recycling of nucleocytoplasmic pre-60S factors[END_REF] and Mak11 (this work) used at a 1:5 000 dilution. The peroxidase activity of secondary antibodies was detected using either the ECL+ (GE Healthcare) or the Immobilon Western (Millipore) chemiluminescence kit.

In vitro binding was tested as previously described (47) using plasmids expressing GSTtagged Rlp24, Rpl3 and Rpl5 (controls) and (His) 6 -Mak11. The presence of Mak11 in the eluate was estimated by immunoblot with anti-Mak11 antibodies.

Sucrose gradient sedimentation

Polysomal extracts were obtained using glass beads vortexing. Polysomes were separated on a 10-50% sucrose gradient and centrifuged at 39 000 rpm for 2h. 45 min. at 4°C in a SW41-Ti rotor. Fractions were recovered with an ISCO fractionator and the 254 nm absorbance was measured. For protein identification by immunoblot, the proteins from each fractions were precipitated with 10% TCA and separated on polyacrylamide gels. TAP-tagged proteins were revealed with a 1:10 000 dilution of peroxidase-antiperoxidase complex (PAP, Sigma).

RNA extraction, Northern blot and primer extension

RNA extractions were performed using glass beads and phenol/chloroform. Large size rRNAs were denatured with glyoxal and separated on 1% agarose gels and small size rRNAs were separated on 5% acrylamide-urea denaturing gels. Northern blots and primer extensions were performed using 32 P-labelled oligonucleotides. The sequence of the oligonucleotides specific of S.cerevisiae were previously described [START_REF] Saveanu | Nog2p, a putative GTPase associated with pre-60S subunits and required for late 60S maturation steps[END_REF]. Those related to S.pombe were based on the detected pre-rRNA intermediates previously described [START_REF] Good | The ribosomal-RNA-processing pathway in Schizosaccharomyces pombe[END_REF] and were the following: CS151, TGT CGG AAA GCA TAG CAA GC, for U2 snRNA, used as a control; CS148, AAC AAA TTT TCG TTC AAC ACC TCA TC, used to detect 27S and 7S intermediates, CS153, CGT TAA GGT TCA AAT ATA AAA GAG specific for the 27S intermediates.

Screen for mak11 ts alleles

For the selection of thermosensitive MAK11 alleles, we used mutagenic PCR combined with gap repair [START_REF] Muhlrad | A rapid method for localized mutagenesis of yeast genes[END_REF]. A region of genomic S. cerevisiae DNA encompassing the MAK11 ORF and 243 nucleotides upstream the ATG sequence as well as 95 nucleotides downstream the stop codon was amplified by PCR using oligonucleotides: GCT CTA GAA GAC ATT TTT CTA GCT ACA TAA; AGG CGC GCC ATC ATC TTT AAC GAT TAA GATA. The resulting PCR product was cloned in the centromeric vector pFL38 (URA3 marker) using XbaI and AscI sites and verified by sequencing. The obtained pFL38-Mak11 plasmid was able to fully complement MAK11 deletion and the resulting haploid strain was further used as LMA260. A PCR-based strategy was used for random mutagenesis of the MAK11 sequence. PCR reactions were performed with oligonucleotides AGA TGC GTA AGG AGA AAA TAC CGC ATC and CGA CTG GAA AGC GGG CAG TGA using pFL38-Mak11 as template. In one case the ratio of dATP to other nucleotides was 1:5 and we used 4 mM MgCl 2 combined with 0.5 mM MnCl 2 and in another case the ratio of dATP to other nucleotides was 1:10 and the reaction mixture contained 10% DMSO. The strain LMA260 was transformed with PCR products and pFL36CII linearized by digestion with XbaI and HindIII. A total of 16 000 clones were obtained with around 5 000 of these being able to grow at 25°C on a selective medium containing 5FOA. After replica plating on selective medium we obtained clones that were able to grow at 25°C but not at 35°C. Plasmids were extracted from nine candidates, and tested by retransformation. The strongest ts phenotype was observed for the candidate mak11-2, further used in this study.

Sequencing of the mak11-2 allele showed several predicted amino acid changes depicted in Figure 7.

High copy number suppressor genetic screen

The mak11-2 ts mutant strain (LMA263-2) was transformed with a yeast genomic high copy library (generated in the pFL44L 2µ URA3 vector) a gift from F. Lacroute, CGM, Gif-sur-Yvette, France. The transformants were selected on minimal medium without uracil and leucine.

After 24 h at 25°C the plates containing the transformants were incubated at 37°C and the clones growing at this restrictive temperature were selected. Plasmids were recovered and used to transform the LMA263-2 strain. The growth phenotype was compared with the growth phenotype of LMA264 transformed with an empty vector. After verification of the suppressor phenotype, the DNA inserts were amplified and the ends were sequenced. The complementation of the mak11-2 ts phenotype was verified using the pCM190-RLP24 vector.

Fluorescence microscopy

Cells transformed with a centromeric plasmid expressing RPL25-eGFP or cells expressing chromosomal TAP-tagged fusion proteins were cultured in minimal medium. The protein A part of the TAP tag was detected with anti-protein A antibodies and Cy3 secondary antibodies [START_REF] Pringle | Immunofluorescence methods for yeast[END_REF].

Observation of the cells by epifluorescence was done as described [START_REF] Lebreton | A functional network involved in the recycling of nucleocytoplasmic pre-60S factors[END_REF].

FACS analysis

To analyze the cell cycle distribution of yeast cells population we used exponentially growing cells in rich medium at 27°C or shifted for up to 6 hours at 37°C. Aliquots of 2 ml were retrieved and fixed with 70% cold ethanol for 1 hour at room temperature. RNA was digested with RNAse A (1 mg/ml in Tris HCl 0.2 M pH 7.5, EDTA 20 mM for 1 h. at 37°C) and DNA was stained with 50 µg/ml propidium iodide in 0.1 ml PBS overnight at 4°C. The cell suspension was diluted to 1 ml with PBS before analysis. Flow cytometry analysis was done using a FACS Calibur (BD Biosciences). One hundred thousand events were used to estimate the ratio between 1n and 2n DNA containing cells.

Rlp24-TAP half-life estimation

For the analysis of Rlp24-TAP half-life we used a procedure derived from (2). Addition of cycloheximide was done at the same time with a shift of the cultures from 27°C to 37°C.

Immunoblotting was performed using peroxidase-anti-peroxidase complexes (Sigma) and the Immobilon Western (Millipore) chemiluminescence kit. Images were obtained with a cooled digital camera (GeneGnome, Syngene) and quantitated using ImageJ (version 1.38a, Rasband, W.S., U. S. National Institutes of Health, Bethesda, Maryland, USA, [http://rsb.info.nih.gov/ij/]).

Results

Mak11 is required for the maturation of 27SB pre-ribosomal RNA in the nucleolus

MAK11 is essential in yeast [START_REF] Icho | The MAK11 protein is essential for cell growth and replication of M double-stranded RNA and is apparently a membrane-associated protein[END_REF] and a mutant allele of MAK11 leads to free 60S ribosomal subunits decrease and formation of halfmer polysomes [START_REF] Ohtake | Yeast virus propagation depends critically on free 60S ribosomal subunit concentration[END_REF]. To investigate the involvement of Mak11 in ribosome biogenesis, and specifically in 60S ribosomal subunit formation, we tested the relative amounts of different mature and precursors rRNA in yeast cells depleted for Mak11. We used a strain deleted for the chromosomal copy of MAK11 where the expression of an N-terminal TAP fusion of MAK11 was placed under the control of a tetracycline repressible promoter on a centromeric plasmid. In the absence of the repressor, the growth of the cells was comparable to the growth of a wild-type strain. Cell aliquots were collected at different time points after doxycyclin addition and total RNA was extracted and tested by primer extension or Northern blot with specific oligonucleotides (Fig. 1). The 25S mature rRNA decreased whereas the 18S was only slightly affected (Fig. 1B), in agreement with the previously observed decrease of free 60S subunit levels. The only pre-ribosomal RNA species that showed a relative accumulation when Mak11 was depleted was the 27SB precursor and, as observed for other 60S mutants, the 35S precursor (Fig. 1B-D). The 27SB did not accumulate when compared with U2 or U5 snRNAs but were increased when compared with the 27SA2 precursor. The apparent increase in U2 and U5 snRNA levels was due to the relative decrease of ribosomal RNA in the total RNA samples that were analyzed.

A decrease in the amounts of 7S pre-rRNA as compared with earlier 27S intermediates could be detected after 6 hours of Mak11 depletion, in correlation with the rapid decrease in the amount of Mak11 as judged by immunoblotting with antibodies binding to the N-terminal protein tag (not shown). The aberrant 23S rRNA processing intermediate, generated by cleavage at position A3 before A2, was detected at 10h of depletion and strongly accumulated at later time points (21 hours of shift to glucose) when 7S became undetectable. This aberrant cleavage was accompanied by a decrease of the 20S pre-rRNA levels (Fig. 1B).

To perform additional functional studies, we searched for variants of Mak11 that, while supporting growth in yeast cells, were responsible for a thermosensitive phenotype. By mutagenic PCR coupled with plasmid gap repair, we obtained several alleles of mak11 that complement the deletion of the gene at 25°C but support only very slow growth at 37°C. The mak11-2 strain began to grow noticeably slower than a wild type strain at 4 hours after a shift of temperature from 25 to 37°C (Fig. 2A). The observed growth defect was associated with an increase in the G1 cell cycle phase length as determined by FACS analysis (Fig. 2B). Such a G1 cell cycle delay when ribosome biogenesis was impaired could be seen when other pre-60S factors like Nog1 were depleted (not shown) and was previously reported for SSU processome factors depletion [START_REF] Bernstein | The small subunit processome is required for cell cycle progression at G1[END_REF]. Total RNA extracted from the wild-type and mak11-2 strain after 4 and 12 hours of growth at 37°C showed a decreased amount of the 25S ribosomal RNA when compared with the 18S ribosomal RNA (Fig. 2C), in agreement with a role of MAK11 in 60S ribosomal subunit formation.

Both protein depletion and mak11 mutation led to 60S formation defects. To look for the cellular localization of these blocked pre-60S particles, we used the Rpl25-GFP fusion previously described as a suitable marker of 60S ribosomal particles precursors export defects [START_REF] Hurt | A novel in vivo assay reveals inhibition of ribosomal nuclear export in ran-cycle and nucleoporin mutants[END_REF]. When expressed in a mak11-2 strain at non-permissive temperature, Rpl25-GFP accumulated in the nuclei of yeast cells, in a region excluded from DNA staining (Fig. 2D). We concluded that Mak11 is not only required for the progression of rRNA maturation at the 27SB step but also plays a role in the exit of the 60S precursors from the nucleolus.

As predicted from our previous work, Mak11 acts thus early after the formation of the 60S precursors in the nucleolus. For a mechanistic analysis of Mak11 action we first defined the composition of pre-ribosomal complexes that contained Mak11.

Complexes associated with Mak11 do not contain late pre-ribosomal factors

Mak11 was found associated with pre-60S complexes purified in association with Rlp24 and its levels increased in these particles when another pre-60S factor, Nog1, was depleted [START_REF] Saveanu | Sequential protein association with nascent 60S ribosomal particles[END_REF].

To place Mak11 on the 60S assembly pathway, we purified the associated complexes and identified the proteins by mass spectrometry. Addition of the TAP tag [START_REF] Rigaut | A generic protein purification method for protein complex characterization and proteome exploration[END_REF] to Mak11 as an Nterminal (strain LMA326) or C-terminal (strain LMA375) fusion had no deleterious effects on Mak11 function since both versions supported growth to wild-type levels (not shown). Forty known and putative pre-60S factors, other than ribosomal proteins, were identified by mass spectrometry from TAP purifications using chromosomal C-terminal tagged Mak11 (Fig. 3A, left lane) as well as from TAP purifications using N-terminal tagged Mak11 expressed from a plasmid in a ∆mak11 strain (Table 2). Notable absences in the list of the identified proteins are Arx1, Nog2, Nug1 and Nsa2 known to associate late in the nucleus to the pre-ribosomes [START_REF] Lebreton | NSA2, an unstable, conserved factor required for the maturation of 27SB PRE-rRNAs[END_REF][START_REF] Nissan | 60S preribosome formation viewed from assembly in the nucleolus until export to the cytoplasm[END_REF][START_REF] Saveanu | Nog2p, a putative GTPase associated with pre-60S subunits and required for late 60S maturation steps[END_REF]. We verified by immunoblotting with specific antibodies that Arx1, Nog2 and Nsa2 were present in complexes purified using Rlp24-TAP but absent when the complexes were purified using Mak11-TAP (Fig. 3B). The reverse experiment confirmed these results; neither Nog2-TAP nor Arx1-TAP were able to co-purify Mak11 (Fig. 3C).

In view of the number of pre-ribosomal proteins found in association with Mak11, we wondered what species of pre-rRNA were present in these particles. The analysis of the different pre-rRNA species enriched in the TEV protease eluate when TAP-Mak11 was purified, revealed the presence of trace amounts of 27SA2, 7S and 35S with a specific enrichment of the 27SB precursor (Fig 3D ,E). Altogether, these results revealed Mak11's association with 27SB pre-rRNA in nucleolar complexes and its requirement for an essential maturation step of 27SB containing pre-60S particles. Mak11 seems to bind only transiently to nuclear 60S precursors as it leaves these particles before the association of late pre-60S factors such as Nog2, Nsa2 or Arx1.

Mak11 is both associated with pre-60S particles and in a free form.

We observed that the amount of proteins purified in association with Mak11 was strongly decreased when the purification was performed in a strain depleted for Rlp24 and was increased when the purification was done after Nog1 depletion (Fig. 3A). To better understand what changed under these conditions, we separated by ultracentrifugation, total protein extracts from a wild type strain or strains depleted for Rlp24 or Nog1, on sucrose gradients. Mak11-TAP was detected in the different fractions by immunoblotting (Fig. 4). Surprisingly, even in a wild-type strain, the Mak11 sedimentation profile showed two peaks, a major one in fractions sedimenting in the upper part of the gradient and a minor one around the position of the 60S pre-ribosomal particles (Fig. 4A). The ratio between these two fractions changed in opposite directions when either Rlp24 or Nog1 were depleted. Most of Mak11 was found in the smaller complexes, under conditions of low Rlp24 levels (Fig. 4B), whereas Mak11 was abundant in the pre-60S fractions when Nog1 was limiting for ribosome biogenesis (Fig. 4C). These changes were correlated with the small amount of pre-ribosomal proteins purified with Mak11-TAP when Rlp24 was depleted while pre-ribosomal proteins accumulated in the complexes associated to Mak11 under Nog1 depletion. In addition, our previous observation of Mak11 accumulating in Rlp24-TAP complexes under Nog1 depletion indicated that pre-60S complexes containing Mak11 where accumulating [START_REF] Saveanu | Sequential protein association with nascent 60S ribosomal particles[END_REF].

We tried to better characterize the form of Mak11 that sedimented in the upper part of the gradient by combining TAP purifications and sucrose gradients and by testing the putative oligomeric state of Mak11. No other proteins were found to be associated with the lower sedimentation rate Mak11-TAP when purified complexes were separated on a sucrose gradient.

Moreover, other tagged forms of Mak11 (HA and myc epitopes) have shown that the small weight fraction of the protein sedimented in the first fractions of the gradients (not shown). The sedimentation of purified recombinant Mak11 from E. coli matched the expected behavior for a monomeric protein (not shown). The relatively high sedimentation rate of the free TAP-tagged Mak11 form was thus probably due to interactions between Mak11 and the tag.

Mak11 seems to have a dynamic distribution between a free form and the pre-60S form, depending on the levels of different pre-60S intermediates. The free form might represent a "storage" of protein, readily available for variable 60S biogenesis demands.

Rlp24 cooperates with Mak11 for 60S ribosomal particles assembly

To obtain further hints about Mak11 function, we performed a high-copy suppressor genetic screen with the mak11-2 thermosensitive strain. The large majority of the recovered plasmids (one hundred) contained the MAK11 gene and were able to complement both the thermosensitivity of the test strain and the loss of a wild-type MAK11 plasmid. Nine other isolated plasmids allowed only partial complementation of the thermosensitive phenotype of the mak11-2 strain and contained sequences that had in common the RLP24 genomic region, from -619 upstream the ATG to 1074 nucleotides downstream the stop codon. We further verified the suggest that Mak11 is required at an early 60S assembly step either for Rlp24 association to precursor complexes or for the stability of these particles.

To further investigate the destabilization of Rlp24 that follows the functional inactivation of MAK11, we transformed a strain expressing TAP-tagged Rlp24 and glucose-repressible MAK11 (LMA515) with a plasmid expressing mak11-2 or a wild type allele. These yeast cells had a thermosensitive or a wild-type phenotype when grown on glucose-containing medium. The levels of Rlp24-TAP at 27°C in the mak11-2 strain were estimated by immunoblotting to be at about 50% of the levels of Rlp24-TAP in the wild type strain (Fig. 6D,E). Strikingly, when we performed a shift of the mak11-2 culture to the non-permissive temperature, Rlp24-TAP levels dropped to barely detectable levels after only one hour of shift to 37°C compared with the wildtype background (Fig. 6D). For a quantitative estimation of the effects of Mak11 inactivation on Rlp24 stability, we looked for the decrease in the amount of Rlp24-TAP after a shift to 37°C, simultaneously with the addition of the translation inhibitor cycloheximide (Fig. 6E). Rlp24-TAP levels decreased at a higher rate in the mak11-2 background than in the wild-type strain with an estimated half-life of 38 minutes for the ts strain and 87 minutes for the wild type strain. Mak11 is thus specifically required to maintain Rlp24 levels in yeast.

RLP24 overexpression complemented the phenotype of the mak11-2 strain, probably via a direct protein-protein interaction, and Rlp24 was destabilized in the absence of Mak11. These data strongly suggest that the essential function of MAK11 in S. cerevisiae is directly linked to 60S ribosomal subunit formation. Since another function has been described for putative orthologues of MAK11 in other organisms, we wondered whether these proteins were involved in the large subunit biogenesis in these organisms.

Skb15, the fission yeast Mak11 homologue is involved in 60S ribosomal subunit biogenesis

Mak11 fission yeast (Skb15) and human (hPip1) homologues were previously described as components of a MAPK signaling pathway, which delivers signals from the cell surface to different effectors [START_REF] Kim | Genetic and molecular characterization of Skb15, a highly conserved inhibitor of the fission yeast PAK, Shk1[END_REF][START_REF] Xia | Regulation of the p21-activated kinase (PAK) by a human Gbeta -like WD-repeat protein, hPIP1[END_REF]. Multiple alignments of the sequences for the human, fission yeast and budding yeast proteins indicated a moderate level of similarity, with about 17% identity between any two aligned sequences (Fig. 7). Reciprocal BLAST searches against entire proteome sequences identified putative orthologues of Mak11 in many eukaryotes.

We wondered whether the fission yeast or the human protein complemented the lethal phenotype of S. cerevisiae cells deleted for MAK11. To test this hypothesis, we cloned the sequences coding for Mak11, Skb15 and hPip1 in vectors that would allow the expression of Nterminal protein A fusions in budding yeast. After transformation of the diploid heterozygote strain containing the deletion of MAK11, sporulation of the diploids was induced and the tetrads were dissected. Only the vectors bearing the MAK11 sequence were able to complement the absence of MAK11 (not shown). Either the sequence divergence is too important to allow functional complementation of MAK11 deletion by Skb15 or hPip1, or Mak11 function is different in budding yeast than Skb15 or hPip1 function in fission yeast or human cells.

Since we could not directly demonstrate the functional conservation of the Mak11 putative homologue Skb15 in budding yeast, we looked for Skb15 potential implication in ribosome biogenesis in S. pombe. We took advantage of a previously described fission yeast strain where the expression of SKB15 is under the control of a repressible promoter [START_REF] Kim | Genetic and molecular characterization of Skb15, a highly conserved inhibitor of the fission yeast PAK, Shk1[END_REF] to assess 60S ribosomal subunit defects when Skb15 was depleted. We compared the polysome profile of extracts from the cells depleted for Skb15 with that of a corresponding wild type strain. As expected for a Mak11 orthologue, depletion of Skb15 led to a polysome profile typical for 60S ribosomal subunit biogenesis defects with a large peak of free 40S, a decrease in the amount of free 60S and a drastic decrease in the polysomes levels (Fig. 8A,B). Skb15 is thus functionally linked to ribosome biogenesis in S. pombe.

To get more mechanistic insights into Skb15 function in fission yeast, we generated a S. pombe strain producing the Skb15 protein fused to the TAP tag. We performed the first step of a TAP purification (TEV eluate) using extracts from this strain and from a corresponding wild type strain as a control. The analysis of co-purified rRNA precursors showed an enrichment of both 7S and 27S fission yeast equivalents of budding yeast pre-rRNA intermediates in the Skb15 associated complex (TEV eluate) in comparison with the wild type control (Fig. 8C). These results indicate that Skb15 is a Mak11 orthologue in S. pombe that associates to pre-60S particles and is required for 60S ribosomal subunit biogenesis in fission yeast.

Discussion

Building pre-ribosomal complexes with WD40 repeats

Based on genetic, co-purification and direct physical links, we show here that Mak11 is an essential, conserved pre-ribosomal protein directly interacting with Rlp24 and affecting Rlp24 stability. Mak11 sequence contains several repeated motifs, with conserved tryptophan and aspartic acid residues and an average length of 40 amino acids (WD40). For the proteins with known structure that contain such motifs it has been shown that the repeated sequences form a circularized beta-propeller structure mediating protein-protein interactions (for review see 33,

51).

There are more than one hundred WD40-repeat proteins in yeast as listed by the Superfam database [START_REF] Madera | The SUPERFAMILY database in 2004: additions and improvements[END_REF]. Almost one fifth of these proteins (22 out of 111) are factors annotated as involved in ribosome biogenesis and assembly -a significant enrichment of this pathway (p < 4*10 -11 ) over a random selection (GO Termfinder, 4). Many of the annotated ribosome biogenesis WD40repeat proteins were shown to directly interact with, or regulate the levels of, other proteins [START_REF] Iouk | Rrb1p, a yeast nuclear WD-repeat protein involved in the regulation of ribosome biosynthesis[END_REF] or to be components of multiprotein subcomplexes. A profusion of WD40-repeat proteins is found in discrete 80S processome subcomplexes. Four out of seven components of the tUTP complex (13) contain WD40-repeat proteins and the UTP "B" complex (9, 29) is composed almost exclusively of WD40-repeat proteins (5/6). One of the first pre-60S subcomplexes to be isolated contains two WD40-repeat proteins, Ytm1 and Erb1, in association with Nop7 [START_REF] Harnpicharnchai | Composition and functional characterization of yeast 66S ribosome assembly intermediates[END_REF][START_REF] Krogan | High-definition macromolecular composition of yeast RNA-processing complexes[END_REF][START_REF] Miles | Ytm1, Nop7, and Erb1 form a complex necessary for maturation of yeast 66S preribosomes[END_REF]. Another subcomplex, transiently associated with late, nuclear pre-60S precursors is composed of four proteins and contains Ipi3 as a WD40-repeat member [START_REF] Krogan | High-definition macromolecular composition of yeast RNA-processing complexes[END_REF][START_REF] Nissan | A preribosome with a tadpole-like structure functions in ATP-dependent maturation of 60S subunits[END_REF].

Two examples of WD40-repeat ribosome biogenesis factors interacting directly with ribosomal proteins for 60S ribosomal subunit assembly are known. Rrb1 interacts with the ribosomal protein Rpl3 in the nucleus and regulates its levels [START_REF] Iouk | Rrb1p, a yeast nuclear WD-repeat protein involved in the regulation of ribosome biosynthesis[END_REF][START_REF] Schaper | A yeast homolog of chromatin assembly factor 1 is involved in early ribosome assembly[END_REF] and Sqt1 interacts with Rpl10 in the cytoplasm [START_REF] Eisinger | SQT1, which encodes an essential WD domain protein of Saccharomyces cerevisiae, suppresses dominant-negative mutations of the ribosomal protein gene QSR1[END_REF][START_REF] West | Defining the order in which Nmd3p and Rpl10p load onto nascent 60S ribosomal subunits[END_REF]. Both these proteins have a role in the association of the corresponding ribosomal protein with the nascent 60S ribosomal subunits and might regulate the levels of the corresponding ribosomal protein. Only one ribosomal protein of the Rpl24e family exist in Archaea and its sequence is closer to the sequence of the pre-ribosomal factor Rlp24 than to the sequence of the yeast ribosomal protein Rpl24 (discussed in 47). Yeast Rlp24 is essential to ribosome assembly, does not participate in translation, and thus may be considered an 'assembly only' version of a ribosomal protein. While it is assumed that most of the ribosomal proteins associate early to the precursors of the ribosomal RNA during ribosome formation, little is known about the timing and potential roles of pre-ribosomal factors in coordinating this assembly process.

Our description of Mak11 as directly interacting with Rlp24 thus fits a pattern established by Sqt1 and Rrb1, factors that contain WD40-repeats and function as partners of ribosomal proteins. Both Sqt1 and Rrb1 were shown to exist mainly as free proteins or small complexes as demonstrated by their sedimentation on sucrose gradients [START_REF] Eisinger | SQT1, which encodes an essential WD domain protein of Saccharomyces cerevisiae, suppresses dominant-negative mutations of the ribosomal protein gene QSR1[END_REF][START_REF] Schaper | A yeast homolog of chromatin assembly factor 1 is involved in early ribosome assembly[END_REF]. However the dynamics of the sedimentation pattern for these proteins under mutant conditions has not been evaluated in these previous studies. We show here that Mak11 is present in the cell both free and in association with pre-60S particles. This association is dynamic, and the relative ratios of the two forms vary under mutant conditions. An increase in the amount of the pre-60S associated Mak11 was observed when Nog1 was depleted, in correlation with the change in Mak11 sedimentation pattern and the increase of the 60S associated fraction. A similar effect was observed for the amount of Sqt1 copurified with pre-ribosomal particles isolated in association with the cytoplasmic GTPase Lsg1.

Blocked particles, containing a dominant negative form of Lsg1 contained higher amounts of Sqt1 and Rpl10 than particles derived from wild-type cells [START_REF] West | Defining the order in which Nmd3p and Rpl10p load onto nascent 60S ribosomal subunits[END_REF].

In conclusion, the WD-40 repeat structure is used by several proteins for binding specifically to ribosomal proteins during ribosome assembly. It would be interesting to test other pre-ribosomal factors having WD-40 repeats for direct physical and functional interactions with ribosomal proteins. It would be important to see whether transient, and possibly regulated interactions between specific pre-ribosomal factors and ribosomal proteins might play a role in the timing of ribosomal protein association with nascent ribosomes.

Functional conservation of Mak11 in eukaryotic ribosome biogenesis

The sequence of Mak11 shows low levels of similarity with sequences of putative orthologues from other eukaryotes. Experiments with Skb15, the Mak11 homologue in S. pombe, allowed us to show that Skb15 associates with precursors of the 60S ribosomal subunits in fission yeast and that depletion of the protein leads to a decrease in free 60S levels. It is thus likely that the putative orthologues of Mak11 are involved in ribosome biogenesis in other eukaryotes as well. Our additional unpublished results suggest that even if SKB15 cannot complement MAK11 deletion, the protein and its mammalian homologues have features similar with Mak11: i) Skb15, when expressed in S. cerevisiae, localized to the nucleus of budding yeast cells with a stronger signal in the nucleolus. With the use of a high-copy plasmid and a strong promoter, the signal for the human Mak11 homologue -hPip1 was also detectable in the nucleus of yeast cells, including the nucleolus. The localization of Skb15 in fission yeast was previously described and the published images of the GFP fusion protein localization [START_REF] Kim | The kelch repeat protein, Tea1, is a potential substrate target of the p21-activated kinase, Shk1, in the fission yeast, Schizosaccharomyces pombe[END_REF] suggest the presence of the protein into the nucleolus of S. pombe cells. Moreover, hPip1 has been identified in highly purified nucleolar fractions of human cells [START_REF] Scherl | Functional proteomic analysis of human nucleolus[END_REF]. ii) Total extracts of S. cerevisiae cells expressing tagged Skb15 were fractionated by sucrose gradient ultracentrifugation. While the tag alone was found exclusively in the upper part of the gradient, tagged Skb15 could also be detected in fractions around 60S -consistent with a weak but significant association of the protein with pre-ribosomal particles (not shown). Skb15 thus contains a nuclear localization signal functional in S. cerevisiae and associate weakly with 60S size particles in budding yeast. iii) In response to various environmental stresses, the changes in SKB15 mRNA levels are similar to the changes in the levels of mRNAs coding for factors predicted to be involved in ribosome biogenesis and assembly in S. pombe [START_REF] Chen | Global transcriptional responses of fission yeast to environmental stress[END_REF].

Both Skb15 and hPip1 have been previously described as p21 activated kinase (PAK) inhibitors [START_REF] Kim | Genetic and molecular characterization of Skb15, a highly conserved inhibitor of the fission yeast PAK, Shk1[END_REF][START_REF] Xia | Regulation of the p21-activated kinase (PAK) by a human Gbeta -like WD-repeat protein, hPIP1[END_REF]. Skb15 was identified by a two-hybrid screen performed using a truncated form of a p21 activated protein kinase, Shk1. SKB15 deletion in S. pombe is lethal. Depletion of Skb15 led to cells with altered morphology and to an increase in the activity of protein kinase Shk1, estimated by the level of Shk1 autophosphorylation. Interestingly, expression of a mouse Skb15 homologue was able to complement the deletion of SKB15 indicating functional conservation between fission yeast and mammals [START_REF] Kim | Genetic and molecular characterization of Skb15, a highly conserved inhibitor of the fission yeast PAK, Shk1[END_REF].

While in vitro experiments were performed with the human Skb15 homologue hPip1

showing that hPip1 could inhibit the activity of human PAK1, no such experiments were reported for Skb15. We tried to reproduce these results with the S. cerevisiae pair of proteins. It has been previously shown that STE20 is functionally equivalent with human PAK1 since its deletion in yeast is complemented by the expression of human PAK1 [START_REF] Brown | Human Ste20 homologue hPAK1 links GTPases to the JNK MAP kinase pathway[END_REF]. We tested in vitro the effect of adding purified Mak11 on the protein kinase activity of isolated Ste20 using MBP (myelin basic protein) as a substrate. Ste20 activity was not affected by added purified Mak11 but we could observe that Mak11 itself was efficiently phosphorylated by Ste20 during these assays (not shown).

A simple hypothesis for the differences between our results and the published studies on Mak11 homologues is that these homologues acquired secondary functions in other eukaryotes, such as protein kinase inhibition. An alternative explanation is that Mak11 homologues function in 60S ribosome formation, as demonstrated here. The stimulation of Shk1 protein kinase activity by Skb15 depletion might thus be a secondary effect of decreasing 60S ribosomal subunits levels. This is an appealing hypothesis since it would suggest that the activation or inhibition of protein kinases might be important in the adaptation of cells to reduced ribosome biogenesis levels. were aligned using ClustalW [START_REF] Thompson | CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice[END_REF] and similar or identical residues were shaded using BOXSHADE so that residues that have similar properties and are found in the three sequences have a black background while those with similar properties in two out of three sequences have a grey background. The residues changes indicated in the alignment correspond to the observed mutations in the mak11-2 strain. Boxed regions correspond to predicted WD40 motifs. The residues changes indicated in the alignment correspond to the observed mutations in the mak11-2 strain.

Tables

Table 1. S. cerevisiae and S. pombe strains used in this study.

Strain

"?" -genotype not tested * -Band position is indicated for the proteins identified from the gel shown in Figure 3. 3

  have been previously described as direct binders and inhibitors of p21 activated protein kinases (PAK), Shk1/Pak1 in fission yeast and Pak1 in H. sapiens. PAKs link different receptors with modification of protein substrates by phosphorylation in the mitogen activated protein-kinase (MAPK) pathway and are directly activated by small GTPases of the Rho or Rac families (for a review see 21). PAKs activation or inhibition affect major cellular pathways since PAK substrates play important roles in cell polarity and morphology, mitotic exit and cytokinesis and mediate cellular responses to external stimuli. These signaling pathways are highly conserved as demonstrated by the heterologous complementation of the absence of yeast Ste20 by its human homologue Pak1 (8).

Figure 1 .

 1 Figure 1. Mak11 depletion in yeast cells leads to a block in 27SB to 7S conversion. A.

Figure 2 .

 2 Figure 2. The mak11-2 ts mutant shows alterations in 25S synthesis, cell cycle and pre-

Figure 3 .

 3 Figure 3. Mak11 associated complexes do not contain late pre-60S factors. A. Proteins

Figure 4 .

 4 Figure 4. Dynamic association of Mak11 to pre-60S particles. Sucrose gradient

Figure 5 .

 5 Figure 5. Mak11 and Rlp24 are functionally and physically linked. A. RLP24 is a high-

Figure 6 .

 6 Figure 6. Mak11 stabilizes Rlp24. A. Strains expressing Rlp24-TAP, Ssf1-TAP (B) and

Figure 7 .

 7 Figure 7. Multiple sequence alignment between Mak11 and putative human (hPip1) and

Figure 8 .

 8 Figure 8. The Mak11 homologue in S. pombe, Skb15, is involved in 60S formation in

  his3∆1/his3∆1 leu2∆0/leu2∆0 lys2∆0/LYS2 MET15/met15∆0 ura3∆0/ura3∆0 Mak11::kanMX4/MAK11 (17) LMA160 MATa trp1-289 ura3-52 ade2 leu2-3,-112 arg4 Rlp24-TAP-TRP1 (47) LMA260 MATa ura3∆0 his3∆ leu2∆0 lys2? met15? Mak11::kanMX4 [pFL38-MAK11] This study LMA263-2 MATa ura3∆0 his3∆ leu2∆0 lys2? met15? Mak11::kanMX4 [pFL36CII-mak11-2] This study LMA264 MATa ura3∆0 his3∆ leu2∆0 lys2? met15? Mak11::kanMX4 [pFL36CII-MAK11] This study LMA326 MATa ura3∆0 his3∆ leu2∆0 lys2? met15? Mak11::kanMX4 [pTG189-MAK11] This study LMA375 MATa ura3∆0 his3∆1 leu2∆0 lys2∆0 met15∆0 Mak11-TAP-HIS3 This study LMA364 MATa ura3∆0 his3∆1 leu2∆0 lys2∆0 met15∆0 P GAL1 ::Nog1/KanMX6 Mak11-TAP-HIS3 This study LMA371 MATa ura3∆0 his3∆1 leu2∆0 met15∆0 P GAL1 ::Rlp24/KanMX6 Mak11-TAP-HIS3 This study LMA431 MATa trp1-289 ura3-52 ade2 leu2-3,-112 arg4 Ssf1-TAP-URA3 P GAL1 ::Mak11/KanMX6 This study LMA437 MATa trp1-289 ura3-52 ade2 leu2-3,-112 arg4 Nog1-TAP-TRP1 P GAL1 ::Mak11/KanMX6 This study LMA515 MATa trp1-289 ura3-52 ade2 leu2-3,-112 arg4 Rlp24-TAP-TRP1 P GAL1 ::Mak11/KanMX6 This study LMA600 h -(Msmt0) leu1-32 ura4-D18 adeM6-210 Skb15-TAP/KanMX6 (S. pombe) This study Pb185 h -(Msmt0) leu1-32 ura4-D18 adeM6-210 (S.pombe) (B. Arcangioli) nmt-Skb15 h 90 ade6-M210 leu1-32 ura4-D18 skb15::ura4::nmt1-skb15-ADE2 (S. pombe) (27) SP870 h 90 ade6-M210 leu1-32 ura4-D18 (S. pombe)

Table 2 .

 2 Non-ribosomal proteins identified in complexes purified in association with TAP-1 Mak11 (N) and Mak11-TAP (C). 2

	Name	ORF	pI	kDa	Band* Tag**
	RRP5	YMR229C	6.1	193		N
	UTP22	YGR090W	8.8	140	1	N+C
	ERB1	YMR049C	4.9	92	2, 3	N+C
	MAK5	YBR142W	8.7	87	5	C
	DRS1	YLL008W	5.5	85	4	N+C
	DBP7	YKR024C	10.0	83	8	C
	NOC2	YOR206W	8.9	82	6	C
	NOC3	YLR002C	5.8	76	8	C
	PUF6	YDR496C	6.9	75	8	N+C
	NOG1	YPL093W	9.4	74	10	N
	NOP7	YGR103W	5.4	70	9	N+C
	NOP2	YNL061W	4.8	70	7	N+C
	DBP9	YLR276C	10.0	68	11	C
	ROK1	YGL171W	9.9	64	14	C
	SIK1/NOP56	YLR197W	9.6	57		N
	HAS1	YMR290C	10.1	57		N
	CBF5	YLR175W	9.5	55		N
	MAK11	YKL021C	8.6	54	16	N+C
	NOP12	YOL041C	10.2	52		N
	NSA1	YGL111W	8.0	52	17	N+C
	SSF1	YHR066W	10.1	52	15	N+C
	YTM1	YOR272W	7.0	51	18	C
	RRP14	YKL082C	10.4	50	13	C
	EBP2	YKL172W	6.4	50	12	C
	FPR3/NPI46	YML074C	4.2	47	15	N+C
	FPR4	YLR449W	4.4	44		N
	CIC1/NSA3	YHR052W	9.5	43	19	C
	RLP7	YNL002C	10.2	37	20	N+C
	MAK16	YAL025C	5.1	36	21	C
	RPF1	YHR088W	10.3	35	24	N+C
	NOP1	YDL014W	11.0	34	23	N+C
	BRX1	YOL077C	10.0	34	25	N+C
	RRP1	YDR087C	5.0	33	22	N+C
	RRP15	YPR143W	5.5	28	22	C
	MRT4	YKL009W	9.1	27	27	C
	NOP16	YER002W	10.1	27	26	N+C
	TIF6/CDC95	YPR016C	4.4	26	30	C
	NOP15	YNL110C	10.1	25	27	C
	RLP24	YLR009W	10.4	24	28	C
	LOC1	YFR001W	11.1	24	29	C
	NIP7	YPL211W	10.1	20	31	C

Acknowledgments

We thank Stevan Marcus (University of Alabama, Tuscaloosa, USA) for the Skb15 strain and plasmid, Mingyao Liu (The University of Texas, Houston, USA) for the hPip1 vector, Kathleen Gould (Vanderbilt University, Nashville, TN, USA) for the pFA6a-CTAP-KanMX6 vector, Françoise Stutz (Centre Médicale Universitaire, Genève, Switzerland) for the pFA6a-TAP-Tag-His3MX6 vector, Edouard Bertrand (IGM-CNRS, Montpellier, France) for the Gateway compatible pET32a vector, Ed Hurt (Biochemie-Zentrum, Heidelberg, Germany) for the centromeric plasmid expressing RLP25-eGFP and François Lacroute (Centre de Génétique Moléculaire, Gif-sur-Yvette, France) who provided the high-copy vector genomic S. cerevisiae library. We are grateful to Benoit Arcangioli and Samia Ben Hassine (Institut Pasteur, Paris, France) for help with S. pombe manipulations. This work was supported by the Ministère délégué à l'Enseignement Supérieur et à la Recherche (ACI-BCM0089-2003).

complementation by using a 2 micron plasmid derived from pCM190 [START_REF] Gari | A set of vectors with a tetracycline-regulatable promoter system for modulated gene expression in Saccharomyces cerevisiae[END_REF] expressing RLP24 under the control of a doxycyclin-repressible promoter. Only the pCM190-RLP24 plasmid could complement the mak11-2 ts phenotype, while the complementation failed if the expression of RLP24 was repressed (Fig. 5A).

We wondered if the complementation of the thermosensitive mak11-2 phenotype by RLP24 overexpression could be explained by a direct physical interaction between the two proteins. To this end, we tested in vitro the putative interaction between isolated Rlp24 and Mak11. After mixing total bacterial extracts from E. coli expressing GST-Rlp24 or two ribosomal proteins, used as negative controls, with an extract from bacteria expressing (His) 6 -Mak11, GST tagged proteins were purified on glutathione-Sepharose. The amount of co-purified Mak11 was estimated by Western blotting. In contrast with the negative controls, GST-Rlp24 was able to copurify a substantial amount of Mak11 (Fig. 5B). This result suggests that functional complementation of Mak11 inactivation by Rlp24 correlates with a direct physical interaction, presumably on pre-60S particles in the nucleus.

A direct interaction between Mak11 and Rlp24, and the decrease of the amount of Mak11 associated pre-ribosomal proteins when Rlp24 was depleted (Fig. 3A) suggested a possible role for Mak11 in Rlp24 association to pre-60S complexes. The amount of proteins purified in association with Rlp24 decreased dramatically during Mak11 depletion (not shown). The absence of a complex under these conditions was explained by a decrease in the total amount of the tagged Rlp24 (Fig. 6A). No change was observed when another tagged protein, Ssf1, was used in a similar experiment and only a slight decrease in total protein concentration was observed with Nog1-TAP (Fig. 6B,C). No change was observed in the levels of the mRNA for Rlp24 when tested by reverse transcription and quantitative real-time PCR (not shown). These results strongly ** -Protein identified in purifications performed using N-terminal (N), C-terminal (C) 1 tagged Mak11 or in both (N+C) 2