Caroline Ego

Lucie Bonhomme

Jean-Jacques Orban De Xivry

David Da Fonseca

Philippe Lefèvre

Guillaume S Masson

Dr Christine Deruelle
email: christine.deruelle@univ-amu.fr

Behavioral characterization of prediction and internal models in adolescents with autistic spectrum disorders

Keywords: Autism, smooth-pursuit, saccades, prediction, internal models, sensorimotor control, adolescents 4

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Making predictions about future sensory events or the consequences of our own actions is a cardinal property of cognitive systems to adapt our behaviors and interact with the environment. Such ability has been largely investigated in motor control where theoretical studies have pointed out the role and the properties of internal models about the dynamics of both inflows and outflows of motor systems (e.g. [START_REF] Wolpert | Forward Models for Physiological Motor Control[END_REF][START_REF] Wolpert | Computational principles of movement neuroscience[END_REF]. In this context, the smooth pursuit system offers a simple but yet powerful empirical framework to investigate these internal models [START_REF] Barnes | The mechanism of prediction in human smooth pursuit eye movements[END_REF][START_REF] Becker | Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target[END_REF][START_REF] Kowler | Davida Teller Award Lecture 2013: The importance of prediction and anticipation in the control of smooth pursuit eye movements[END_REF]. For instance, in response to a fully predictable moving target, smooth eye movements can be initiated in the correct direction and speed before target motion, a behavior called anticipatory pursuit [START_REF] Kowler | Cognitive expectations, not habits, control anticipatory smooth oculomotor pursuit[END_REF]. If the same target is transiently occluded during steady-state pursuit, the oculomotor system is able to maintain the on-going eye movement during several hundred of ms even though the eye velocity decreases during the blanking period [START_REF] Becker | Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target[END_REF][START_REF] Mitrani | Pursuit eye movements of a disappearing moving target[END_REF]. In adults, this decrease in eye velocity induces a lag of the eye with respect to the target position that is compensated by saccades, whose total amplitude is inversely proportional to the residual eye velocity [START_REF] Coppe | Dramatic impairment of prediction due to frontal lobe degeneration[END_REF][START_REF] Orban De Xivry | A dynamic representation of target motion drives predictive smooth pursuit during target blanking[END_REF], 2006). Lastly, when the duration of target blanking is predictable, the pursuit eye velocity starts increasing again before target reappearance (Bennett andBarnes, 2004, 2003;[START_REF] Orban De Xivry | Evidence for synergy between saccades and smooth pursuit during transient target disappearance[END_REF]. Such anticipatory eye acceleration is another signature of predictive mechanisms based upon an internal representation of the target timing. These trial-by-trial saccadic adjustments and anticipatory pursuit reacceleration are considered as markers of the internal model of both target and eye velocities [START_REF] Coppe | Dramatic impairment of prediction due to frontal lobe degeneration[END_REF][START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF] that is used by the automatic predictive mechanisms involved in the sensorimotor control of pursuit eye movements [START_REF] Bogadhi | Dynamic interaction between retinal and extraretinal signals in motion integration for smooth pursuit[END_REF][START_REF] Orban De Xivry | Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics[END_REF]. At a neurophysiological level, empirical evidences highlight the importance of the frontal lobes [START_REF] Barborica | Estimating invisible target speed from neuronal activity in monkey frontal eye field[END_REF][START_REF] Ding | Dissociable frontal controls during visible and memoryguided eye-tracking of moving targets[END_REF][START_REF] Ferrera | Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task[END_REF][START_REF] Lencer | Cortical mechanisms of smooth pursuit eye movements with target blanking. An fMRI study[END_REF][START_REF] Missal | Supplementary eye fields stimulation facilitates anticipatory pursuit[END_REF]) and of the cerebellum [START_REF] Cerminara | An internal model of a moving visual target in the lateral cerebellum[END_REF][START_REF] Lisberger | Internal models of eye movement in the floccular complex of the monkey cerebellum[END_REF][START_REF] Wolpert | Internal models in the cerebellum[END_REF] in building the internal models used for sensorimotor prediction.

Because of their neurological substrates, predictive eye movements have been proposed as reliable biomarkers of neurological and psychiatric disorders involving frontal lobes such as schizophrenia (e.g., [START_REF] Benson | Simple viewing tests can detect eye movement abnormalities that distinguish schizophrenia cases from controls with exceptional accuracy[END_REF] or cortical degeneration (e.g., [START_REF] Coppe | Dramatic impairment of prediction due to frontal lobe degeneration[END_REF].

Recently, a lack of ability to predict future events or to build and update internal models of the world and our own actions has been proposed as a theoretical framework to shed light on the cognitive dysfunctions observed in Autism Spectrum Disorders (ASD) (e.g., [START_REF] Gomot | A challenging, unpredictable world for people with autism spectrum disorder[END_REF][START_REF] Pellicano | When the world becomes "too real": a Bayesian explanation of autistic perception[END_REF][START_REF] Sinha | Autism as a disorder of prediction[END_REF][START_REF] Quattrocki | Autism, oxytocin and interoception[END_REF]; [START_REF] Van De Cruys | Precise minds in uncertain worlds: predictive coding in autism[END_REF][START_REF] Sevgi | Social Bayes: Using Bayesian modeling to study autistic trait-related differences in social cognition[END_REF]. Such inability could impact social, behavioral, cognitive or motor functions and explain some of the clinical hallmarks of this pathology [START_REF] David | A pilot study: coordination of precision grip in children and adolescents with high functioning autism[END_REF]. Interestingly, the neural substrates involved in elaborating internal models (i.e., mainly the frontal lobes and the cerebellum) are thought to be particularly affected in ASD [START_REF] Amaral | Neuroanatomy of autism[END_REF][START_REF] Brambilla | Brain anatomy and development in autism: review of structural MRI studies[END_REF][START_REF] Palmen | Neuropathological findings in autism[END_REF][START_REF] Sparks | Brain structural abnormalities in young children with autism spectrum disorder[END_REF]. Empirical evidences supporting the hypothesis of a deficit in prediction in ASD remain, however, highly controversial and mitigated (e.g. [START_REF] Gowen | Motor abilities in autism: a review using a computational context[END_REF]. A few motor studies reported impairments in predictive control or in internal models in ASD, as illustrated by deficiencies in the temporal coordination between grip and load forces during objects grasping [START_REF] Schmitz | Motor control and children with autism: deficit of anticipatory function?[END_REF]. At perceptual level, the weaker adaptation to complex visual inputs (such as faces or numerosity) in children with ASD compared to typically developing children was taken as evidence of a reduced ability to build internal models of the visual world from their previous, cumulative experience [START_REF] Pellicano | When the world becomes "too real": a Bayesian explanation of autistic perception[END_REF]). However, several other studies failed to find a significant difference in prediction at both perceptual and motor levels between individuals with and without ASD. [START_REF] Blakemore | Tactile sensitivity in Asperger syndrome[END_REF] for instance, showed that the attenuation of the tickling perception due to selfgeneration is present in ASD, clearly indicating that they are able to use a sensory prediction.

In addition, the ability of children with ASD to acquire and adapt internal models of selfgenerated movements is as efficient as that of typically developing children [START_REF] Gidley Larson | Acquisition of internal models of motor tasks in children with autism[END_REF] even though it relies on different motor coordinates [START_REF] Haswell | Representation of internal models of action in the autistic brain[END_REF][START_REF] Marko | Behavioural and neural basis of anomalous motor learning in children with autism[END_REF]. Overall, this second series of studies suggest that motor prediction is intact in this pathology.

To resolve these controversies, there is a strong need for a solid experimental framework able to probe the ability of ASD participants to predictively control behaviors.

Simple visuomotor transformations such as tracking eye movements appear as a good candidate. It is, indeed, a low-level sensorimotor task that involves automatic predictive mechanisms but allows to alleviate biases introducted by high-level perceptual, motor or cognitive processing. Moreover, the maturation of these predictive mechanisms for ocular tracking is now well understood [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF][START_REF] Ego | The saccadic system does not compensate for the immaturity of the smooth pursuit system during visual tracking in children[END_REF]. By estimating pursuit performance for moving targets that are transiently occluded during the steady-state phase of pursuit, contrasted visually-guided (i.e., target present, [START_REF] Ego | The saccadic system does not compensate for the immaturity of the smooth pursuit system during visual tracking in children[END_REF] and predictive (i.e., target absent, [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF] mechanisms were assessed in a large population of participants spanning from 5 to 20 years old. Both a late maturation of the predictive mechanisms as reflected by lower ability to compensate for late smooth pursuit maturation [START_REF] Ego | The saccadic system does not compensate for the immaturity of the smooth pursuit system during visual tracking in children[END_REF] and a weaker accuracy of the internal models governing the synergy between the saccadic and the pursuit systems were observed in young children (<10 years old) [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF].

Interestingly, Ego and collaborators reported that these objective and selective behavioral signatures of the prediction mechanisms can be accurately estimated in severe atypical development, such as in cerebral palsy [START_REF] Ego | Spontaneous improvement in oculomotor function of children with cerebral palsy[END_REF].

Based on the methodology calibrated in these previous works, our objective was to test the prediction hypothesis in ASD in the context of ocular tracking. We investigated a large group of children and adolescent ASD, reasonning that a late maturation shall impact the ASD, but not the matched control, group. Here, we show on the contrary that ASD children and adolescents exhibit similar performance to typically-developing age-matched control but much better performance than 6 years old children, suggesting that automatic predictive mechanisms may in fact be intact in this pathology. This result calls for a more careful and restrictive definition of the prediction hypothesis in ASD.

Methods

Participants

Eye movements were recorded in 40 participants with Autism Spectrum Disorder (ASD) with no intellectual impairment and in 35 age-matched control participants (see Table 1). ASD participants were recruited at the Autism Center of Reference, a specialized clinical center from the Pediatric Psychiatry Unit of the Marseille University Hospital. All participants had normal, or corrected-to-normal vision. They were all evaluated with a WAIS (III/IV) or a WASI test and performance IQ were higher than 70 for all participants (ASD participant included). A statistical difference was found between IQ scores of the two groups (T test 2 tailed, p=0.003) but not between ages (p=0.47). None of the participant with ASD was taking medication susceptible to affect their eye movements at the time of testing. All of them have been evaluated using the ADOS scale and were also asked to complete the Empathy Quotient (EQ) self-report questionnaire (Baron-Cohen and Weelwright, 2004) (see Table 1).

Among the 40 ASD participants, only 3 showed clinical signs of attentional deficits during the clinical evaluation but were still included in the study. For one particular analysis (Figure 6E,F), results from participants with ASD were also compared to those of 60 control participants (aged 5 to 34y) who participated in a very similar previous study [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF] using the same procedures and identical set-ups.

Our objective was to compare the difference in oculomotor parameters between autistic children and age-matched controls to the change in parameters due to development.

Experimental Setup

Participants were seated on a chair with their head restrained by a chin-rest and faced a computer screen (47.5 x 30 cm) placed 57 cm away from them. Eye movements of both eyes were recorded at 500 Hz using an infrared eye tracker (Eyelink 2, SR Research, Ottawa, Canada) for the first 44 participants (28 ASD and 16 control participants). For the 31 additional participants, eye movements were recorded at 1000 Hz using an Eyelink 1000 ( 12ASD and 8 control participants). We checked that all pursuit and saccades parameters were not significantly different between the two recording systems.

Behavioral paradigm

The paradigm was similar to the one used in [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF] (Figure 1). Participants had to pursue a red dot (0.6 deg) displayed at the center of a green bird (horizontal width: 4 deg) that moved horizontally on the video screen. All trials started with an initial fixation period (1 s) on one side of the screen at a position randomly selected between +/-19 and +/-22 degrees of eccentricity. At the end of the fixation period, the target disappeared for 0.3 s (gap period), reappeared and then immediately started to move at 20 deg/s to the other side (i.e. towards the center of the screen) for 2 s. The gap period was introduced to shorten the pursuit onset latency. In the test trials, after 0.6 s, the target disappeared (blanking period) for 0.8 s and then reappeared for the last 0.6 s, as illustrated in Fig. 1. Each participant completed 6 blocks of 20 trials. For the first two trials and two other trials randomly placed in the block, the target was continuously kept visible (control trials) to reinforce the continuous motion of the target (no gap and no blanking period). The direction of the target motion was kept constant within a block but randomized across blocks. Control trials (for which the target was continuously visible) were sorted out from the data set and not analyzed as we focused on the difference in predictive behavior between the two groups of participants. For the quantitative analysis, we defined four successive time periods: T1 for the three first test trials, T2 for the next four, T3 for the next four and, finally T4 for the last five test trials. Using different sized bins allowed us to take into account both the fact that it takes three trials to observe anticipatory eye movements at the beginning of a block design protocole (Bennet et al., 2010) and the timing of the interleaved control trials.

Such binning has been calibrated in previous studies (Coppé et al., 2012;[START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF].

In the test trials, we first measured the visually-guided steady-state pursuit gain defined as the ratio between the mean eye velocity (averaged over a 50ms interval centered at 100ms before target blanking) and the target velocity. When a pursued target disappears, the eye velocity decreases exponentially until a plateau called residual velocity [START_REF] Becker | Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target[END_REF]. After just a few trials and when the timing of target reappearance is known, the pursuit eye velocity can begin to increase prior to target reappearance [START_REF] Bennett | Human ocular pursuit during the transient disappearance of a visual target[END_REF]. Such predictive smooth response was evaluated using the residual gain (computed on a 50ms interval centered 500ms after target disappearance) and the predictive recovery.

The predictive recovery is defined as the slope of the regression line fitted on a 150ms interval of the de-saccaded eye velocity trace, starting 100ms before and ending 50ms after target reappearance. Predictive saccades are saccades triggered in absence of visual information and the latencies of which range between 120 and 800ms after target disappearance (see Orban de [START_REF] Orban De Xivry | Smooth pursuit performance during target blanking does not influence the triggering of predictive saccades[END_REF]. To construct heat maps of saccades endpoints, all saccades endpoints (time of offset and landing position) were replaced by 3D

Gaussian curves. Heat maps of participant groups were constructed with the sum of all Gaussians representing the saccades endpoints divided by their maximum. Colors represent the height of the resulting sum (see [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF] for more details). Note that one autistic kid did not exhibit many saccades during the occlusion and his data had to be removed from the analyses of the precision of predictive saccades.

Saccade-pursuit interaction was studied using both the saccadic eye displacement (SAD) and smooth pursuit eye displacement (SED) during blanking. SAD is defined as the sum of the amplitudes of the saccades triggered during blanking. The difference between the total eye displacement and SAD is called SED. Both SAD and SED were normalized with target displacement. The relationship between SAD and SED was quantified using the slope of the regression line as well as the root mean square error (RMSE). For this particular analysis, performances of ASD participants were compared to the one of the control group of this paper (Ctrl 1) and two groups from a similar previous study [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]: Ctrl 2 is a group of age-matched control children (the 40 participants with the closest age) and Young is a group of 10 younger control children (aged 5 to 7 years).

For all analyses, no directional biases were observed in the data and thus data from both directions were collapsed. Repeated measures ANOVA's were performed with groups as between participants factor and periods as within-participants factor. All analyses were performed using Matlab. Regressions were computed using the robustfit function.

Lastly, we performed a group-size analysis to estimate the statistical power of our study. Deficits in motor coordination for high-functioning autistic children compared to agematched control were shown to have an effect size of 1.2 (Cohen's d) (see [START_REF] Fournier | Motor coordination in autism spectrum disorders: a synthesis and meta-analysis[END_REF], for a meta-analysis). It is however almost impossible to infer the expected size effect using different sensorimotor tasks, as for instance the variability sources can be totally different. Instead, we reasoned that impairment in internal model function would correspond to a developmental delay of internal model formation of several years. For instance, in cognitive studies, this developmental delay can be up to 7 years (e.g. [START_REF] Baron-Cohen | The autistic child's theory of mind: a case of specific developmental delay[END_REF].

In our previous study [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF], for a difference of three years (5-7 vs 10-12 years old), we found an effect size of 1.4 (Cohen's d). Therefore, if we expect delayed internal model formation such as suggested by others, we can reasonably expect an effect size of 1 between ASD and TD children. To detect such effect size with a 90% power (which is higher than the usual 80% power required), one needs to have at least 21 participants per group.

Our groups are larger than that, allowing for a robust statistical analysis.

Results

The ability to track moving targets that transiently disappear is at the core of this study. Here, we are interested in comparing the tracking ability of participants with ASD and agedmatched controls. Typical oculomotor responses from one control and one ASD participant are displayed in Figure 2. These two trials have similar characteristics. Both participants tracked the moving target accurately when it was visible. Indeed, the eye velocity is close to target velocity for both participants during the first part of the trial (between the two grey areas Figure 2C and2D). Then, when the target was blanked (beginning of the second grey area), the eye velocity of both participants rapidly dropped by about 50%. Eye velocity started to increase again around target reappearance time (Figure 2C and2D). In addition, both participants use a combination of smooth pursuit and saccades to pursue the invisible target (Figure 2A andB). Below, the pursuit, saccadic behavior and the interaction between both types of eye movements in participants with ASD will be compared to the one of agematched control participants. The mean desaccaded eye velocities for both groups for the first and last trials of the blocks are illustrated in Figure 3. The pursuit of participants with ASD (in orange) is very similar to the one observed in controls (in blue). Overall, both ASD and controls are able to follow the target at the correct velocity when visible (just before the second grey bar in Figure 3A andB). This translates into a visually-guided pursuit gain close to one for both groups (Figure 4A, Controls: 0.952±0.02; ASD: 0.95±0.017 (mean±SE); main effect of group:

F(1,73)=0.0005, p=0.98). The gain of visually-guided pursuit was not correlated with the performance IQ (N=75, R=0.07, p=0.57). When the target disappears, the eye velocity begins to decrease until it reaches a plateau (called residual velocity) at about 30% of target velocity, similar in the two groups (Figure 3A andB). The residual gain was about 0.35 and did not differ between groups (Figure 4B, F(1,73)=1.9, p=0.17) and was not correlated with the performance IQ (N=75, R=0.1, p=0.4). After a few trials, participants learned the timing of target reappearance and started anticipating it. Participants stopped to decelerate their eyes earlier in order to catch the target faster after target reappearance (see difference between Figure 3A,B). The tendency to increase the eye velocity in anticipation of target reappearance is reflected by the predictive recovery (eye acceleration at the end of the blanking period) that becomes positive after the first period (T1) (see Figure 4C). Such predictive recovery did not differ between groups (Figure 4C, F(1,73)=0.39, p=0.53) but was weakly correlated with the performance IQ (R=0.24, p=0.035). Therefore, we added the IQ as a co-variate of the predictive recovery and still failed to find a significant differences across the groups (ANCOVA: main effect of group when controlling for IQ: F(1,71)=.054, p=0.82). Overall, our results show that predictive smooth pursuit is not altered in autism. This is further demonstrated by the similar anticipatory pursuit initiation observed during the initial gap that is, before target onset. Because of anticipatory tracking, eye velocity already reached nearly 20% of target velocity at target motion onset. There was no statistical differences between the two groups (F(1,73)=0.23 , p=0.63) nor between the successive blocks (F(3,219)=1.37 , p=0.25).

Predictive saccades are preserved in autism

The two groups of participants use a combination of smooth pursuit and saccades to pursue the blanked target, as illustrated in Figure 2A,B. While predictive smooth pursuit is similar between groups, the saccades might exhibit different characteristics. To test whether there were differences in saccadic behavior during target blanking, we computed the number of saccades occurring during this period. This is represented by the heat maps of saccades endpoints, which reflect the time and position of saccadic ends. First, the number of saccades did not differ between groups (Figure 5A,B, Mann-Whitney test: U = 700, p = 0.2).

Second, heat maps were qualitatively similar (Figure 5E andF). This was quantified using the position error with respect to the position of the blanked target at the end of the predictive saccades (triggered during blanking) which is similar between both groups (Figure 5C, F(1,72)=0.047, p=0.83) and was not correlated with the IQ (N=75, R=0.15, p=0.2). The variability of this position error was also similar between groups (Figure 5D, F(1,72)=2.98, p=0.09) and was correlated with the participants' IQ (N=75, R=-0.31, p=0.007). This result was confirmed when the difference in IQ between the two groups was taken into account (main effect of group when taking QI as covariate: F(1,71)=0.73, p=0.39). The similarity of both heat maps and position errors at the end of predictive saccades showed that the two groups used a similar strategy to track the invisible target. Thus, both ASD and control participants used predictive saccades to orient the eyes ahead of the target. E: Heat map of the position of predictive saccades endpoints versus time during the blanking of the target for control subjects. F: Heat map of predictive saccades endpoints for subjects with autism. On panels A, B, E and F, the time zero corresponds to the target disappearance and 0.8 s is the time of target reappearance. On panels E and F, the white line is the virtual (not visible) target position. The red colors represent the time and space where the saccades land with the higher frequency. The dark blue color is used for the combinations location/timing where no saccade lands.

Saccade-pursuit interaction during blanking shows that internal models are preserved in

Autism

The decrease in eye velocity observed at target disappearance largely varied between trials. In order to ensure that position error is minimum at target reappearance, the amplitude of the saccades needs to be adjusted for each trial separately, in function of the decay in eye velocity. That is, the amplitude of predictive saccades should be larger when the residual eye velocity is lower (Figure 6A) and smaller when residual eye velocity is higher (Figure 6B). In Figure 6C,D, for each participant, the normalized saccadic eye displacement is plotted as a function of the normalized pursuit eye displacement for each trial. Ideally, when the predictive pursuit movement is really small (e.g., SED = 0.2 Figure 6A andC), the saccade displacement should be high (e.g., SAD = 0.7 Figure 6A andC). The black line connecting (0,1) to (1,0) with a slope of -1 indicates the optimal relationship between saccadic and pursuit displacements. Along this line, and for each trial, the sum of smooth pursuit and saccadic eye displacement exactly corresponds to the target displacement, leading to a zero position error at target reappearance. The observed relationship between SAD and SED is presented for one illustrative participant with ASD (Figure 6C) and one control participant (Figure 6D).

Given that there is no visual information available, this compensation can only be based upon an internal estimate of the eye (and target) displacement during the blanking period. The strength of the relationship (slope and RMSE) between pursuit and saccadic eye movement, as computed participant-by-participant gives a measure of the quality of these internal models. This slope slightly differs between the ASD and the control group (ASD vs Ctrl 1: Mann-Whitney: U = 700, p = 0.038). However, this difference was very small compared to the change in slope with age observed in a previous study, using the same methods and protocols [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. These previous data have been replotted in Figure 6E and F, for comparison with the current results for both the slope and RMSE of the relationship between saccade and pursuit. Our objective was to compare the difference in these relationships between autistic children and age-matched controls (current study) to the change in parameters due to development [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. Indeed, the difference between ASD and Ctrl 1 is much smaller than the difference observed between aged-matched controls (Ctrl 2) and the younger children (Young) (Figure 6E, permutation test: p< 0.0014).

Indeed, the slopes observed in ASD children were not statistically different from the slopes observed in the two age-matched control groups (Tukey-Kramer test, ASD vs. Ctrl 1: p=0.21; ASD vs. Ctrl 2: p=0.94) but were larger than the slopes observed in younger children (p=0.003). Similarly, the RMSE (the other parameter for the quality of the saccade-pursuit interaction) of ASD children exhibited the same pattern (Figure 6F). Namely, the difference in RMSE between ASD and Ctrl 1 was significantly smaller (permutation test, p=0.003) than the change of RMSE observed with development (Ctrl 2 vs. Young). The value of RMSE in the ASD group was similar to the one of the two age-matched control groups (Tukey-Kramer test, ASD vs. Ctrl1: p = 0.11; ASD vs. Ctrl2: p = 0.9) but differed from the younger children (ASD vs. Young: p = 0.011).

. Young: p = 0.001).

Figure 6: Saccade-pursuit interaction. A: Position of the eye vs. time for a single trial from an ASD adolescent. On this trial, smooth eye velocity during blanking is very low and a large saccade is executed. B: Position of the eye for another trial from the same participant. In this trial, the eye is relatively close to the target (dashed black trace) and a smaller saccade is executed. On these two last panels, the grey rectangles represent the time periods of target blanking. C and D: Two typical examples of the trial-by-trial relationship between the saccadic eye displacement (SAD) and the smooth eye displacement (SED) for an ASD participant and a control, respectively. On these two panels, each dot represents one trial. The color lines represent the linear fits. The black lines represent the optimal compensation with a slope equal to -1. In panel C, the two trials corresponding to panels A and B are linked with them thanks to a thin black line and arrow. E: Slope of the regression line characterizing the saccade-pursuit interaction for the ASD group and the control group (Ctrls1) of the present study. For comparisons purpose, data from typically developing children of the same age (Ctrl2) and of a younger age (Young, 5-7 years old) are represented. These data are taken from [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. F: Quality of the linear fits between SAD and SED represented by the root mean square error (RMSE). On the two last panels, open disks are the means per subject. Squares are the means per group computed with the means per participant. Error bars represent 95% confidence intervals of the mean.

While the slope parameter used to quantify saccade-pursuit interaction was not related to the measured performance IQ of the participants (correlation between slope and IQ: r=-0.04, p=0.7), we found that the RMSE was significantly correlated with IQ (both groups: r=-0.4, p=0.0004; ASD: r=-0.4, p=0.01; Ctrl: r=-0.26, p=0.14). Namely, the RMSE became smaller with increasing IQ. Therefore, we added the IQ as an additional co-variate in the analysis of the RMSE but still did not find a significant difference in RMSE between ASD and control children (ANCOVA: main effect of group: F(1,71)=0.044, p=.83; interaction between IQ and group: F(1,71)=0.009, p=0.92). In other words, the small difference observed between ASD and controls for the RMSE parameters was likely due to the small difference in IQ between the two groups.

Position error at reappearance

As we previously reported, the position error at reappearance provides a global performance index on the predictive tracking during blanking [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. The similarity between control and ASD participants in the position error (Figure 7,F(1,73) = 0.01, p=0.92) further supports our conclusion that there is no deficit in prediction for the ASD children. This parameter was not correlated with the IQ of the participants (N=75, R=0.09, p=0.42).

Discussion

Using a normative approach of both sensory-driven and predictive pursuit, we show that ocular tracking performance is indistinguishable between two large groups of ASD and matched typically developing adolescents. More specifically, our results demonstrate that predictive mechanisms driving smooth pursuit before target motion initiation as well as during target blanking are intact in children and adolescents with ASD. Moreover, all oculomotor signatures of internal models about target and eye motions are normal as well. It could be argued that prediction is not that important for the pursuit during target blanking or that the task is not difficult enough. However, prediction is the only driving mechanism of the smooth pursuit response during the 800ms of the occlusion given that there is no visual feedback and that eye movements cannot be driven by proprioception. In addition, the predictive behavior observed during the blanking periods has been shown to be really sensitive to brain maturation [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. Therefore, we believe that if ASD is associated with a delay in the maturation of the brain structures supporting predictive oculomotor behavior, our task would be sensitive enough to detect such a deficit. Our findings thus clearly indicate that, at least for low-level sensorimotor skills, predictive mechanisms are not affected in highfunctioning ASD children and adolescents. They argue against a recent and popular hypothesis that views autism as a general deficit of prediction mechanisms (e.g., [START_REF] Gomot | A challenging, unpredictable world for people with autism spectrum disorder[END_REF][START_REF] Pellicano | When the world becomes "too real": a Bayesian explanation of autistic perception[END_REF][START_REF] Sinha | Autism as a disorder of prediction[END_REF]. Results will be discussed in regards to the existing, controversial literature on the sensorimotor and cognitive aspects of oculomotor control in ASD and their neural bases.

Is there a deficit in oculomotor control in Autism?

Whether or not children with ASD exhibit a specific deficit in the control of eye movements is still debated. Overall, smooth pursuit in ASD has not been well documented (see [START_REF] Rommelse | A review on eye movement studies in childhood and adolescent psychiatry[END_REF] for a review). In a large group of adolescent and young adults, [START_REF] Takarae | Pursuit eye movement deficits in autism[END_REF] investigated open-and closed-loop pursuit performance during rectilinear (i.e. identical to the current paradigm) and sinusoidal target motions. They reported a small (~10%) but systematic decrease in pursuit gain of both phases in the older sub-group of the ASD cohort. This reduction was interpreted as an evidence for a maturational disturbance of the frontal lobe, as originaly proposed using different saccadic tasks [START_REF] Goldberg | Deficits in the initiation of eye movements in the absence of a visual target in adolescents with high functioning autism[END_REF]; see below). It shall be noticed however that no deficit in pursuit gain was observed between the younger ASD and control participants (<16 years old). The small difference observed between the two older groups (>16 years old) was due to the fact that pursuit gain did not further improve with age in ASD as it did in control participants.

Consistent with an intact pursuit in young ASD, [START_REF] Kemner | In search of neurophysiological markers of pervasive developmental disorders: smooth pursuit eye movements?[END_REF] reported normal closedloop pursuit gain in a group of adolescents (i.e., mean age ~16 years old) diagnosed with pervasive developmental disorders. Thus, our results are coherent with these previous studies showing normal pursuit in ASD of similar age and IQ distributions. By contrast to the previous studies, our study also revealed that the absolute performance was nearly perfect (i.e. a closed-loop gain ~1) in both groups, suggesting a normal tracking oculomotor control in high-functioning ASD. We moreover observed that predictive saccades naturally occurring during the blanking phase of pursuit were identical between the two groups. This result is consistent with those of [START_REF] Takarae | Pursuit eye movement deficits in autism[END_REF] and of [START_REF] Kemner | In search of neurophysiological markers of pervasive developmental disorders: smooth pursuit eye movements?[END_REF] reporting normal saccadic behavior during rectilinear and sinusoidal tracking. It is also consistent with previous studies showing a nearly normal control of visually-driven saccades in adolescent ASD participants [START_REF] Goldberg | Deficits in the initiation of eye movements in the absence of a visual target in adolescents with high functioning autism[END_REF][START_REF] Landry | Impaired disengagement of attention in young children with autism[END_REF]Bryson, 2004, Rosenhall et al., 1988;van der Geest et al., 2001). Using state-of-the-art eye movement recording techniques, our study strongly supports the view of an intact low-level oculomotor system in the autistic pathology, evidencing the functional integrity of cerebellar-brainstem networks underpinning oculomotor control (Nowinski et al., 2005).

Is there a cognitive deficit in ocular behavior control?

Ocular behaviors have been also extensively used to probe cognitive deficits in ASD (see [START_REF] Benson | Eye movements in autism spectrum disorder[END_REF][START_REF] Falck-Ytter | Eye tracking in early autism research[END_REF][START_REF] Rommelse | A review on eye movement studies in childhood and adolescent psychiatry[END_REF] for recent reviews). An influential study by [START_REF] Goldberg | Deficits in the initiation of eye movements in the absence of a visual target in adolescents with high functioning autism[END_REF] used a set of standard saccadic tasks to disentangle sensorimotor and cognitive aspects of oculomotor control in this population. As stated above, the fact that memory-guided saccades and anti-saccades, but not visually-guided, reflexive saccades, were selectively disturbed in a large group of highfunctioning ASD adolescents was taken as evidence for neurodevelopmental abnormalities affecting the prefrontal (e.g., FEFs) and frontal (dlPFC) cortical areas known for their role in the cognitive control of saccadic eye movements. Interestingly, [START_REF] Goldberg | Deficits in the initiation of eye movements in the absence of a visual target in adolescents with high functioning autism[END_REF] also asked their participants to look back and forth between two alternatively illuminated targets and found fewer predictive saccades in the population of ASD adolescents. Whereas such task would allow testing predictive abilities regarding the timing of saccades, it provides little information on the internal models used for such prediction.

Surprisingly, the impact of cognitive mechanisms upon smooth pursuit eye movements has been barely investigated in ASD. [START_REF] Aitkin | Anticipatory smooth eye movements in autism spectrum disorder[END_REF] reported normal pursuit behavior during a complex task requiring both prediction and cognitive processing about the perceptual organization of the visual scene. The presence of a cue (barrier) on the visual tracking path elicited similar anticipatory eye movements in participants with ASD and in typically developing participants. The T-maze task used by [START_REF] Aitkin | Anticipatory smooth eye movements in autism spectrum disorder[END_REF] did not involve learning or memory mechanisms as the participants had to judge the most probable target trajectory online. Still, their results are consistent with ours that, during transient target occlusion, adolescents with ASD exhibit both a normal decrease in tracking velocity during blanking periods and a normal anticipatory eye acceleration before target reappearance, as compared to typically developing teenagers. Moreover, in our oculomotor task, anticipatory pursuit during target initiation and reappearance involves both learning and memory processing about the timing and the kinematics of the different phases of the target trajectory [START_REF] Bennett | Oculomotor prediction of accelerative target motion during occlusion: long-term and short-term effects[END_REF][START_REF] Bennett | Target acceleration can be extracted and represented within the predictive drive to ocular pursuit[END_REF][START_REF] Bogadhi | Dynamic interaction between retinal and extraretinal signals in motion integration for smooth pursuit[END_REF][START_REF] Madelain | Effects of learning on smooth pursuit during transient disappearance of a visual target[END_REF][START_REF] Montagnini | Predicting 2D target velocity cannot help 2D motion integration for smooth pursuit initiation[END_REF][START_REF] Orban De Xivry | Evidence for synergy between saccades and smooth pursuit during transient target disappearance[END_REF]. This is demonstrated by the small but significant improvement of the tracking performance over the successive blocks (see [START_REF] Ego | Spontaneous improvement in oculomotor function of children with cerebral palsy[END_REF][START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]). Yet, no difference between ASD and controls was reported here, suggesting that they were able to build and update an internal representation of target motion dynamics. Lastly, the coordination between two types of eye movements (saccade and pursuit) was not different between the two groups, further demonstrating the accuracy of such internal model (Orban de [START_REF] Orban De Xivry | A dynamic representation of target motion drives predictive smooth pursuit during target blanking[END_REF][START_REF] Orban De Xivry | Evidence for synergy between saccades and smooth pursuit during transient target disappearance[END_REF]. Overall, using both pursuit and saccade markers, we establish that predictive eye movements are preserved in ASD even when it is necessary to estimate both the timing and the velocity of the sensory events from the previous trials.

Autism has been associated to scattered morphological and functional brain changes [START_REF] Amaral | Neuroanatomy of autism[END_REF][START_REF] Auzias | Atypical sulcal anatomy in young children with autism spectrum disorder[END_REF][START_REF] Bailey | A clinicopathological study of autism[END_REF][START_REF] Brambilla | Brain anatomy and development in autism: review of structural MRI studies[END_REF]Brun et al., 2015;[START_REF] Courchesne | Mapping early brain development in autism[END_REF][START_REF] Peters | Brain functional networks in syndromic and nonsyndromic autism: a graph theoretical study of EEG connectivity[END_REF]. The correlation of these cerebral changes with specific alterations of sensory, motor or cognitive abilities is, however strongly disputed such that, a well-accepted view of functional brain changes in ASD is far from being reached. Behavioral performances related to well-documented neuronal mechanisms can be a powerful guide when searching for specific brain developmental abnormalities. In human and non-human primates, pursuit maintenance during target occlusion sustains neuronal activity in a network of cortical (e.g. MST, FEF/SEF, dlPFC areas) and cerebellar (e.g. Floculus) areas [START_REF] Lisberger | Internal models of eye movement in the floccular complex of the monkey cerebellum[END_REF][START_REF] Ono | The neuronal basis of on-line visual control in smooth pursuit eye movements[END_REF][START_REF] Orban De Xivry | Saccades and pursuit: two outcomes of a single sensorimotor process[END_REF]. Moreover, anticipatory tracking responses have been associated with predictive neuronal activities in the prefrontal oculomotor fields (FEF/SEF) [START_REF] Barborica | Estimating invisible target speed from neuronal activity in monkey frontal eye field[END_REF][START_REF] De Hemptinne | Neuronal bases of directional expectation and anticipatory pursuit[END_REF][START_REF] Ding | Dissociable frontal controls during visible and memoryguided eye-tracking of moving targets[END_REF][START_REF] Ferrera | Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task[END_REF][START_REF] Lencer | Cortical mechanisms of smooth pursuit eye movements with target blanking. An fMRI study[END_REF][START_REF] Missal | Supplementary eye fields stimulation facilitates anticipatory pursuit[END_REF]. The nearly perfect anticipatory tracking responses that we reported in ASD (see also [START_REF] Aitkin | Anticipatory smooth eye movements in autism spectrum disorder[END_REF] suggest that such frontal and cerebellar networks are functionally intact in high-functioning ASD adolescents. In the same vein, internal models driving both anticipatory pursuit and saccades during occlusion have been associated to computations performed in the parietal cortex and the cerebellum [START_REF] Lisberger | Internal models of eye movement in the floccular complex of the monkey cerebellum[END_REF][START_REF] Orban De Xivry | Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics[END_REF]. The absence of behavioral deficits in the present study argues for a functional integrity of the fronto-cerebellar networks in ASD [START_REF] Hashimoto | Singlephoton emission computed tomography of the brain in autism: Effect of the developmental level[END_REF][START_REF] Luna | Neocortical system abnormalities in autism: an fMRI study of spatial working memory[END_REF].

Conclusion: Is there a general deficit of predictive mechanisms in Autism?

Autistic spectrum disorders remain largely a mystery and has therefore generated a number of theories trying to explain their cognitive disabilities within a single, generic computational framework. Just to mention a few recent propositions, autism was considered as an excessive neural variability [START_REF] Dinstein | Neural variability: friend or foe?[END_REF], an over-specificity during learning [START_REF] Harris | Perceptual learning in autism: over-specificity and possible remedies[END_REF] or a change in the balance between excitation and inhibition [START_REF] Rosenberg | A computational perspective on autism[END_REF]. These theories aim at proposing that ASD may broadly alter neural computation rather than narrowly impacting individual systems. Recently, [START_REF] Sinha | Autism as a disorder of prediction[END_REF] proposed that a generic deficit in predictive mechanisms is a cardinal feature of ASD. Such deficient predictive abilities might be related to an abnormal ability to build internal models build from the past history of sensory evidence or action [START_REF] Pellicano | When the world becomes "too real": a Bayesian explanation of autistic perception[END_REF]. On the contrary, we and others [START_REF] Aitkin | Anticipatory smooth eye movements in autism spectrum disorder[END_REF] found that predictive mechanisms and internal models involved in the control of tracking behavior are preserved in high-functioning, adolescent patients with ASD. A vast repertoire of sensorimotor and cognitive skills relies on predictive mechanisms. We show here that some of them may be intact. It is thus critical that future studies attempt to decipher which prediction-based behaviors are specifically impaired or spared in ASD. Saccadic and pursuit eye movements are powerful behavioral probes to delineate low and high cognitive computations that are specifically affected in ASD.

FIGURE CAPTIONS

Figure 1: Time course of a test trial. After 1s of fixation and a gap period (target blanked for 0.3 s), the target started to move horizontally at a constant velocity of 20deg/s. 0.6 s later the target was blanked for 0.8 s (blanking period) and then reappeared and continued moving for another 0.6 s. E: Heat map of the position of predictive saccades endpoints versus time during the blanking of the target for control subjects. F: Heat map of predictive saccades endpoints for subjects with autism. On panels A, B, E and F, the time zero corresponds to the target disappearance and 0.8 s is the time of target reappearance. On panels E and F, the white line is the virtual (not visible) target position. The red colors represent the time and space where the saccades land with the higher frequency. The dark blue color is used for the combinations location/timing where no saccade lands.

Figure 6: Saccade-pursuit interaction. A: Position of the eye vs. time for a single trial from an ASD adolescent. On this trial, smooth eye velocity during blanking is very low and a large saccade is executed. B: Position of the eye for another trial from the same participant. In this trial, the eye is relatively close to the target (dashed black trace) and a smaller saccade is executed. On these two last panels, the grey rectangles represent the time periods of target blanking. C and D: Two typical examples of the trial-by-trial relationship between the saccadic eye displacement (SAD) and the smooth eye displacement (SED) for an ASD participant and a control, respectively. On these two panels, each dot represents one trial. The color lines represent the linear fits. The black lines represent the optimal compensation with a slope equal to -1. In panel C, the two trials corresponding to panels A and B are linked with them thanks to a thin black line and arrow. E: Slope of the regression line characterizing the saccade-pursuit interaction for the ASD group and the control group (Ctrls1) of the present study. For comparisons purpose, data from typically developing children of the same age (Ctrl2) and of a younger age (Young,[START_REF] Brambilla | Brain anatomy and development in autism: review of structural MRI studies[END_REF](6)[START_REF] Orban De Xivry | A dynamic representation of target motion drives predictive smooth pursuit during target blanking[END_REF] are represented. These data are taken from [START_REF] Ego | Development of internal models and predictive abilities for visual tracking during childhood[END_REF]. F: Quality of the linear fits between SAD and SED represented by the root mean square error (RMSE). On the two last panels, open disks are

Figure 1 :

 1 Figure1: Time course of a test trial. After 1s of fixation and a gap period (target blanked for 0.3 s), the target started to move horizontally at a constant velocity of 20deg/s. 0.6 s later the target was blanked for 0.8 s (blanking period) and then reappeared and continued moving for another 0.6 s.

Figure 2 :

 2 Figure 2: Typical trials. Position vs. time of the eye for a typical trial from a control participant (A) and a participant with Autism (B). C and D: The corresponding desaccaded eye velocities. Dashed lines represent respectively the target position and velocity. Grey areas represent the time periods when the target is not visible.

Figure 3 :

 3 Figure 3: Mean desaccaded eye velocity for both ASD (orange) and control (blue) groups during the first (A) and last trials (B) of each block. The black dashed traces indicate target velocity profile. Grey bars represent the initial (gap) and late blanking periods (i.e. target invisible).

Figure 4 :

 4 Figure 4: Predictive smooth pursuit. A: Evolution of the visually guided pursuit gain through trials of a block for the ASD (in orange) and control group (in blue). B: Evolution of the residual gain through the block. C: Evolution of the predictive recovery through the block. On each panel, data points are the average per group computed on the mean per participant and error bars are standard errors of the means.

Figure 5 :

 5 Figure 5: Predictive saccades. A: Histogram of the number of saccades executed during the blanking period by both groups (ASD in orange and controls in blue). B: Number of predictive saccades per trial per group. Data points are means per subject. Error bars are standard errors of the mean C: Evolution through trials of a block of the position error at the end of predictive saccades for both groups. D: Evolution through trials of a block of the standard deviation of the position error at the end of predictive saccades for both groups. On panels C and D, data points are the means per subject and error bars are standard errors of the mean. E: Heat map of the position of predictive saccades endpoints versus time during the blanking of the target for control subjects. F: Heat map of predictive saccades endpoints for subjects with autism. On panels A, B, E and F, the time zero corresponds to the target disappearance

  Figure 6

Figure 7 :

 7 Figure 7: Evolution of the position error at reappearance through trials of a block for the ASD group (in orange) and the control group (in blue). Data points are the mean per group computed with the mean per subject. Error bars represent standard errors of the mean.

Figure 2 :

 2 Figure 2: Typical trials. Position vs. time of the eye for a typical trial from a control participant (A) and a participant with Autism (B). C and D: The corresponding desaccaded eye velocities. Dashed lines represent respectively the target position and velocity. Grey areas represent the time periods when the target is not visible.

Figure 3 :

 3 Figure 3: Mean desaccaded eye velocity for both ASD (orange) and control (blue) groups during the first (A) and last trials (B) of each block. The black dashed traces indicate target velocity profile. Grey bars represent the initial (gap) and late blanking periods (i.e. target invisible).

Figure 4 :

 4 Figure 4: Predictive smooth pursuit. A: Evolution of the visually guided pursuit gain through trials of a block for the ASD (in orange) and control group (in blue). B: Evolution of the residual gain through the block. C: Evolution of the predictive recovery through the block. On each panel, data points are the average per group computed on the mean per participant and error bars are standard errors of the means.

Figure 5 :

 5 Figure 5: Predictive saccades. A: Histogram of the number of saccades executed during the blanking period by both groups (ASD in orange and controls in blue). B: Number of predictive saccades per trial per group. Data points are means per subject. Error bars are standard errors of the mean C: Evolution through trials of a block of the position error at the end of predictive saccades for both groups. D: Evolution through trials of a block of the standard deviation of the position error at the end of predictive saccades for both groups. On panels C and D, data points are the means per subject and error bars are standard errors of the mean.E: Heat map of the position of predictive saccades endpoints versus time during the blanking of the target for control subjects. F: Heat map of predictive saccades endpoints for subjects with autism. On panels A, B, E and F, the time zero corresponds to the target disappearance and 0.8 s is the time of target reappearance. On panels E and F, the white line is the virtual (not visible) target position. The red colors represent the time and space where the saccades land with the higher frequency. The dark blue color is used for the combinations location/timing where no saccade lands.

Table 1 :

 1

	Participants description. For the two groups, we indicate the range and mean (+/-
	SD) age, the total number and the number of males and the performance scores for IQ. For
	the ASD group, we report the scores (mean +/-SD) obtained with the ADOS scale on two
	items (Communication, Social Interaction) and the EQ scale.
	All procedures were approved by the local Ethics Committee and were conducted in
	accordance with the Declaration of Helsinki. All participants, or their representatives signed
	an informed consent. All experiments took place in the local Clinical Unit.

Group Age (years) N N males Performance IQ ADOS Communication ADOS Social interaction EQ ASD

		10 to 29y	40	36	98 +/-36	3.3 +/-3.8	7 +/-2.6	27.5+/-10.9
		(16.4 +/-4.2y)
	Ctrl	10 to 28y	35	21
		(15.7 +/-4.1y)

GSM and CD were supported by the Centre National de la Recherche Scientifique and by the French Agence Nationale de la Recherche (Project SPEED, ANR-13-SHS2-0006). Support for JJOX was provided by the Brains Back to Brussels program from the Brussels region (Belgium). Support for CE was provided by Fondation JED Belgique. This work was also supported by the Belgian Program on Inter-university, Attraction Poles, initiated by the Belgian Federal Science Policy Office, Actions de Recherche Concertées (French community, Belgium), and the European Space Agency (ESA) of the European Union. We thank Andrew Meso, Fréderic Barthélémy and Isabelle Charvin for their help in conducting the experiments.

the means per subject. Squares are the means per group computed with the means per participant. Error bars represent 95% confidence intervals of the mean.


Bruno Bézard
email: bruno.bezard@obspm.fr

Christopher T Russell
email: ctrussel@igpp.ucla.edu

Takehiko Satoh
email: satoh@stp.isas.jaxa.jp

Suzanne E Smrekar
email: suzanne.e.smrekar@jpl.nasa.gov

Colin F Wilson
email: colin.wilson@physics.ox.ac.uk

Topical Collection on Venus

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

The incredible diversity of scientific questions that can be investigated at Venus, Earth's twin planet, is manifested by the rich history of exploration and breadth of surface, interior, and atmospheric studies. This Venus III collection of papers discusses the current understanding of Venus following the end of the Venus Express mission in December 2014. The pioneering book Venus (Hunten, Colin, Donahue, Moroz Eds.), published in 1983, reported on the amazing set of data collected by a series of Mariner, Venera and Pioneer Venus missions between 1962 and 1978, including several surface landers. The Venus II book (Bougher, Hunten and Phillips Eds.) that came out in 1997 was motivated by the need to reassess the state of our knowledge following the end of the Pioneer Venus Orbiter in 1992, more USSR missions (Venera and Vega), the Galileo flyby in 1990, the Magellan mission (1990)(1991)(1992)(1993)(1994), and various ground-based observational campaigns. Since then, the Venus Express spacecraft, which was launched in November 2005 and ceased activity in December 2014, has brought a wealth of unique data on Venus' atmosphere, surface and space environment. With this major landmark in the history of Venus's exploration, the time was ripe for synthesizing the resulting scientific studies together with other advances in a Venus III book, this Topical Collection of Space Science Reviews. This collection comes too early to incorporate all the findings from the Akatsuki orbiter, which arrived in December 2015 and is conducting its observations of Venus as this set of papers goes to press.

The ten articles in this collection are the direct products of the efforts of 41 authors and coauthors. They review our knowledge of Venus's interior structure, surface composition, and atmosphere in terms of thermal structure, dynamics, composition, chemistry, clouds, aeronomy and interaction with the solar wind. They also identify remaining questions and needed measurements, which provide fuel for thinking about the future of Venus research and exploration.

As an introduction to the following articles, the paper by Taylor et al. presents a brief history of Venus exploration and an overview for the general reader of current knowledge on the surface, interior, atmosphere, climate and magnetosphere of Earth's near twin planet. The paper by Smrekar et al. reviews current data that constrain interior structure and dynamics, and provides new insights into key questions, such as why plate tectonics is present on Earth and absent on Venus. Gilmore et al. review the constraints on Venus's surface composition derived from Venus Express near-infrared measurements along with the recent theoretical and experimental works that improve our understanding of atmosphere-surface interactions. The paper by Limaye et al. is devoted to the thermal structure and radiative balance of Venus's atmosphere. Recent results on the vertical thermal structure from different remote sensing techniques are presented with emphasis on knowledge gaps and open issues. The atmospheric dynamics of Venus from the surface up to ~100 km is reviewed by Sanchez-Lavega et al. The paper includes descriptions of the wind field, dominated by the so-called super-rotation, of the prominent polar vortices, and of the various types of waves that have been detected. Numerical models that have been developed to investigate Venus's global circulation are also reviewed. Marcq et al. discuss the composition and chemistry of the neutral atmosphere, focusing on the horizontal, vertical and temporal variability of the minor species observed below ~90 km. Particular attention is paid to the vertical profiles of CO and OCS below the clouds and of CO, SO and SO2 above the clouds. The paper by Titov et al. explores the complexity of the Venus cloud system. After a presentation of the vertical structure of the mesospheric hazes and main cloud deck, the paper reviews the available information on the cloud morphology and on the microphysical properties of the particle population, discusses the cloud composition, chemistry and microphysics, describes the radiative effects of the cloud layers and evaluates the occurrence of lightning. The paper by Gérard et al. reviews the composition, thermal structure, dynamics and transport of the Venus upper atmosphere above ~90 km. It describes in particular how observations of the dayglow and nightglow emissions from different molecules provide invaluable tools to sound this region and improve three-dimensional models of its dynamics. The paper by Futaana et al. examines the near-Venus space environment, the solar wind interaction with Venus and its impacts down to the ionopause. Emphasis is put on results from the Venus Express mission, which observed with unprecedented detail the structure of the plasma environment, particularly the north polar region, revealed the neutral exosphere, and provided a measurement of the water loss from planetary ion escape. The collection concludes with the paper by Glaze et al., which opens a perspective towards the future of Venus research and exploration. Many key questions remain about Venus's evolution and current geologic and atmospheric processes. Their elucidation will require laboratory and modeling studies as well as new measurements from Earth and spacecraft, including new missions capable of making in situ measurements in the atmosphere and at the surface.

The success of this collection has been due to many people. First of all, the editors wish to thank the authors who had the difficult job of distilling the voluminous reports that such missions produce, to a coherent set of highly readable articles. The editorial process also benefited from an excellent group of referees who acted as a test readership and helped guide the authors to produce even more complete and useful manuscripts. These referees included Sushil K. Atreya, Bruno Bézard, Mark Bullock, Thérèse Encrenaz, Justin Filiberto, Misha Ivanov, Esa Kallio, Vladimir Krasnopolsky, Emmanuel Lellouch, Helen Parish, Javier Peralta, Peter Read, Takehiko Satoh, Gerald Schubert, Tom G. Slanger, Suzanne Smrekar, Paul Tackley, Adriana C. Ocampo Uria, O.L. Vaisberg.

Equally important has been the strong support this project has received from Springer Nature and the extra effort expended by Ganesh Jagadeesan, Vygintas Vilimas, and Hannah Kaufman. At UCLA we were skillfully assisted by Marjorie Sowmendran who acted as the interface between the authors, the referees, and the publishers.

Space Sci Rev (2018) 214:128 https://doi.org/10.1007/s11214-018-0564-8


Julien Camperi

Valerie Pichon

Thierry Fournier

Nathalie Delaunay
email: nathalie.delaunay@espci.fr

First profiling in hydrophilic interaction liquid chromatography of intact human chorionic gonadotropin isoforms

Keywords: Glycosylation, HILIC chromatography, Human Chorionic Gonadotropin, Intact protein, Isoforms

The study of glycoproteins is a rapidly growing field, which is not surprising considering that approximately 70% of human proteins are glycosylated and that numerous biological functions are associated to the glycosylation. In this work, our interest focused on the heterodimeric human Chorionic Gonadotropin (hCG) glycoprotein that is the specific hormone of the human pregnancy, consisting of an α and a β subunit, so-called hCGα and hCGβ, respectively. This protein possesses a very high structural heterogeneity, essentially due to the presence of 8 glycosylation sites, but also other types of post-translational modifications. In this study, for the first time, the potential of hydrophilic interaction liquid chromatography (HILIC) was investigated to separate the intact hCG isoforms. Three different HILIC stationary phases were tested using an hCG-based drug as standard, a recombinant hCG. For each stationary phase, the effect of the initial mobile phase composition based on ACN/H 2 O mixture, the slope of the gradient, the content and nature of the acidic additive (formic acid and trifluoroacetic acid (TFA)), and the addition of a volatile salt (ammonium formate) on the retention and the resolution were studied. The best HILIC separation was obtained with the amide column and a mobile phase composed of water/ACN containing 0.1% of TFA. The repeatability in terms of retention times and peak areas was then assessed. Finally, the method was applied to the analysis of a second hCGbased drug obtained from urine of pregnant women. Both drugs gave chromatograms with more than 10 peaks. However, they were significantly different, which demonstrated the potential of HILIC method for hCG isoform fingerprinting.

Introduction

Human Chorionic Gonadotropin (hCG) constitutes the first hormonal message from the placenta to the mother, detectable in maternal blood as early as one week after fecundation, and used in pregnancy diagnosis [START_REF] Fournier | Review: hCGs: different sources of production, different glycoforms and functions[END_REF]. This protein is essential for the development of both placenta and fetus [START_REF] Guibourdenche | Physiopathologie de l'hormone chorionique gonadotrope humaine (hCG) dans la trisomie 21 foetale, Immuno-Anal[END_REF]. It is composed of two non-covalently linked subunits, hCGα and hCGβ. The α-subunit has a molecular weight (MW) of about [START_REF] Fekete | Chromatographic, Electrophoretic, and Mass Spectrometric Methods for the Analytical Characterization of Protein Biopharmaceuticals[END_REF]198 Da regardless of glycans and 2 N-glycosylation sites and is common to the pituitary gonadotropin hormones such as the follicle-stimulating hormone, the thyroid-stimulating hormone, and the luteinizing hormone [START_REF] Kessler | Structure and location of the O-glycosidic carbohydrate units of human chorionic gonadotropin[END_REF][START_REF] Liu | Silencing of the gene for the alpha-subunit of human chorionic gonadotropin by the embryonic transcription factor Oct-3/4[END_REF]. The β-subunit represents the specific part of hCG and has a MW of 15,521 Da regardless of glycans and 2 N-and 4 O-glycosylation sites [START_REF] Liu | Mass spectrometric characterization of the beta-subunit of human chorionic gonadotropin[END_REF]. Therefore, with 8 glycosylation sites, hCG is a highly heterogeneous protein.

Its characterization was already performed by analysing the glycopeptides obtained after an enzymatic digestion [START_REF] Nerenz | Estimating the hCGβcf in urine during pregnancy[END_REF][START_REF] Lund | Epitope analysis and detection of human chorionic gonadotropin (hCG) variants by monoclonal antibodies and mass spectrometry[END_REF][START_REF] Lund | Immuno-MS based targeted proteomics: highly specific, sensitive, and reproducible human chorionic gonadotropin determination for clinical diagnostics and doping analysis[END_REF] or the released N-glycans after a PNGase-F treatment to obtain the glycan profiles of hCG-based drugs [START_REF] Liu | Mass spectrometric characterization of the beta-subunit of human chorionic gonadotropin[END_REF]. Furthermore, one study also demonstrated the potential of pronase to determine the composition of O-glycans present on the β-subunit [START_REF] Wuhrer | Structural glycomics using hydrophilic interaction chromatography (HILIC) with mass spectrometry[END_REF]. However, all these approaches are time-consuming, because they require numerous pre-treatment steps, and they do not give the exact combinations of multiple posttranslational modifications (PTMs) of a given isoform. This is why today the fast and cheap analysis of intact glycoprotein presents a large interest.

High-performance liquid chromatography (HPLC) in combination with UV or mass spectrometry (MS) detection is now a common approach for the analysis of intact proteins [START_REF] Fekete | Chromatographic, Electrophoretic, and Mass Spectrometric Methods for the Analytical Characterization of Protein Biopharmaceuticals[END_REF][START_REF] Sandra | Modern chromatographic and mass spectrometric techniques for protein biopharmaceutical characterization[END_REF]. Recently, we demonstrated the potential of reversed phase liquid chromatography (RPLC) for the separation of intact hCG isoforms [START_REF] Camperi | An attempt to characterize the human Chorionic Gonadotropin protein by reversed phase liquid chromatography coupled with high-resolution mass spectrometry at the intact level[END_REF]. However, RPLC may lack of selectivity to separate the hCG glycoforms whereas some papers dealing with other proteins showed that it could be performed by hydrophilic interaction liquid chromatography (HILIC) [START_REF] Pedrali | Characterization of Intact Neo-Glycoproteins by Hydrophilic Interaction Liquid Chromatography[END_REF][START_REF] Rinaldi | Application of a rapid HILIC-UV method for synthesis optimization and stability studies of immunogenic neo -glycoconjugates[END_REF][START_REF] Tengattini | Hydrophilic interaction liquid chromatography-mass spectrometry as a new tool for the characterization of intact semi-synthetic glycoproteins[END_REF][START_REF] Periat | Potential of hydrophilic interaction chromatography for the analytical characterization of protein biopharmaceuticals[END_REF][START_REF] Domínguez-Vega | High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry[END_REF].

Indeed, HILIC allowed the monitoring of the glycosylation reaction with different glycans of a model protein (ribonuclease A) and 2 antigenic proteins hyphenated with UV or MS detection [START_REF] Pedrali | Characterization of Intact Neo-Glycoproteins by Hydrophilic Interaction Liquid Chromatography[END_REF][START_REF] Rinaldi | Application of a rapid HILIC-UV method for synthesis optimization and stability studies of immunogenic neo -glycoconjugates[END_REF][START_REF] Tengattini | Hydrophilic interaction liquid chromatography-mass spectrometry as a new tool for the characterization of intact semi-synthetic glycoproteins[END_REF]. Periat et al. also demonstrated the potential of HILIC-UV for the quality control of therapeutic monoclonal antibodies [START_REF] Periat | Potential of hydrophilic interaction chromatography for the analytical characterization of protein biopharmaceuticals[END_REF]. Recently, Domínguez-Vega et al.

demonstrated that HILIC associated to high-resolution MS allowed the profiling of interferon-beta-1a and the identification of 12 glycoforms [START_REF] Domínguez-Vega | High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry[END_REF]. They characterized also recombinant human erythropoietin (4 glycosylation sites) and they identified 51 glycoforms and other PTMs [START_REF] Domínguez-Vega | High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry[END_REF].

Here, for the first time, HILIC was investigated to separate the intact hCG isoforms. The chromatographic performances of three HILIC-columns (Accucore Amide, Mixed-Mode HILIC-1, and GlycanPac AXH-1) were compared. The effect of different parameters on the separation (initial mobile phase composition, slope of the gradient, temperature of analysis, injection volume, nature and content of salt) were studied for each column. Finally, the best column with the optimal conditions was used for the analysis of 2 hCG-based drugs demonstrating the potential of HILIC for hCG isoform fingerprinting.

Materials and methods

Reagents and analytes

HPLC-grade acetonitrile (ACN) and formic acid (FA) were supplied by Carlo Erba (Val de Reuil, France). Ammonium formate (AF) and trifluoroacetic acid (TFA) were purchased from Sigma-Aldrich (Saint Quentin Fallavier, France). The ultra-pure water was obtained with a Direct-Q3

UV system (Millipore, Molsheim, France). Ovitrelle® (Serono Europe Ltd., London, UK) and Pregnyl® (Organon, Oss, The Netherlands), are drugs based on recombinant hCG (r-hCG) and urinary hCG (u-hCG), respectively. Ovitrelle® was available as a solution containing 250 µg of r-hCG, and Pregnyl as a lyophilized powder containing 5000 IU u-hCG (1 IU is equivalent to a concentration of 0.092 µg L -1 [START_REF] Berger | The molecular relationship between antigenic domains and epitopes on hCG[END_REF][START_REF] Berger | Towards user-oriented standardization of pregnancy and tumor diagnosis: assignment of epitopes to the three-dimensional structure of diagnostically and commercially relevant monoclonal antibodies directed against human chorionic gonadotropin and derivatives[END_REF]).

The Ovitrelle® solution was reconstituted in 0.5 mL of water to obtain a stock solution containing 500 µg mL -1 of r-hCG. For Pregnyl®, the lyophilized content of each ampoule was dissolved in 1 ml of water to obtain a concentration of 460 µg mL -1 . All the preparations were aliquoted and stored at -20°C. Before injection, the hCG fractions were washed and pre-concentrated at 1 mg mL -1 by centrifugation with ultra-centrifugal units (Merck, Darmstadt, Germany) having a molecular weight cut-off of 10 kDa.

Instrumentations and columns

The experiments were carried out with a 1260 Infinity instrument (bio-inert system), equipped with a quaternary pump, an autosampler, a column oven, and a UV detector (Agilent Technologies, Les Ulis, France). The three studied HILIC-columns were: Accucore Amide (150 x 2.1 mm, 2.6 µm, 150 Å), Acclaim Mixed-Mode HILIC-1 (150 x 2.1 mm, 3 µm, 120 Å), and GlycanPac AXH-1 (150 x 2.1 mm, 3 µm, 120 Å), all purchased from ThermoFisher (Le Pecq, France). The injection volume was fixed at 1 µL and the flow rate at 0.4 mL min -1 .

The column temperature was fixed at 50°C for the Acclaim Mixed-Mode HILIC-1 column, and at 60°C for the two other columns. FA, TFA, and AF were tested in different proportions in mobile phases composed of acetonitrile and water. Gradient elution conditions were optimized for each HILIC column. The detection was performed at 214 nm.

Results and discussions

Preliminary choices of the mobile and stationary phases

First, we selected different HILIC stationary phases. Only ones with large particle pore sizes (≥ 120 Å) were considered to avoid exclusion of the isoforms from the pores. The most commonly used stationary phase for HILIC analysis of intact proteins in literature is amidebonded silica, which demonstrated a good potential for the protein glycoprofiling [START_REF] Pedrali | Characterization of Intact Neo-Glycoproteins by Hydrophilic Interaction Liquid Chromatography[END_REF][START_REF] Rinaldi | Application of a rapid HILIC-UV method for synthesis optimization and stability studies of immunogenic neo -glycoconjugates[END_REF][START_REF] Tengattini | Hydrophilic interaction liquid chromatography-mass spectrometry as a new tool for the characterization of intact semi-synthetic glycoproteins[END_REF][START_REF] Periat | Potential of hydrophilic interaction chromatography for the analytical characterization of protein biopharmaceuticals[END_REF][START_REF] Domínguez-Vega | High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry[END_REF].

Therefore, an Accucore amide stationary phase with core-shell particles was selected. The mixed-mode GlycanPac AXH-1 column was also selected, since it was developed for the separation of uncharged and charged N-glycans combining weak anion exchange and HILIC mechanisms, although its performance has not been evaluated yet for the separation of intact proteins. Finally, an Acclaim Mixed-Mode HILIC-1 column based on silica bonded with an alkyl chain with a diol end group, used mainly for the separation of surfactants [START_REF] Liu | New polar-embedded stationary phase for surfactant analysis[END_REF], was also selected, although no application to proteins has been reported yet. Therefore, these three stationary phases are expected to provide quite different retention mechanisms for the hCG isoforms.

First experiments were achieved with r-hCG used as standard, as it was expected to be the purest of our two available hCG-based drugs. A mobile phase containing a water/ACN mixture acidified with 0.1% (v/v) TFA was used, since its strong influence on retention in HILIC was reported [START_REF] Fekete | Ultra-high-performance liquid chromatography for the characterization of therapeutic proteins[END_REF]. Indeed, TFA acts as an ion-pairing agent and it generates some solvating effect that both impact selectivity, retention, resolution, and peak shape [START_REF] Zhang | Polyacrylamide brush layer for hydrophilic interaction liquid chromatography of intact glycoproteins[END_REF]. Even if Periat et al. demonstrated that the temperature does not have a significant impact on the HILIC separation as on RPLC [START_REF] Periat | Potential of hydrophilic interaction chromatography for the analytical characterization of protein biopharmaceuticals[END_REF], values between 50 and 65°C were used in HILIC for the separation of intact glycoproteins [START_REF] Pedrali | Characterization of Intact Neo-Glycoproteins by Hydrophilic Interaction Liquid Chromatography[END_REF][START_REF] Rinaldi | Application of a rapid HILIC-UV method for synthesis optimization and stability studies of immunogenic neo -glycoconjugates[END_REF][START_REF] Tengattini | Hydrophilic interaction liquid chromatography-mass spectrometry as a new tool for the characterization of intact semi-synthetic glycoproteins[END_REF][START_REF] Periat | Potential of hydrophilic interaction chromatography for the analytical characterization of protein biopharmaceuticals[END_REF][START_REF] Domínguez-Vega | High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry[END_REF]. Therefore, the column temperature was fixed at 60°C for the Accucore Amide and GlycanPac AXH-1 columns and at 50°C for the Acclaim Mixed-Mode HILIC-1 column, which corresponds to the maximal recommended temperature given by the manufacturer.

Injection conditions

The biggest challenge in HILIC for the analysis of intact proteins is the injection conditions, i.e. to have sample media that do not induce a significant deformation of chromatographic peaks while allowing the solubilization of the protein without causing its precipitation. Indeed, the dissolution in a solvent with a high ACN content (e.g. 70-90%) close to the mobile phase composition cannot be done with proteins, since they precipitate [START_REF] Astefanei | Different Stationary Phase Selectivities and Morphologies for Intact Protein Separations[END_REF].

Therefore, samples were prepared by dissolving hCG in water containing different ACN contents (10, 20, and 30%). and by injecting a reduced volume (1 µl, about 0.2% for the sample-to-column volume ratio) to prevent from the peak deformation induced by the high percentage of water [START_REF] Ruta | A systematic investigation of the effect of sample diluent on peak shape in hydrophilic interaction liquid chromatography[END_REF]. It was observed that the chromatographic performances in terms of efficiencies and retention times did not change (data not shown), confirming the results of Ruta et al. [START_REF] Ruta | A systematic investigation of the effect of sample diluent on peak shape in hydrophilic interaction liquid chromatography[END_REF]. Therefore, hCG was dissolved only in water for the rest of the study, thus ensuring a better stability of hCG samples.

Optimization of the gradient for each column

Different initial ACN contents (between 85 and 65%) and slopes of the mobile phase gradient (from 4.5% down to 0.25% per min) were tested with each HILIC column. Figure 1 presents the resulting chromatograms obtained with the conditions leading to the best separation for each column. Gradient: from 85 to 40% of ACN with a slope of 1.5% min -1 . r-hCG: 1 mg mL -1 in water. Other conditions: see experimental section.

With the Acclaim Mixed-Mode HILIC-1 column, the best separation was achieved by applying an isocratic elution at 90% ACN for 3 min followed by a gradient from 90 to 60% with a slope of 4.5% min -1 (Figure 1A). Only two peaks at 8.8 and 9.6 min were observed in these conditions. With the GlycanPac AXH-1 column (Figure 1B), a mobile phase gradient from 85 to 40% (1.5% per min) gave a better separation than the first column within two distinct elution zones. Indeed, at least 4 peaks were observed between 7.8 and 9.6 min and a second large one between 10.1 and 12.9 min but with an insufficient resolution to distinguish peaks.

The best HILIC separation was obtained with the amide-bonded stationary phase (Figure 1C) as already observed in the literature for other glycoproteins. Using the same mobile phase gradient as for the GlycanPac AXH-1 column, the separation enabled to distinguish three elution zones: 19.8-21.8 min, 21.8-23.4 min, and 23.4-25.2 min. For the first zone, 8 peaks were observed. For the second and the third zones, the presence of several peaks can also be suspected, but the resolution is still insufficient here. Nevertheless, this stationary phase seems to be the most appropriate to separate a very large number of hCG isoforms and demonstrates the high heterogeneity of this protein. Moreover, the number of peaks obtained here by the analysis of intact hCG isoforms in HILIC is significantly superior to the one obtained in RPLC [START_REF] Camperi | An attempt to characterize the human Chorionic Gonadotropin protein by reversed phase liquid chromatography coupled with high-resolution mass spectrometry at the intact level[END_REF]. At this stage, it is possible to assume that each peak may correspond to at least one isoform, including glycoform or other PTMs. However, the UV detection does not allow going further in the characterization of hCG isoforms.

Test of different additives in the mobile phase

The replacement of 0.1% TFA by 0.1% of FA in the mobile phase was considered.

Unfortunately, the separation of isoforms dramatically dropped for the three HILIC-columns. Indeed, as shown in Figure 2A that presents a chromatogram obtained using the amidebonded stationary phase, only one large zone of co-eluting peaks was obtained. For the two other columns, a single large peak was observed (see Figures S1A andS1B in the supplementary data). Then, different percentages of FA, from 0.1% to 1%, were tested but no improvement of the resolution was observed.

Other ionic additives, such as ammonium acetate or ammonium formate, are commonly used in HILIC because they can reduce the proteins adsorption and modify the solvation property, affecting the separation mechanism [START_REF] Buszewski | Hydrophilic interaction liquid chromatography (HILIC)-a powerful separation technique[END_REF]. Therefore, different concentrations of AF (10, 50, and 100 mM) in the mobile phase were tested. The pH was adjusted in the aqueous part at 2.2 with FA. With the Acclaim Mixed-Mode HILIC-1 column, no peak was observed whatever the AF concentration (see Figure S1C). The best separation was obtained with 100 mM AF for the GlycanPac AXH-1 and the Accucore amide columns (Figures S1D and2B, respectively), but in both cases only 4 peaks were observed. Therefore, the chromatographic performances were largely inferior to the ones with 0.1% of TFA in mobile phase, which 10 demonstrates (i) the strong influence of TFA on the retention and resolution of hCG isoforms in HILIC and (ii) that the amide column gave their best separation. Column: Accucore Amide. Gradient: from 85 to 40% of ACN with a slope of 1.5% min -1 . r-hCG: 1 mg mL -1 in water. Other conditions: see experimental section. Finally, as the presence of TFA appears as mandatory in the mobile phase, its percentage was reduced to 0.05%. If we compare the resulting chromatogram (Figure 2C) to the one obtained with 0.1% of TFA (Figure 1C), both are similar but with a decrease in resolution when the TFA content decreases (5 vs 8 peaks were observed for the first elution zone). The last parameter tested to improve again the separation was a decrease in the flow-rate but no significant improvement of resolution values was obtained (between 0.91 and 1.10) whereas the analysis time increased dramatically.

To conclude, the Accucore Amide column with a mobile phase composed of water and ACN containing 0.1% TFA gave the most efficient separation of hCG isoforms. The resolution values of the peaks of the first elution zone (between 19.8 and 21.8 min) were calculated and they are between 0.81 and 1.02 (see Table 1). The repeatability was then assessed. As shown in Table 1, the relative standard deviation (RSD) values for the retention times was inferior to 0.84%, which demonstrate the repeatability of this analytical method. The RSD values for peak areas between 2.5 and 12.3% may demonstrate some instability of the sample.

Table 1:

Resolution and RSD values of the retention times and the areas of the peaks obtained by the HILIC analysis of r-hCG with the optimized conditions (n=3). r-hCG: 1 mg mL -1 in water. For peak number: see Figure 3.

Peak

Analysis of 2 hCG-based drugs

Once the separation parameters were optimized with r-hCG, a second hCG-based drug obtained from urine of pregnant women (u-hCG) was analyzed using the same conditions. As for r-hCG, Table 2 presents the RSD values of the retention times of the main peaks of u-hCG and they were between 0.25 and 1.92%. The RSD values for the peak areas were between 3.6 and 14.3%. Chromatograms of u-hCG (Figure 3A) and r-hCG (Figure 3B) are significantly different. With u-hCG, the separation profile reveals a first elution zone with at least 10 peaks between 18.7 and 21.6 min, partially resolved (0.67 <Rs<0.99, see Table 2), and a second with one large peak between 23.1 and 25.5 min. Unfortunately, the separation conditions do not allow the separation of isoforms present in this second zone. observed on the u-hCG chromatogram. Therefore, these results clearly revealed the presence of hCG isoforms that differ in number and nature in the two hCG-based drugs. This HILIC-UV method can therefore easily distinguish the two drugs, with a relatively short analysis time (26 min) and reasonable cost, and could be used for quality control of hCGbased drugs or fingerprinting studies in real biological samples.

Conclusions

For the first time, the potential of HILIC was evaluated for the separation of the isoforms of the highly glycosylated hCG at the intact level. Despite the different analytical challenges of the HILIC mode for the analysis of intact protein such as injection conditions, it was demonstrated that the amide HILIC-column is able to resolve numerous hCG isoforms.

Despite uncomplete resolution of this method, its potential for fingerprinting approach was demonstrated. If a higher level of information is necessary, such as the number of glycoforms, their composition, their structure, and the presence of other types of PTMs, the hyphenation with high resolution MS is mandatory. This constitutes the next step of this study.

Figure 1 :

 1 Figure 1: Analysis by HILIC of r-hCG with the three tested columns and the most appropriate mobile

Figure 2 :

 2 Figure 2: Effect of different additives in the mobile phase on the HILIC analysis of r-hCG. Mobile

Figure S1 :

 S1 Figure S1: Effect of different additives in the mobile phase on the HILIC analysis of r-hCG. (A, B) 0.1% of FA and (C,D) 100 mM AF + 1% FA. (A, C) Acclaim Mixed-Mode HILIC-1. (B, D) GlycanPac AXH-1 columns. Gradient: from 85 to 40% of ACN with a slope of 1.5% min -1 , Temperature, (A, C) 50°C and (B, D) 60°C. r-hCG s: 1 mg mL -1 in water. Other conditions: see experimental section

Figure 3 :

 3 Figure 3: Superposition of the (A) u-hCG and (B) r-hCG HILIC chromatograms. Accucore Amide

Table 2 :

 2 Resolution and RSD values of the retention times and the areas of the peaks obtained from the HILIC analysis of u-hCG with the optimized conditions (n=3). u-hCG: 1 mg mL -1 in water. For peak number: see Figure3.

	Peak number	1'	2'		3'	4'	5'	6'	7'	8'	9'	10'
	% RSD tr (%)	0.45	0.92		0.43	0.32	0.34	0.55	0.25	0.47	0.88	0.56
	% RSD Area (%)	5.40	12.50	4.23	3.67	4.25	7.52	2.10	4.21	14.2	6.2
	Resolution	-	0.86	0.86	0.99	0.80	0.67	0.80	0.86	0.76	-

Acknowledgments

This work has received the support of "Institut Pierre-Gilles de Gennes" (laboratoire d'excellence, "Investissements d'avenir" program ANR-10-IDEX-0001-02 PSL and ANR-10-LABX-31). The authors also thank Mr. Claude Netter for the Acclaim Mixed-Mode HILIC-1 column.