Yuming Fang

Chi Zhang

Jing Li

Matthieu Perreira

Da Silva

Patrick Le Callet

VISUAL ATTENTION MODELING FOR STEREOSCOPIC VIDEO

Keywords: Visual attention, saliency detection, stereoscopic video, depth feature, planar motion, depth motion

In this paper, we propose a computational model of visual attention for stereoscopic video. Low-level visual features including color, luminance, texture and depth are used to calculate feature contrast for spatial saliency of stereoscopic video frames. Besides, the proposed model adopts motion features to compute the temporal saliency. Here, we extract the relative planar and depth motion for temporal saliency calculation. The final saliency map is computed by fusing the spatial and temporal saliency together. Experimental results show the promising performance of the proposed method in saliency prediction for stereoscopic video.

INTRODUCTION

Visual attention is a significant mechanism in visual perception to rapidly detect the salient information of natural scenes. When observing natural scenes, selective attention would focus on some specific salient information and ignore other unimportant information due to the limited processing resources. Basically, there are two approaches in visual attention: bottom-up and top-down. The bottom-up process is data-driven and task-independent for automatic salient region detection, while the top-down approach is a task-dependent cognitive process related to certain specific task, observers' experiences, etc. [START_REF] Treisman | A feature-integration theory of attention[END_REF][START_REF] Fang | A visual attention model combining top-down and bottom-up mechanisms for salient object detection[END_REF].

Generally, the salient regions extracted by visual attention models can be widely used in many 2D multimedia applications such as visual quality assessment [START_REF] Lin | Perceptual visual quality metrics: A survey[END_REF], coding [START_REF] Borji | State-of-the-Art in Visual Attention Modeling[END_REF][START_REF] Guo | A novel multi-resolution spatiotemporal saliency detection model and its applications in image and video compression[END_REF], segmentation [START_REF] Tian | Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes[END_REF][START_REF] Lei | Evaluation and modeling of depth feature incorporated visual attention for salient object segmentation[END_REF], etc. Previously, there have been various computational visual attention models proposed for 2D images/video [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF]- [START_REF] Li | Finding the Secret of Image Saliency in the Frequency Domain[END_REF]. Itti et al. proposed an early visual attention model for images by feature contrast from luminance, color, and orientation [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF]. Following this work, Harel et pixels in [START_REF] Harel | Graph-based visual saliency[END_REF]. Le Meur et al. proposed a saliency detection model by the characteristics of the human visual system including contrast sensitivity function, visual masking, and center-surround interactions [START_REF] Le Meur | A coherent computational Approach to model the bottom-up visual attention[END_REF]. Some studies try to extract the saliency information of images from transform domain [START_REF] Hou | Saliency Detection: A spectral residual approach[END_REF][START_REF] Fang | Saliency detection in the compressed domain for adaptive image retargeting[END_REF][START_REF] Li | Finding the Secret of Image Saliency in the Frequency Domain[END_REF]. Recently, machine learning techniques are widely used for visual attention modeling [START_REF] Judd | Learning to predict where humans look[END_REF][START_REF] Li | Visual Saliency with Statistical Priors[END_REF]. More related works can be referred to the study [START_REF] Borji | State-of-the-Art in Visual Attention Modeling[END_REF].

In the past decade, there have been various emerging stereoscopic multimedia applications with the rapid development of stereoscopic display technologies, including 3D visual quality assessment [START_REF] Shao | Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties[END_REF][START_REF] Wang | Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images[END_REF][START_REF] Huynh-Thu | The Importance of Visual Attention in Improving the 3DTV Viewing Experience: Overview and New Perspectives[END_REF], 3D video coding [START_REF] Ma | Low Complexity Adaptive View Synthesis Optimization in HEVC Based 3D Video Coding[END_REF][START_REF] Gu | Fast segmentwise DC coding for 3D video compression[END_REF], 3D content generation [START_REF] Cao | Converting 2D Video to 3D: An Efficient Path to a 3D Experience[END_REF], etc. For these 3D multimedia applications, the integration of visual 3D perception models can be used to improve the performance of these related 3D multimedia processing algorithms. Stereoscopic visual attention, an important stereoscopic visual perception process, can be used to extract the salient regions in stereoscopic visual content for various stereoscopic multimedia applications.

Currently, there are some studies focusing on investigating stereoscopic visual attention modeling. Chamaret et al. proposed a saliency detection algorithm for 3D rendering [START_REF] Chamaret | Adaptive 3d rendering based on region-of-interest[END_REF]. In that study, the disparity map is used to weight the 2D saliency map to calculate the final saliency map for 3D images [START_REF] Chamaret | Adaptive 3d rendering based on region-of-interest[END_REF]. Lang et al. constructed an eye tracking database for stereoscopic images and investigated the influence of depth in stereoscopic visual attention modeling [START_REF] Lang | Depth matters: influence of depth cues on visual saliency[END_REF]. In the study [START_REF] Wang | A Computational Model of Stereoscopic 3D Visual Saliency[END_REF], Wang et al. designed stereoscopic saliency detection models by combing depth saliency and 2D saliency with the fusion methods of summation and multiplication. An eye tracking database was also built to demonstrate the performance of the stereoscopic saliency detection models [START_REF] Wang | A Computational Model of Stereoscopic 3D Visual Saliency[END_REF]. Recently, Fang et al. proposed a stereoscopic visual attention model by combining 2D saliency map, depth saliency map and center bias map [START_REF] Fang | Saliency Detection for Stereoscopic Images[END_REF].

Most of the existing stereoscopic visual attention models introduced above are proposed for stereoscopic images. The research on visual attention modeling for stereoscopic video is still limited in the research community. Compared with the visual attention modeling for stereoscopic images, the influence of motion has to be considered in visual attention modeling for stereoscopic video. Basically, there are two types of motion in stereoscopic video: the planar motion and depth motion. In this paper, we propose a novel computational visual attention model for stereoscopic video. Low-level features including color, luminance, texture and depth are extracted to compute the feature contrast for spatial saliency prediction. In addition, planar motion and depth motion are computed for temporal saliency estimation. The final saliency map of stereoscopic video is computed by combining the spatial and temporal saliency maps.

The remaining of this paper is organized as follows. Section 2 introduces the proposed model in detail. In Section 3, we conduct the comparison experiment to demonstrate the performance of the proposed model. The final section concludes the study.

PROPOSED METHOD

As described in the previous section, we propose a visual attention model for stereoscopic video by combining the spatial and temporal saliency maps. The proposed model firstly extracts the features of luminance, color, texture and depth from stereoscopic video. Then it computes the feature contrast for these features for spatial saliency calculation. Additionally, the planar and depth motion is extracted from the stereoscopic video for temporal saliency computation. We will introduce the details step by step in the following subsections.

Feature Extraction

In the proposed method, we extract three types of features: 2D feature, depth feature and motion features. The 2D and depth features are used to calculate the feature contrast, which has been widely used for visual attention modeling in the literature. For the motion feature, we extract the planar and depth motion for temporal saliency calculation.

In the proposed model, we extract the feature contrast from low-level features of color, luminance, texture and depth for the proposed model. It is well known that DCT (Discrete Cosine Transform) is a superior representation of energy compactness and most energy is concentrated on a few lowfrequency coefficients. Because of this, DCT has been widely used in various multimedia processing applications. Inspired by a recent study which uses DCT coefficients in saliency estimation [START_REF] Fang | Saliency detection in the compressed domain for adaptive image retargeting[END_REF], DCT coefficients are adopted to compute the feature contrast for stereoscopic video in this study.

The input image is firstly transformed in YCbCr color space and divided into small image patches. For each image patch, the DCT coefficients are adopted to represent the energy. Here As we know, the Cb and Cr components mainly include color information in images. Thus, we only use AC coefficients in Y component to represent the texture feature for each image patch. In each DCT block, the first several low-frequency coefficients in the left-upper corner include most energy. Thus, similar with the study [START_REF] Fang | Saliency Detection for Stereoscopic Images[END_REF], we only use the first nine AC coefficients in each DCT block with zig-zag scanning to represent the texture feature T :

T = {Y AC1 , Y AC2 , ..., Y AC9 }.
For stereoscopic video, the disparity map shows the parallax of image pixels between the left-view and right-view image pair. It is estimated in unit of pixels for disparity system. Here, we first estimate the disparity map for depth feature extraction. The disparity map P is computed with the variational algorithm by Chambolle and Pock [START_REF] Chambolle | A first-order primal-dual algorithm for convex problems with applications to imaging[END_REF]. The depth map P ′ can be calculated based on the disparity as follows [START_REF] Wang | A Computational Model of Stereoscopic 3D Visual Saliency[END_REF]:

P ′ = v/(1 + d • h P • w ) (1)
where v denotes the viewing distance; d is the interocular distance; w and h are the width (in cm) and horizontal resolution of the display screen, respectively. After the depth map is computed, DC coefficients of image patches in the depth map are used as the depth feature in the proposed model. The depth feature D can be represented as: D = P ′ DC . There are two types of motion in stereoscopic video: the planar motion and depth motion. The motion map of the left view is first estimated by optical flow [START_REF] Sun | Secrets of optical flow estimation and their principles[END_REF]. The motion map can be represented as the motion vectors in x and y directions: M x (i, j, t) and M y (i, j, t), respectively (where (i, j) denotes the pixel location in the image; t represents the t-th frame.). The depth motion M d can be computed as follows [START_REF] Li | Methods for assessment and prediction of QoE, preference and visual discomfort in multimedia application with focus on S-3DTV[END_REF].

M d (i t , j t , t) = P (i t + M x (i t , j t , t), j t + M y (i t , j t , t), t + 1) -P (i t , j t , t) ( 2
)
where P is the disparity map. Since the depth motion would influence the 2D motion estimation in x-direction, the 2D motion map M x (i, j) is actually a combination of the depth motion and 2D x-direction motion. Thus, the planar x-direction motion can be computed by removing depth motion part from M x (x, y). The planar motion at x and y directions can be computed as follows [START_REF] Li | Methods for assessment and prediction of QoE, preference and visual discomfort in multimedia application with focus on S-3DTV[END_REF].

M px = M x - 1 2 M d ( 3
)
M py = M y (4)

Saliency Estimation

It is well known that the salient regions in images pop out due to its high feature contrast from their surround regions.

The salient regions can be detected by the center-surround differences between image patches. Following the study [START_REF] Fang | Saliency detection in the compressed domain for adaptive image retargeting[END_REF], we compute the saliency value of each image patch by the feature differences between this image patch and all the others in the image. A Gaussian model of spatial distances between image patches is adopted to weight the feature differences for saliency estimation. For each image patch i, its saliency value S m i from feature m can be calculated as:

S q i = ∑ j̸ =i 1 σ √ 2π e -d 2 ij /(2σ 2 ) R q ij ( 5
)
where q ∈ {L, C 1 , C 2 , T, D}; d ij is the spatial distance between image patches i and j; R q ij represents the feature difference between image patches i and j; σ is the parameter of Gaussian model, which is used to determine the degree of local and global feature contrast. For the luminance, color and depth features, the feature differences R q ij between image patches i and j can be computed by the normalized differences between the corresponding DC coefficients. For the texture feature composed of nine AC coefficients, the feature difference R T ij can be calculated by the L2 norm. After the feature maps from luminance, color, texture, and depth are computed according to Eq. ( 5), we calculate the spatial saliency map for stereoscopic video by simply linear combination of these feature maps as follows.

S s = 1 Q ∑ q S q (6)
where Q represents the number of features used for spatial saliency prediction.

It is well accepted that object motion is highly correlated with visual attention [START_REF] Abrams | Motion onset captures attention[END_REF][START_REF] Yantis | Abrupt visual onsets and selective attention: Evidence from visual search[END_REF]. In general, an object with strong motion with respect to the background would attract human's attention [START_REF] Abrams | Motion onset captures attention[END_REF][START_REF] Yantis | Abrupt visual onsets and selective attention: Evidence from visual search[END_REF]. The planar motion and depth motion we compute in Eqs. ( 2) -( 4) are absolute local motion. Usually, the object motion we perceive represents the relative motion between the object and background [START_REF] Snowden | Sensitivity to relative and absolute motion[END_REF][START_REF] Poggel | Cueing attention by relative motion in the periphery of the visual field[END_REF]. Thus, we have to calculate the relative planar motion and relative depth motion for temporal saliency extraction of stereoscopic video.

To be aligned with the spatial saliency map computation in Eq.( 5), the motion feature maps are estimated as relative planar motion and relative depth motion by motion feature contrast. For each image patch i, the relative motion value from planar/depth motion can be computed as follows:

v m i = ∑ j̸ =i 1 σ √ 2π e -d 2 ij /(2σ 2 ) R m ij ( 7
)
where m ∈ {M d , M p } (M d and M q represent depth motion and planar motion, respectively). R m ij denotes the depth/planar motion differences between patches i and j. The other parameters are similar with those in Eq. ( 5). Please note that the depth/planar motion difference R m ij is computed by motion normalization as follows:

R m ij = |M m i -M m j | |M m i | + |M m j | + C (8)
where M m ∈ {M d , M p }; C is a small constant. Eq. ( 7) computes the relative motion in a more localized form within a large neighboring region rather than comparing with the global background motion. After the feature maps of planar and depth motion are computed in Eq. ( 7), we estimate the temporal saliency by combining these two motion feature maps as follows.

S t =        0, max i (v p i ) ≤ T && max i (v d i ) ≤ T v d , max i (v p i ) ≤ T && max i (v d i ) > T v p , max i (v d i ) ≤ T && max i (v p i ) > T 1 2 (v d + v p ), otherwise (9)
where i denotes the image patch in the video frame; v p and v d represent the relative planar motion and relative depth motion computed by Eq. ( 7), respectively; T is a threshold value. From Eq. ( 9), we can see that, the temporal saliency would be zero if both relative planar motion and relative depth motion are small; the temporal saliency would be relative planar motion (relative depth motion) if the relative depth motion (relative planar motion) is small; the temporal saliency would be the linear combination of relative planar motion and relative depth motion if both of them are larger than the threshold. With the computed spatial and temporal saliency maps, the final saliency map for stereoscopic video can be obtained by combining these two maps. Here, we consider the spatial and temporal saliency as the same important, and thus we use the simple linear combination method to fuse these two types of saliency maps as follows.

S = 1 2 (S s + S t ) (10)

EXPERIMENTAL RESULTS

In this section, we conduct the comparison experiment to show the performance of the proposed visual attention model of stereoscopic video. We use a subset of the eye tracking database [START_REF] Fang | An eye tracking database for stereoscopic video[END_REF] to conduct the experiment. This database includes video sequences with various content with different levels of 3D effect, aesthetic composition, variations in colour, environment, motion, texture, light, etc. [START_REF] Fang | An eye tracking database for stereoscopic video[END_REF]. The stereoscopic video sequences are obtained by the Panasonic AG-3D camera and Intel SATA3 SSDs to record the visual content. These video sequences are recorded with the resolution of 1920 × 1080. The eye tracking data is obtained from the recording data by the SMI RED (4 firewire) remote eye-tracker working at 60Hz. A set of 40 subjects from iment and they were asked not to move during the experiment. The stereoscopic video sequences were displayed on a 26-inch Panasonic BT-3DL2550 LCD screen with the refresh rate of 60Hz and resolution of 1920 × 1080. The video sequences were viewed by subjects with a pair of passive polarized glasses. The lab environment luminance was adjusted for subjects with an appropriate size for the pupil during the eye tracking experiment. The gaze points recorded by the eye tracker are used to create the fixation density map, which is used as the ground truth of saliency estimation for stereoscopic video.

In this experiment, we evaluate the performance of the proposed model by comparing the saliency maps from the computational visual attention models with the fixation density map from eye tracking data. In general, an effective visual attention model can predict the saliency map similar with the fixation map. We use three common methods to evaluate the performance of the proposed model: Receiver Operating Characteristics (ROC), Linear Correlation Coefficient (CC), and Normalized Scanpath Saliency (NSS).

In the research community, ROC curve is widely adopted for performance evaluation of visual attention models. Through the defined threshold, the saliency map from visual attention model can be divided into salient points and nonsalient points. There are target points and background points in the fixation map from eye tracking data. The True Positive Rate (TPR) is computed as the percentage of target points falling into the salient points from the visual attention model, while the False Positive Rate (FPR) is calculated by the percentage of background points falling into the salient points from the visual attention model. The ROC curve of the visual attention model can be obtained as the curve of TPR vs. FPR through defining different thresholds. The area under ROC curve (AUC) provides an overall performance evaluation. A better visual attention model is expected to have a larger AUC value.

CC can be used to measure the degree of linear correlation between the saliency map and fixation map. Here, Pearson's correlation coefficient between two variables is used to compute CC for visual attention models. It is computed by the covariance of the saliency map and fixation map divided by the product of the standard deviations as:

CC(s, f ) = cov(s, f ) σ s σ f ( 11
)
where s and f are saliency map and fixation map, respectively. The CC values are in the range of [0,1] and with larger CC value, the visual attention model can obtain better performance of saliency prediction. Besides, we also use NSS to evaluate the performance of the proposed model. It is defined by the response value at human fixation locations in the normalized saliency map with zero mean and unit standard deviation as:

N SS(s, f ) = 1 σ s (s(i f , j f ) -µ s ) (12)
where (i f , j f ) is the pixel location in the fixation map; µ s is the mean value of the saliency map; s and f are the saliency map and fixation map, respectively; σ s is the standard deviation of the saliency map. Usually, with the higher the NSS value, the visual attention model can estimate better saliency results.

In this experiment, we compare the proposed model with several existing studies including Itti-2D [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF], Fang-3D [START_REF] Fang | Saliency Detection for Stereoscopic Images[END_REF], Seo-2DV [START_REF] Seo | Static and space-time visual saliency detection by self-resemblance[END_REF]. Specially, Itti-2D is a classical visual attention model for 2D images; Fang-3D is a visual attention model proposed recently for stereoscopic images; Seo-2DV is a saliency detection model for 2D video sequences. The comparison experimental results are shown in Table . 1. From this table, we can see that Fang-3D and Seo-2DV can obtain much better performance than Itti-2D, which mean that the recent 3D saliency detection model [START_REF] Fang | Saliency Detection for Stereoscopic Images[END_REF] and 2D video saliency detection model can predict more accurate saliency information for stereoscopic video than the classic model [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF]. Compared with the other existing related saliency detection models, the proposed model can obtain higher values of AUC, CC and NSS. This means that the proposed model can obtain the best performance of saliency prediction for stereoscopic video among the compared models.

In Fig. 1, we also provide some comparison samples from different saliency detection models. From these comparisons, we can find that Itti-2D can only detect the contour information in the images. The Fang-3D model would lost some salient regions. On the contrary, the proposed model can obtain better saliency maps than other existing ones.

CONCLUSION

In this paper, we propose a novel visual attention model for stereoscopic video by combining the spatial and temporal saliency. The features of luminance, color, texture and depth are extracted to compute the feature contrast, which is adopted for spatial saliency prediction for stereoscopic video. For temporal saliency estimation, we consider both of the relative planar and depth motion. Experimental results show the promising performance of the proposed model in saliency prediction of stereoscopic video. In the future, we will further investigate the combination methods for different feature maps in the final saliency prediction for stereoscopic video.

  al. used graph theory to better measure the saliency of image This work was supported in part by the Natural Science Foundation of China under Grant 61571212 and by the Natural Science Foundation of Jiangxi under Grant 20151BDH80003.

  , we use the DC coefficient of Y component to represent the luminance feature of each image patch. Assume that Y DC is the DC coefficient of Y component, the luminance feature L can be represented as: L = Y DC . The DC coefficients of Cb and Cr components are used to represent the color features of image patches. Assume that Cb DC and Cr DC are the DC coefficients of Cb and Cr components, respectively, The color features of each image patch C 1 and C 2 can be denoted as: C 1 = Cb DC and C 2 = Cr DC .

Fig. 1 .

 1 Fig. 1. Comparison of different saliency detection algorithms. First column to the final column: original images; saliency map from Itti-2D, Fang-3D, the proposed model; the ground truth.

Table 1 .

 1 Comparisons of different saliency detection models Models Itti-2D [4] Fang-3D [24] Seo-2DV [36] Proposed Model

	AUC	0.6598	0.7294	0.7303	0.7632
	CC	0.2302	0.2562	0.2643	0.3102
	NSS	0.5886	0.8499	0.8542	0.9155


Nathalie Nevo

Lucie Thomas

Cerina Chhuon

Zuzanna Andrzejewska

Joanna Lipecka

François Guillonneau

Anne Bailleux

Aleksander Edelman

Corinne Antignac

Ida Chiara Guerrera
email: chiara.guerrera@inserm.fr

Ida Chiara Guerrera

site Broussais Hôpital Necker

Rue Maria

Helena Vieira

Da Silva

Impact of cystinosin glycosylation on protein stability by differential dynamic SILAC

Keywords: mass spectrometry PDI, protein disulfide isomerase -glycosidase F enzyme RIA, relative isotope abundance RSLC, rapid separation liquid chromatography SILAC

Cystinosis is a rare autosomal recessive lysosomal storage disorder characterized by intralysosomal accumulation of cystine. The causative gene for cystinosis is CTNS, which encodes the protein cystinosin, a lysosomal proton-driven cystine transporter. Over 100 mutations have been reported, leading to varying disease severity, often in correlation with residual cystinosin activity as a transporter and with maintenance of its protein-protein interactions.

In this study, we focus on the ΔITILELP mutation, the only mutation reported that sometimes leads to severe forms, inconsistent with its residual transported activity. ΔITILELP is a deletion that eliminates a consensus site on N66, one of the protein's seven glycosylation sites. Our hypothesis was that the ΔITILELP mutant is less stable and undergoes faster degradation.

Our dynamic SILAC study clearly showed that wild-type cystinosin is very stable, while ΔITILELP is degraded three times more rapidly. Additional lysosome inhibition experiments confirmed ΔITILELP instability and showed that the degradation was mainly lysosomal. We observed that in the lysosome, ΔITILELP is still capable of interacting with the V-ATPase complex and some members of the mTOR pathway, similar to the wild-type protein. Intriguingly, our interactomic and immunofluorescence studies showed that ΔITILELP is partially retained at the endoplasmic reticulum (ER). We proposed that the ΔITILELP mutation causes protein misfolding, ER retention and inability to be processed in the Golgi apparatus, and we demonstrated that ΔITILELP carries high-mannose glycans on all six of its remaining glycosylation sites. We found that the high turnover of ITILELP, due to its immature glycosylation state in combination with low transport activity, might be responsible for the phenotype observed in some patients.

INTRODUCTION

Cystinosis (OMIM 219800) is a rare autosomal recessive lysosomal storage disorder characterized by intralysosomal accumulation of cystine, leading to impaired function of multiple organs [START_REF] Gahl | Cystinosis[END_REF]. Based on the age of onset and severity of symptoms, three clinical forms of cystinosis have been described: infantile (severe), juvenile and ocular (mild).

The causative gene for cystinosis is CTNS, which encodes the protein cystinosin, a lysosomal protondriven cystine transporter [START_REF] Town | A novel gene encoding an integral membrane protein is mutated in nephropathic cystinosis[END_REF][START_REF] Cherqui | The targeting of cystinosin to the lysosomal membrane requires a tyrosine-based signal and a novel sorting motif[END_REF][START_REF] Kalatzis | Cystinosin, the protein defective in cystinosis, is a H(+)-driven lysosomal cystine transporter[END_REF]. Recently, we have shown that in addition to transporting cystine, cystinosin is involved in nutrient-sensing in kidney proximal tubular cells via interactions with v-ATPase, Ragulator and Rags [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF], and that the lack of cystinosin results in dysregulation of the mTOR signaling pathway.

Cystinosin is a 367-amino-acid (AA) protein with a nominal mass of 41.7 kDa. It has a short cytosolic 10-AA C-terminal tail, seven transmembrane (TM) domains spanning the lysosomal membrane and a 128-AA N-terminal region in the lysosomal lumen bearing seven N-glycosylation consensus sites N-X-S/T (N36, N41, N51, N66, N84, N104 and N107, hereafter referred to as N1 to N7) (Figure 1A). The molecular weight (MW) of mature cystinosin is estimated at approximately 56 kDa. Cystinosin contains two lysosomal targeting motifs: a classical tyrosine-based motif (GYDQL) in the C-terminal tail and a conformational signal located on the fifth inter-TM loop [START_REF] Cherqui | The targeting of cystinosin to the lysosomal membrane requires a tyrosine-based signal and a novel sorting motif[END_REF][START_REF] Andrzejewska | Lysosomal Targeting of Cystinosin Requires AP-3[END_REF].

Most early functional studies conducted on a set of 31 cystinotic mutations revealed a correlation between cystine transport activity and severity of symptoms [START_REF] Kalatzis | Cystinosin, the protein defective in cystinosis, is a H(+)-driven lysosomal cystine transporter[END_REF]. Most infantile mutations abolished cystine transport, whereas less severe mutations only reduced it [START_REF] Kalatzis | Cystinosin, the protein defective in cystinosis, is a H(+)-driven lysosomal cystine transporter[END_REF]. Patients affected by severe symptoms harbor severe mutations on both alleles, while patients with milder forms usually carry nonsevere mutations on both alleles or in association with a severe mutation. However, some discrepancies have been observed, such as with I67-P73del (ΔITILELP), which leads to 19% residual transport activity and is considered a non-severe mutation [START_REF] Kalatzis | Cystinosin, the protein defective in cystinosis, is a H(+)-driven lysosomal cystine transporter[END_REF]. Individuals with the ΔITILELP mutation on both alleles have the juvenile form, whereas those with the ΔITILELP mutation on one allele in association with the 57 kb deletion (the most common severe mutation) can develop the infantile form [START_REF] Heil | The molecular basis of Dutch infantile nephropathic cystinosis[END_REF][START_REF] Kalatzis | Molecular pathogenesis of cystinosis: effect of CTNS mutations on the transport activity and subcellular localization of cystinosin[END_REF][START_REF] Midgley | Natural history of adolescent-onset cystinosis[END_REF]. ΔITILELP is an in-frame deletion adjacent to an N-glycosylation site at position 66. The ΔITILELP mutation (deletion of AA67-73) is predicted to prevent the recognition consensus sequence of N-glycosylation N-X-S/T on NIT (AA66-68) by oligosaccaryltransferase. Therefore, ΔITILELP results in the removal of seven amino acids in the N-terminal luminal region and the loss of glycosylation on N66, the fourth of seven N-glycosylation sites [START_REF] Marshall | Glycoproteins[END_REF]. To date, ΔITILELP is the only mutation detected in patients that leads to faulty glycosylation.

As glycosylation of lysosomal membrane proteins is known to protect from proteolysis [START_REF] Kundra | Asparagine-linked oligosaccharides protect Lamp-1 and Lamp-2 from intracellular proteolysis[END_REF], we hypothesized that faulty glycosylation caused by ΔITILELP might influence the stability of the protein. To investigate the stability of ΔITILELP, we compared its degradation rate to wild-type (WT) cystinosin. As additional controls, we also studied the degradation of two mutations that do not impact glycosylation sites (N288K and N323K). The classical approach for protein-turnover studies is pulse-chase labeling with radioactive amino acids. We used an alternative approach based on mass spectrometry, known as dynamic stable isotope-labeling by amino acids in cell culture (dynamic SILAC) [START_REF] Doherty | Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC[END_REF][START_REF] Beynon | The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale[END_REF]. In this metaboliclabeling time-course experiment, the ratio of the unlabeled and labeled peptides of a protein reflects the ratio of pre-existing and newly synthesized proteins at each measured time point. It has been shown that from these values, the degradation rate of the proteins can be successfully calculated by curve-plotting or mathematical formulas [START_REF] Schwanhäusser | Global quantification of mammalian gene expression control[END_REF][START_REF] Doherty | Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC[END_REF].

To expand our knowledge of the cellular mechanisms involved in the pathogenicity of ΔITILELP, we analyzed its fate in the cell by studying its glycosylation, cellular localization and specific protein partners, as compared to WT cystinosin.

EXPERIMENTAL PROCEDURES

Antibodies

Antibodies were obtained from the following sources: antibody to PDI and calnexin from Enzo Life Sciences, antibody to GFP from Roche, anti-mouse IgG-HRP from GE Healthcare, and Alexa Fluor 555and 647-conjugated secondary antibodies from Life Technologies. The Lamp-1 (1D4B) monoclonal antibody developed by J. Thomas August was obtained from the Developmental Studies Hybridoma Bank, developed under the auspices of the National Institute of Child Health and Human Development, and maintained by the Department of Biology at the University of Iowa, Iowa City, Iowa, USA.

Cell culture

NIH/3T3 fibroblast cell lines were grown in DMEM supplemented with 10% foetal bovine serum (FBS), 100 U/mL penicillin, 0.1 mg/mL streptomycin and 2 mM L-glutamine (complete DMEM medium) (all from Life Technologies).

For dynamic SILAC studies, NIH/3T3 cells stably expressing different fusion proteins were plated at 4×10 6 cells per 150-mm cell-culture dish. After removing the complete DMEM medium, the cells were quickly washed twice with PBS and then incubated with DMEM medium lacking arginine and lysine, supplemented with 175.2 mg/l [ 13 C 6 ] L-arginine, 84 mg/l [ 13 C 6 ] L-lysine, 10% dialysed FBS (all from ThermoFisher), 100 mg/L proline (Sigma-Aldrich), 100 U/mL penicillin, 0.1 mg/mL streptomycin and 2 mM L-glutamine for chase periods of 0, 6, 18 and 24 h. At these times, the cells were treated for immunoprecipitation before being processed for proteomic turnover analysis.

To calculate the cell-growth rate (D), WT and mutant cells were plated in 10-cm tissue-culture dishes at 1.3×10 6 cells per 150-mm cell-culture dish, cultivated for 24 h, collected at several time-points (0, 6, 18 and 24 h) and then counted using the Beckman Coulter Z2 cell counter. The cell growth rate (D) was calculated by fitting the number of cells (y) at different times (x) to an exponential growth curve in Microsoft Excel, using the formula: y = a * eDx, where "a" is the number of cells at 0 h.

Generation of stable cell lines

To generate NIH/3T3 fibroblast cell lines stably expressing human protein cystinosin-EGFP, the construct pCTNS-EGFP (3) was sub-cloned into the lentiviral pRRL.SIN.cPPT.PGK/WPRE vector [START_REF] Zufferey | Woodchuck hepatitis virus posttranscriptional regulatory element enhances expression of transgenes delivered by retroviral vectors[END_REF]. The cystinosin mutants ΔITILELP, N288K, N323K (5) and N1N7 (the seven putative N-glycosylated sites mutated in alanine) were obtained by the modification of the pRRL.SIN.cPPT.PGK/WPRE-CTNS-EGFP construct with specific mutagenic primers using the Stratagene QuikChange® Site-Directed Mutagenesis Kit according to the manufacturer's recommendations. Lentiviruses were produced in HEK293T cells as previously described [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. NIH/3T3 cells were transduced by lentiviral particles containing CTNS-EGFP or its mutated forms at an MOI of 7 in the presence of 8 µg/mL polybrene.

Generation of EGFP fusion constructs and transfection of NIH/3T3 fibroblasts

The pCTNS-EGFP construct and its mutant form ΔITILELP have been described previously [START_REF] Cherqui | The targeting of cystinosin to the lysosomal membrane requires a tyrosine-based signal and a novel sorting motif[END_REF]. For the mutant N66A (the N66 glycosylation site mutated in alanine), the modification of the pCTNS-EGFP construct (3) was carried out using the Stratagene QuikChange Site-Directed Mutagenesis Kit according to the manufacturer's recommendations. NIH/3T3 fibroblasts were transfected with 2 µg of the different constructs using Lipofectamine® 2000 reagent (Invitrogen) according to the manufacturer's protocol.

Cells were lysed 48 h after transfection for western blotting.

Co-immunoprecipitation and immunoblotting

Co-immunoprecipitation and western blotting experiments on cystinosin-EGFP and its mutants in NIH/3T3 cells were performed as previously described [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. Immunoprecipitated calnexin was expressed as a ratio of the signals of calnexin and cystinosin-EGFP using BIO-1D software (Vilber Lourmat).

Statistical analysis using the Kruskal-Wallis test (p<0.05) with post-hoc Dunn's test was performed with Prism version 5 (GraphPad software). The threshold for statistical significance was set at p˂0.05. Each bar represents the mean ± SEM from four independent experiments.

Immunofluorescence

NIH/3T3 cells were plated on glass coverslips in 12-well tissue-culture dishes, then 24-38 hours later were treated as previously described [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. For the colocalization analysis, the reticular and lysosomal compartments were defined by delimiting the PDI-or Lamp1-labeled areas, respectively, using ImageJ 1.50 software. The reticular or lysosomal accumulation of cystinosin-EGFP or its mutants was measured as a ratio, expressed in percentage of GFP fusion proteins that colocalized with the PDI-or Lamp1labeled areas. Statistical analysis using the Kruskal-Wallis test with post-hoc Dunn's test was performed with Prism version 5 (GraphPad software). The threshold for statistical significance was set at p˂0.05. Three independent biological replicates were analyzed, and for each experiment, colocalization GFP:PDI or GFP:Lamp1 for five cells of each cell type was performed.

PNGase F and Endo H treatments

Cell lysates were treated for 1 h at 37°C with 100 U of PNGase F or Endo H (BioLabs) per µg of total proteins according to the manufacturer's recommendations except for the denaturation step, which was done at 50°C instead of 100°C. The resulting cell lysates were analysed by western blotting.

Drug treatment and FACS analysis

NIH/3T3 cells stably expressing WT cystinosin-EGFP and ΔITILELP-EGFP were incubated with 10 µM clasto-lactacystin β-lactone (Calbiochem) or 100 nM bafilomycin A1 (SIGMA) for 24 h in the complete DMEM medium. Cells were then harvested by using trypsin, and centrifuged for 2.5 min at 1500 rpm.

The cell pellets were washed once with PBS and resuspended in 500 µl PBS with 1% bovine serum albumin. EGFP expression was analyzed by direct flow cytometry using FACSCalibur with CellQuest software (BD Biosciences). Dead cells and debris were excluded by gating on forward/side light scatter, and 10,000 events corresponding to EGFP-positive cells were analyzed per sample. The average expression level of EGFP in individual NIH/3T3 cells was determined by calculating the geomean fluorescence intensity (GFI). The GFIs of the treated cells were normalized relative to the GFI of the untreated cells (with DMSO vehicle). Statistical analysis using the Kruskal-Wallis test with post-hoc Dunn's test was performed with Prism Version 5 (GraphPad software). The threshold for statistical significance was set at p˂0.05. Each bar represents the mean ± SEM from five independent experiments.

Mass spectrometry (MS) analysis for turnover study

For turnover studies, immunoprecipitated protein samples were resolved by SDS-PAGE on 10% gel and large protein bands corresponding to cystinosin were excised. In-gel trypsin digestion was performed as previously described [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. Nano-LC-MS/MS analysis of in-gel digested samples was performed on an Ultimate 3000 Rapid Separation Liquid Chromatography (RSLC) system coupled to a Q-Exactive Plus mass spectrometer (Thermo Scientific, Waltham, MA, USA). Extracted peptides were resuspended in 0.1% (v/v) trifluoroacetic acid and 10% acetonitrile, then loaded onto a µ-precolumn (Acclaim PepMap 100 C18, cartridge, 300 µm i.d.×5 mm, 5 µm, Dionex), followed by separation on the analytical 50-cm nano column (0.075 mm ID, Acclaim PepMap 100, C18, 2 µm, Dionex). The chromatography solvents were (A) 0.1% formic acid in water, and (B) 80% acetonitrile with 0.08% formic acid. Peptides were eluted from the column using a gradient from 5% to 40% B over 38 min, then analyzed by data-dependent MS/MS, using the top-10 acquisition method. Briefly, the instrument settings were as follows: resolution was set to 70,000 for MS scans and 17,500 for the data-dependent MS/MS scans to increase speed. The MS AGC target was set to 3.10 6 counts with a maximum injection time of 200 ms, while the MS/MS AGC target was set to 1.10 5 with a maximum injection time of 120 ms. Dynamic exclusion was set to 30 s. Raw files were processed using the Proteome Discoverer 1.4.0.288 software (Thermo Scientific, San Jose, CA, USA) and searched using Mascot 2.2 against the Mus musculus Uniprot KB/Swiss-Prot database v.5/7/2012 (16,331 entries) integrated with the sequences of the EGFP cystinosin WTs and mutants.

Search parameters included fixed modification with carbamidomethyl (C) and variable modifications with oxidation (M), Label: 13 C(6) (K), Label: 13 C(6) (R), and two missed cleavages. The enzyme was trypsin, monoisotopic peptide mass tolerance was ±2 ppm, and fragment mass tolerance was ±0.05 Da. The identification validation was performed using a percolator, allowing 1%-5% of target FDR for peptides based on the q-value and determined by the target-decoy approach.

The mass spectrometry proteomics data have been deposited at Panorama Public (https://panoramaweb.org/labkey/Guerrera.url). The MS raw files related to the turnover study have been deposited at the ProteomeXchange Consortium via the PRIDE (17) partner repository with the dataset identifier PXD005357.

Turnover data analysis

The loss of a protein can be calculated from the relative isotope abundance (RIA) at each time point. The RIA represents the portion of the residual pre-existing protein (MS1 area of the light peptides: A L ) versus the total amount of the protein in the cell, both pre-existing (A L ) and newly synthesized (MS1 area of the heavy peptides: A H ), at each time-point. RIA is therefore calculated as:

The MS1 areas A H and A L were extracted for peptides SVSLTVPPVVK, EDGNILGHK, FEGDTLVNR and FSVSGEGEGDATYGK for cystinosin-EGFP using Skyline v2.6.0 (freely available at https://skyline.gs.washington.edu). To insure accuracy in the peak attribution, a dedicated Skyline library was built using the search files from the same samples. Peptide YFPQAYMNFYYK was excluded due to the presence of a methionine partially oxidised under different conditions. A thorough manual check and correction of peak picking was performed based on retention time and mass error. The RIA L,t was calculated for each peptide at each time-point (0, 6, 18 and 24 h) for WT cystinosin and each mutants ΔITILELP, N288K, N323K and N1N7 (deglycosylated form of cystinosin) [START_REF] Doherty | Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC[END_REF][START_REF] Pratt | Dynamics of protein turnover, a missing dimension in proteomics[END_REF]. The rate of loss of the protein, K loss , was calculated fitting the RIA L,t to an exponential decay curve using Excel and GraphPad v7:

K loss was additionally calculated mathematically in Excel, as the average of the K loss at each single timepoint for each peptide [START_REF] Schwanhäusser | Global quantification of mammalian gene expression control[END_REF][START_REF] Pratt | Dynamics of protein turnover, a missing dimension in proteomics[END_REF], in each independent biological triplicate:

The rate of degradation (K deg ) was calculated as the difference between the rate of loss (K loss ) and the cell-growth rate (D):

At least three independent biological replicates were analyzed (four for WT cystinosin-EGFP, three for ΔITILELP, N288K, N323K and N1N7). For each experiment, three to four peptides were measured at 0, 6, 18 and 24 h (details in Table S1). Each mutant was always run in parallel with WT cystinosin-EGFP. The cell-growth rate (D) was measured in at least two independent experiments for each sample (four for WT cystinosin-EGFP, two each for ΔITILELP, N323K, N288K and N1N7) (details in Table S3). Statistical analysis using one-way ANOVA was performed with Prism version 7 (GraphPad software), using K loss values obtained by curve-fitting and formulas, from at least three independent experiments and three to four peptides per time-point. The threshold for statistical significance was set at p˂0.05. Each bar represents the mean ± SD.

Mass spectrometry analysis for interactomic study

For interactomic analysis, co-immunoprecipitated protein samples were concentrated on top of 10% SDS-PAGE gel and excised. In-gel trypsin digestion was performed as previously described [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. Analysis by LC-MS/MS was performed as described in "Mass spectrometry (MS) analysis for turnover study", except for gradient length, which was 120 min rather than 38 min. The mass spectrometry proteomics data and details on the identifications and quantifications have been deposited at the ProteomeXchange Consortium via the PRIDE (17) partner repository with the dataset identifier PXD004948.

Interactomic data analysis of the MS files was processed with MaxQuant software version 1.5. Match-between-runs was allowed using default parameters. The false discovery rate (FDR) for PSM and protein identification was set to 1%, determined by a target-decoy approach. Scores were calculated in MaxQuant as previously described, and left as the default parameters [START_REF] Cox | MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification[END_REF]. The reverse-and commoncontaminant hits were removed from the MaxQuant output. Proteins were quantified according to the MaxQuant label-free algorithm using Label-free Quantification LFQ intensities [START_REF] Cox | MaxLFQ allows accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction[END_REF]. Protein quantification was obtained using at least two peptides per protein. For pathway analysis, we annotated all of the proteins according to the KEGG database, and selected those classified as "protein processing in endoplasmic reticulum".

Three biological replicates were analyzed for WT cystinosin-EGFP, ΔITILELP-EGFP and controls (without GFP). Statistical analysis between WT cystinosin and ΔITILELP was performed after the filtering-out of all proteins identified the negative control with Perseus software (version 1.5.5.3, freely available at www.perseus-framework.org) [START_REF] Tyanova | The Perseus computational platform for comprehensive analysis of (prote)omics data[END_REF]. For statistical comparison, the LFQ data were transformed in log2 and imputed to fill in the missing data points by creating a Gaussian distribution of random numbers with a standard deviation of 33% relative to the standard deviation of the measured values, and a threestandard-deviation downshift of the mean to simulate the distribution of the low signal values. We performed a volcano plot analysis of the data using FDR=0.05 and S0=0.5.

Experimental design and statistical rationale

For the western blot analyses, we performed four independent experiments. Statistical analysis was done with the Kruskal-Wallis test with post-hoc Dunn's test (threshold p˂0.05). For colocalization statistical analysis, we performed three independent biological replicates. Statistical analysis was done with Kruskal-Wallis and post-hoc Dunn's tests (threshold p˂0.05). For drug treatments and FACS analyses, we performed five independent biological replicates. Statistical analysis was done with Kruskal-Wallis and post-hoc Dunn's tests (threshold p˂0.05).

For MS turnover studies, we performed a minimum of three independent biological replicates (four for WT cystinosin-EGFP, three each for ΔITILELP, N288K, N323K and N1N7). For each experiment, three to four peptides were measured at 0, 6, 18 and 24 h (details in Table S1). Each mutant was always run in parallel with WT cystinosin-EGFP. The cell-growth rate (D) was measured in at least two independent experiments for each sample (four for WT cystinosin-EGFP, two each for ΔITILELP, N323K, N288K and N1N7) (details in Table S3). Statistical analysis using one-way ANOVA was performed with Prism version 7 (GraphPad software) using K loss values obtained by a formula (threshold p˂0.05) (details in Table S4a).

RESULTS

Detection of EGFP-tagged cystinosin peptides by MS

To analyze the turnover of WT and mutated cystinosin, we transduced 3T3 cells with lentiviral constructs to stably express the cystinosin-EGFP fusion protein and its mutated forms. The schematic representation of WT cystinosin and ΔITILELP can be found in Figure 1A and 1B. We consistently identified specific cystinosin peptides in accordance with the WT or the mutated forms N288K and N323K (Figure S1). In all analyzed forms of cystinosin, we could identify the peptide AA23-33 (SVSLTVPPVVK). These data suggest that the signal peptide of cystinosin was cleaved after S22, generating a new N-terminal sequence starting with SVSLTVPPVVK, despite previous reports describing an uncleavable signal peptide [START_REF] Cherqui | The targeting of cystinosin to the lysosomal membrane requires a tyrosine-based signal and a novel sorting motif[END_REF][START_REF] Attard | Severity of phenotype in cystinosis varies with mutations in the CTNS gene: predicted effect on the model of cystinosin[END_REF]. Our data agreed with the prediction of the currently used algorithm (http://www.cbs.dtu.dk/services/SignalP) (Figure S2).

ΔITILELP-EGFP mutation results in the loss of seven amino acids and one N-glycosylation site (N66); consequently, its expected molecular mass would be approximately 3 kDa smaller than that of WT cystinosin, if all remaining sites were fully glycosylated. However, the observed molecular mass of the glycosylated form was much lower than expected and inconsistent with the simple deletion of seven amino acids and the loss of one glycan (Figure 1C). Again, the detection of the peptide SVSLTVPPVVK (Figure 1B) for ΔITILELP indicated that the N-terminal domain of the mutated protein was intact.

To further verify the effect of the glycan in position 66 (N66) on the protein electrophoretic profile, we substituted the N-glycosylated site N66 with alanine (N66A). N66A showed a molecular mass shift compared to WT cystinosin that was consistent with the loss of one glycan (Figure 1C).

Half-life of cystinosin and ΔITILELP

To investigate whether N-glycosylation influences the stability of ΔITILELP, we measured protein degradation using a dynamic SILAC approach. We incubated unlabelled cells expressing cystinosin and ΔITILELP with [ 13 C 6 ] L-arginine and [ 13 C 6 ] L-lysine for 6, 18 and 24 h in order to selectively label newly synthesised proteins. The WT cystinosin was used as the main control while N288K and N323K, which do not impact glycosylation, were used as additional controls. An artificial mutant simulating the loss of the seven glycosylations (N1N7) was also investigated.

At each time-point, cystinosin and its mutants were immunoprecipitated and analyzed by LC-MS/MS. For each sample, the MS analysis was performed to confirm the presence of cystinosin or its mutants and to verify the incorporation of labelled amino acids during the time-course. For calculating the turnover rate of a given protein, we used the precursor ion signal (MS1 area) from four labelled and unlabelled peptides from that protein at each time-point (an example for one peptide is shown in Figure 2). The extracted MS1 areas were used to calculate the relative isotope abundance at each time point (RIA t ) for each cell type and each replicate (Table S1). RIA L at time 0 was always set to 1 because the cells contained 100% of the light amino acids at this time-point. The K loss values were obtained either by fitting (RIA L , t) on an exponential decay curve (K loss by formula) [START_REF] Doherty | Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC[END_REF][START_REF] Beynon | The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale[END_REF][START_REF] Pratt | Dynamics of protein turnover, a missing dimension in proteomics[END_REF] or by single-time-point analysis (K loss by fitting) (14, 16) (Table S2). The K loss obtained with the two approaches fit a linear regression curve (R 2 =0.985), suggesting that the K loss values obtained by fitting on an exponential decay curve or on a single-time-point analysis are very similar to each other (Figure S3). The cells expressing cystinosin and its mutants were counted at each time-point to determine the cell-growth rate or dilution rate (D) (Table S3).

The reproducibility of the measurements reported on the exponential decay curves between three different independent biological replicates appeared to be very high (Figure 3) ( 16). The WT cystinosin was found to be very stable, with K loss =0.0434±0.008, while ΔITILELP was degraded three times faster than WT cystinosin, with K loss =0.1291±0.014 (Figure 3, Table S2). The division rates of cells overexpressing cystinosin-EGFP and ΔITILELP-EGFP were similar, indicating that the loss rate of ΔITILELP was due to increased degradation (Figure 3 histograms, Table S4). N288K and N323K showed a tendency, though not significant, toward increased degradation when compared to WT cystinosin. Strikingly, the substitution of seven asparagine residues with alanine residues (N1N7), mimicking complete deglycosylation, resulted in a very stable protein with a turnover similar to that of WT cystinosin (Figure 3).

Cystinosin and ΔITILELP lysosomal degradation

To confirm the higher degradation rate of ΔITILELP, fibroblasts expressing ΔITILELP-EGFP or cystinosin-EGFP were treated for 24 h with bafilomycin A1 (Baf A1), an activity-inhibitor of lysosomes and other acidic compartments. EGFP fluorescence was then measured by flow cytometry. Upon Baf A1 treatment, the EGFP signal was significantly increased for both WT cystinosin and ΔITILELP when compared to DMSO-treated cells (vehicle), indicating that both proteins undergo lysosomal degradation. To test whether the proteasome partially contributes to the degradation of ΔITILELP, the same fibroblasts were treated with clasto-lactacystin β-lactone (CL), a specific proteasome inhibitor. In the fibroblasts expressing cystinosin-EGFP or ΔITILELP-EGFP, the EGFP fluorescence intensity did not change after 24 h of CL treatment, suggesting that proteasome degradation does not contribute significantly to the increased loss of ΔITILELP (Figure 4).

ER retention of ΔITILELP

To investigate whether the ΔITILELP mutation impacts protein-protein interactions, we explored the interactome of ΔITILELP compared to that of WT cystinosin by MS. We compared proteins coimmunoprecipitated by WT cystinosin or by ΔITILELP, then identified 181 such proteins as potentially specific interactants of WT cystinosin and/or ΔITILELP, as they were not identified in the negative control (without GFP) (Table S5). The results of this MS analysis largely confirmed our previously published interaction network generated for WT cystinosin ( 5), as we identified eight subunits of the vacuolar type H + -ATPase (V-ATPase) and Rag GTPase C (Figure 5, full protein list in Table S5). ΔITILELP maintained the same protein-protein interactions as WT cystinosin in the lysosome. However, ΔITILELP, but not WT cystinosin, specifically co-immunoprecipitated 17 proteins directly implicated in protein-processing in the ER (Figure 5).

We investigated the cellular distribution of WT cystinosin and its mutants in cells stably overexpressing EGFP-tagged proteins by immunofluorescence. WT cystinosin-EGFP presented a predominant colocalization with the late endosomal-lysosomal marker Lamp-1 (83.62±1.73%) and a minor colocalization with protein disulfide isomerase (PDI), an ER marker (15.64±2.28%). Similar to WT cystinosin, N288K and N323K showed predominant lysosomal localization in 3T3 cells. In striking contrast, the signal for ΔITILELP corresponding to an ER localization was higher (48.26±2.57%), along with a decreased punctuated late endosomal-lysosomal localization pattern (47.66±2.01%) (Figure 6A and6B).

Multitransmembrane proteins that do not acquire proper configurations accumulate in the ER, where they are recognized by calnexin [START_REF] Lamriben | N-Glycan-based ER Molecular Chaperone and Protein Quality Control System: The Calnexin Binding Cycle[END_REF]. To analyze the interaction between cystinosin and this chaperone, lysates of 3T3 cells stably expressing cystinosin-EGFP or its mutated forms were immunoprecipitated with anti-GFP antibody, and co-immunoprecipitated endogenous calnexin was detected by western blotting. As expected from the localization data, interactions with calnexin were significantly increased for ΔITILELP compared to WT cystinosin, N288K or N323K (Figure 6C and6D). The mutants N66A and N1N7 appeared to localize at the lysosome as well, without any ER retention (Figure S4). Together, these data indicate that the ΔITILELP mutation leads to partial retention of cystinosin in the ER due to misfolding of the protein.

N-linked high-mannose glycans of ΔITILELP

Lysosomal transmembrane proteins are synthesized in the ER, where folding and the initial stages of glycosylation (high-mannose type glycans) occur. They are then targeted to lysosomes via the trans-Golgi network, acquiring further modifications (complex type N-glycans). To verify the glycosylation stage of WT cystinosin and ΔITILELP, we performed enzymatic deglycosylation using the peptide:N-glycosidase F enzyme (PNGase F), which removes all N-linked glycan residues. As a control, we used N1N7, the constitutively deglycosylated form of cystinosin. After incubation with PNGase F, WT cystinosin had the migration profile of N1N7, while N1N7 did not show any changes (Figure 7A), showing that WT cystinosin is indeed heavily glycosylated. ΔITILELP was also completely deglycosylated by PNGase F, showing that it also contains a certain level of glycosylation. N288K and N323K, used as positive controls, showed the same electrophoretic profile as WT cystinosin.

We therefore tested the effect of the Endoglycosidase H enzyme (Endo H), as it specifically hydrolyses non-mature N-glycans (high-mannose type). In contrast to WT cystinosin, ΔITILELP was sensitive to Endo H digestion (Figure 7B), suggesting that ΔITILELP carries high-mannose on its available glycosylation sites.

WT cystinosin is not affected by Endo H digestion, confirming that the WT form contains complex-type glycans, acquired during maturation in the Golgi apparatus.

DISCUSSION

In this study, we investigated the impact of cystinosin deletion ITILELP on the stability of the protein.

The ITILELP mutation disrupts the proper glycosylation of cystinosin on N66. In the homozygous state, ITILELP leads to juvenile cystinosis, which correlates with the ability to maintain minimal cystine transport activity in vitro [START_REF] Midgley | Natural history of adolescent-onset cystinosis[END_REF]. However, when found in the heterozygous state together with a 57 kb deletion on the second allele, the ITILELP mutation can result in the development of the severe infantile phenotype [START_REF] Heil | The molecular basis of Dutch infantile nephropathic cystinosis[END_REF][START_REF] Kalatzis | Molecular pathogenesis of cystinosis: effect of CTNS mutations on the transport activity and subcellular localization of cystinosin[END_REF]. These surprising observations led us to hypothesize that the deletion of seven amino acids, together with the lack of glycosylation on N66, interferes with cystinosin stability.

To investigate ITILELP turnover, we used the dynamic SILAC approach with MS. However, we faced two main challenges. First, cystinosin is not easily detectable by MS in non-fractionated samples, such as a total cell lysate or blood (no records on peptide identification in proteomic studies are reported at nextprot.org). This is probably due to the very low abundance of endogenous cystinosin in cells and to characteristics of its sequence, which contains seven TM domains and seven glycosylation sites. To our knowledge, endogenous cystinosin was identified with two peptides in only one MS study of enriched lysosomal membranes [START_REF] Schröder | Integral and Associated Lysosomal Membrane Proteins[END_REF]. Furthermore, no good antibodies are available to immunoprecipitate endogenous cystinosin from cell lysates in order to enrich endogenous cystinosin. For these reasons, we developed cellular models of stably expressing EGFP-tagged WT and mutated cystinosin. Although these tagged proteins localize correctly in the lysosome and have been previously used for functional studies, we are aware that the use of EGFP-tagged cystinosin is a constraint and we cannot be certain that the endogenous proteins follow exactly the same behaviour. In this study, we detected four peptides of cystinosin (two from WT cystinosin and two specific to mutants) and provided evidence that the Nterminal signal peptide can be cleaved before S23.

The second challenge was that dynamic SILAC, although a well-established approach, can be tricky when studying the turnover of stable proteins. We calculated the K loss from the MS1 areas of the peptides and their labelled counterparts using curve-fitting or mathematical formulas applied to each time-point (reported in different forms in the literature, but equivalent) [START_REF] Schwanhäusser | Global quantification of mammalian gene expression control[END_REF][START_REF] Pratt | Dynamics of protein turnover, a missing dimension in proteomics[END_REF]. Our data confirmed that the K loss values calculated by curve-fitting and mathematical formulas correlate perfectly when many data points are used in the calculations. However, stable proteins such as WT cystinosin are virtually undegraded during the cell cycle/lifespan (rate of degradation: K deg ), and are only diluted upon cell division. In this case, the rate of loss (K loss ) of the protein is very close to the cell-division rate (D), and the K deg =K loss -D value nearly reaches zero. After verifying that D was similar for all of the cell lines studied, we chose to report and compare the K loss of WT cystinosin and the mutants.

Our dynamic SILAC data showed that WT cystinosin is a very stable protein and that the ITILELP mutation increased three-fold the degradation of the protein, while the N288K and N323K mutations did not significantly influence the protein's stability. We clearly showed that ITILELP undergoes lysosomal degradation, as v-ATPase inhibition by bafilomycin A1 (BafA1), but not proteasomal inhibition, resulted in increased cellular levels as observed by an increased fluorescence The rate of lysosomal degradation of ITILELP was three times greater than that of the WT protein, further confirming the instability of this mutant.

We have recently demonstrated that cystinosin mutations, in addition to inefficient efflux of cystine from the lysosome, induce changes in the protein network that might modulate the role of cystinosin in the mTOR pathway [START_REF] Andrzejewska | Cystinosin is a Component of the Vacuolar H+-ATPase-Ragulator-Rag Complex Controlling Mammalian Target of Rapamycin Complex 1 Signaling[END_REF]. For this reason, we decided to investigate the ITILELP protein network. Our interactomic study revealed that similar to WT cystinosin, ITILELP maintains interactions at the lysosomal membrane with the V-ATPase complex and some proteins of the mTOR pathway. The presence of functional ITILELP on the lysosomal membrane aligns with the fact that patients with homozygous ITILELP mutations develop juvenile rather than infantile cystinosis. In addition, ITILELP interacts with 15 proteins of the ER, most of which have been implicated in ER quality-control and processing (e.g. calreticulin, Os9, Sel1l) [START_REF] Bernasconi | Stringent requirement for HRD1, SEL1L, and OS-9/XTP3-B for disposal of ERAD-LS substrates[END_REF]. Immunofluorescence studies confirmed these findings and showed the partial localization of ITILELP in the ER and in lysosomes.

The accumulation of misfolded proteins in the ER can lead to ER stress and cell death. To prevent cell injury, non-native proteins can exit the ER to undergo proteasomal and/or lysosomal degradation [START_REF] Chiang | IRE1 directs proteasomal and lysosomal degradation of misfolded rhodopsin[END_REF][START_REF] Dersh | OS-9 facilitates turnover of nonnative GRP94 marked by hyperglycosylation[END_REF]. To better understand the reasons for ER retention, we further explored the extent of the faulty glycosylation of ITILELP and demonstrated that it carried immature high-mannose glycans in all six remaining sites not directly affected by the mutation, suggesting that it is prevented from reaching the Golgi network for further processing. This is coherent with the observed ITILELP molecular weight by SDS-PAGE. Several studies have been conducted to decipher the glyco-code of processed proteins, revealing the importance of N-glycans in directing protein maturation [START_REF] Hebert | The intrinsic and extrinsic effects of N-linked glycans on glycoproteostasis[END_REF]. The glycan composition controls the calnexin binding cycle, which is important for the recognition of misfolded proteins in the ER [START_REF] Hammond | Role of N-linked oligosaccharide recognition, glucose trimming, and calnexin in glycoprotein folding and quality control[END_REF][START_REF] Helenius | How N-linked oligosaccharides affect glycoprotein folding in the endoplasmic reticulum[END_REF]. Along these lines, the strong interaction shown in our study between ITILELP and the ER chaperone, calnexin, suggested that this mutant was recognized as a non-native protein by the ER quality-control system and was partially retained in this compartment. The N1N7 mutant, which does not carry any glycosylation, is correctly directed from the ER to the lysosome and its stability is not affected.

We suggest that ITILELP is initially retained in the ER, preventing it from being processed in the Golgi to obtain mature glycosylation, but it is directly transported to lysosome via its two targeting sequences.

Further studies will be necessary to fully understand the escape mechanisms of ITILELP from the ER to the lysosome. One possible mechanism is ER macro-autophagy that may be induced by ER stress [START_REF] Høyer-Hansen | Connecting endoplasmic reticulum stress to autophagy by unfolded protein response and calcium[END_REF].

However, some unconventional cellular mechanisms of ER escape by retained proteins have also been described. Non-mature CFTR can be targeted to the plasma membrane, skipping the Golgi compartment via the unconventional GRASP-dependent pathway [START_REF] Gee | Rescue of ΔF508-CFTR Trafficking via a GRASP-Dependent Unconventional Secretion Pathway[END_REF]. Additionally, the formation of ER-derived vesicles referred to as EDEMosomes, which are necessary for ERAD tuning, enable the removal of selected ERAD regulators from the ER and their degradation by proteasomes and endolysosomal proteases. These vesicles contain EDEM1 and Sel1L but can also carry other ER proteins [START_REF] Bernasconi | ERAD and ERAD tuning: disposal of cargo and of ERAD regulators from the mammalian ER[END_REF].

We conclude that the high turnover of ITILELP, due to its immature glycosylation state together with low transport activity, might be responsible for the phenotype observed in some patients who carry this mutation heterozygously, together with the 57 kb deletion. Volcano plot representation of proteins immunoprecipitated by the anti-GFP antibody and identified by mass spectrometry in ΔITILELP-EGFP-vs cystinosin-EGFP-expressing cells. In blue, proteins known to be lysosomal partners of cystinosin. In red, proteins classified by the KEGG pathway database as "protein processing in endoplasmic reticulum".

  2.8 and searched with the Andromeda search engine against the Mus musculus Uniprot KB/Swiss-Prot database 2016_01 (20199 entries), integrated with FASTA sequences of WT cystinosin-EGFP. To search parent mass and fragment ions, we set initial mass deviations of 4.5 ppm and 20 ppm, respectively. The minimum peptide length was set at seven amino acids and strict specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as a fixed modification, whereas oxidation (Met) and N-terminal acetylation were set as variable modifications.

For

  MS-based interactomic studies, we performed three independent biological replicates for WT cystinosin-EGFP, ΔITILELP-EGFP and controls (cells without GFP expressing plasmid). Statistical analysis between WT cystinosin and ΔITILELP was performed with Perseus software (version 1.5.0.31). For statistical comparison, the LFQ data were transformed in log2 and imputed to fill in the missing data points by creating a Gaussian distribution of random numbers with a standard deviation of 33% relative to the standard deviation of the measured values, and with two to five standard deviations downshift of the mean to simulate the distribution of the low signal values. We performed a volcano plot analysis based on the t test, applying FDR=0.05 and S0=1.

Figure 1 .

 1 Figure 1. Protein topology plot and electrophoretic profiles of WT cystinosin and ΔITILELP. (A)

Figure 2 .

 2 Figure 2. MS1 areas of peptide FSVSGEGEGDATYGK in EGFP-tagged cystinosin, ΔITILELP, N288K and

Figure 3 .

 3 Figure 3. Degradation curves of cystinosin and its mutants. RIA (relative isotope abundance, y axis) for

Figure 4 .

 4 Figure 4. Lysosomal degradation pathway of cystinosin. NIH/3T3 cells stably expressing cystinosin-EGFP

Figure 5 .

 5 Figure 5. Protein partners for WT cystinosin and ΔITILELP in lysosomal and reticular compartments.

Figure 6 .

 6 Figure 6. ER retention of ΔITILELP. (A) NIH/3T3 cell lines stably expressing cystinosin-EGFP, ΔITILELP,

Figure 7 .

 7 Figure 7. Deglycosylation of cystinosin and its mutated forms. (A) Lysates of NIH/3T3 cell lines stably

  Figure 1

Figure 3

 3 Figure 3

  Figure 3

ACKNOWLEDGMENTS

We greatly appreciate Prof. R.J. Beynon for valuable advice in turnover data interpretation, Dr. P. Codogno and Dr. A. Delaunay-Moisan for thoughtful discussions, N. Gaudin, M. Garfa-Traore, and R.

Desvaux at Imagine Cell Imaging for expert assistance in confocal microscopy.

FUNDING

This work was supported by grants from the Cystinosis Research Foundation and from the Investments for the Future program (ANR-10-IAHU-01).