Mélanie Fouesnard

Johanna Zoppi

Mélanie Petera

Léa Le Gleau

Carole Migné

Fabienne Devime

Stéphanie Durand

Alexandre Benani

Samuel Chaffron

Véronique Douard

Gaëlle Boudry
email: gaelle.boudry@inrae.fr

Dietary switch to Western diet induces hypothalamic adaptation associated with gut microbiota dysbiosis in rats

Background: Early hyperphagia and hypothalamic inflammation encountered after Western diet (WD) are linked to rodent propensity to obesity. Inflammation in several brain structures has been associated with gut dysbiosis. Since gut microbiota is highly sensitive to dietary changes, we hypothesized that immediate gut microbiota adaptation to WD in rats is involved in inflammationrelated hypothalamic modifications.

Methods: We evaluated short-term impact of WD consumption (2h, 1, 2 and 4 days) on hypothalamic metabolome and caecal microbiota composition and metabolome. Data integration analyses were performed to uncover potential relationships among these three datasets. Finally, changes in hypothalamic gene expression in absence of gut microbiota were evaluated in germ-free rats fed WD for 2 days.

Results: WD quickly and profoundly affected the levels of several hypothalamic metabolites, especially oxidative stress markers. In parallel, WD consumption reduced caecal microbiota diversity, modified its composition towards pro-inflammatory profile and changed caecal metabolome. Data integration identified strong correlations between gut microbiota subnetworks, unidentified caecal metabolites and hypothalamic oxidative stress metabolites. Germ-free rats displayed reduced energy intake and no changes in redox homeostasis machinery expression or pro-inflammatory cytokines after 2 days of WD, in contrast to conventional rats, which exhibited increased SOD2, GLRX and IL-6 mRNA levels.

Conclusion:

A potentially pro-inflammatory gut microbiota and an early hypothalamic oxidative stress appear shortly after WD introduction. Tripartite data integration highlighted putative links between gut microbiota sub-networks and hypothalamic oxidative stress. Together with the absence of hypothalamic modifications in germ-free rats, this strongly suggests the involvement of the microbiotahypothalamus axis in rat adaptation to WD introduction and in energy homeostasis regulation.

Introduction

Obesity is the consequence of prolonged disruption of energy homeostasis and hedonic/reward control of appetite [START_REF] Berthoud | Homeostatic and Non-homeostatic Pathways Involved in the Control of Food Intake and Energy Balance[END_REF] , mainly regulated by the coordinated action of many brain regions including the brainstem, the hypothalamus and cortical or subcortical brain areas [START_REF] Berthoud | Homeostatic and Non-homeostatic Pathways Involved in the Control of Food Intake and Energy Balance[END_REF] . Among them, the hypothalamus integrates internal peripheral signals mostly from the gastro-intestinal tract as well as from adipose tissue or pancreas [START_REF] Ciofi | Brain-Endocrine Interactions: A Microvascular Route in the Mediobasal Hypothalamus[END_REF] . It also integrates brainstem information originating from the vagus nerve and circulating peripheral signals to adapt energy intake and/or energy expenditure [START_REF] Dietrich | Hypothalamic control of energy balance: insights into the role of synaptic plasticity[END_REF][START_REF] Nuzzaci | Plasticity of the Melanocortin System: Determinants and Possible Consequences on Food Intake[END_REF][START_REF] Pinto | Rapid Rewiring of Arcuate Nucleus Feeding Circuits by Leptin[END_REF][START_REF] Pierce | De Novo Neurogenesis in Adult Hypothalamus as a Compensatory Mechanism to Regulate Energy Balance[END_REF] .

In rodents, long-term consumption of a Western diet (WD), i.e. a low-fiber, high fat and sugar diet, as opposed to a well-balanced diet (chow), leads to the development of obesity and metabolic disorders.

However, this long-term condition is preceded by an asymptomatic period characterized by metabolic and behavioral adaptations to the acute dietary switch from chow to WD [START_REF] Benani | Food Intake Adaptation to Dietary Fat Involves PSA-Dependent Rewiring of the Arcuate Melanocortin System in Mice[END_REF] . Indeed, although continuous exposure to WD leads to either no effect on energy intake [START_REF] Hamilton | Changes in intestinal barrier function and gut microbiota in high-fat diet-fed rats are dynamic and region dependent[END_REF][START_REF] Guerville | Chronic refined low-fat diet consumption reduces cholecystokinin satiation in rats[END_REF] , hyperphagia in some studies [START_REF] Argueta | Peripheral endocannabinoid signaling controls hyperphagia in western diet-induced obesity[END_REF][START_REF] Argueta | Cannabinoid CB1 Receptors Inhibit Gut-Brain Satiation Signaling in Diet-Induced Obesity[END_REF] or sporadic spikes of hyperphagia in others [START_REF] Brenachot | Lack of Hypothalamus Polysialylation Inducibility Correlates With Maladaptive Eating Behaviors and Predisposition to Obesity[END_REF] , the initial eating behavior upon WD introduction is highly reproducible. Rodents exhibit a 1-day hyperphagic phase, due to the palatability of the diet [START_REF] Valdivia | Acute High Fat Diet Consumption Activates the Mesolimbic Circuit and Requires Orexin Signaling in a Mouse Model[END_REF] , followed by a progressive normalization of energy intake within a week [START_REF] Benani | Food Intake Adaptation to Dietary Fat Involves PSA-Dependent Rewiring of the Arcuate Melanocortin System in Mice[END_REF][START_REF] Butler | Melanocortin-4 receptor is required for acute homeostatic responses to increased dietary fat[END_REF][START_REF] Buckman | Evidence for a novel functional role of astrocytes in the acute homeostatic response to high-fat diet intake in mice[END_REF] . At the hypothalamic level, major changes are observed, including molecular signatures of modified cell interactions, synaptogenesis and increased anorexigenic tone due to pro-opiomelanocortin neurons rewiring and neurogenesis [START_REF] Benani | Food Intake Adaptation to Dietary Fat Involves PSA-Dependent Rewiring of the Arcuate Melanocortin System in Mice[END_REF][START_REF] Gouazé | Cerebral Cell Renewal in Adult Mice Controls the Onset of Obesity[END_REF] .

Moreover, 1 to 3 days of WD consumption induces hypothalamic inflammation, as demonstrated by increased mRNA levels of pro-inflammatory cytokines [START_REF] Thaler | Obesity is associated with hypothalamic injury in rodents and humans[END_REF][START_REF] Mendes | Hypothalamic Microglial Activation in Obesity: A Mini-Review[END_REF] . In mice, this early phase is immediately followed by reactive gliosis, i.e. astroglial and microglial activation [START_REF] Terrien | Reduced central and peripheral inflammatory responses and increased mitochondrial activity contribute to dietinduced obesity resistance in WSB/EiJ mice[END_REF][START_REF] Souza | Short-Term High-Fat Diet Consumption Reduces Hypothalamic Expression of the Nicotinic Acetylcholine Receptor α7 Subunit (α7nAChR) and Affects the Anti-inflammatory Response in a Mouse Model of Sepsis[END_REF][START_REF] Sugiyama | Hypothalamic glial cells isolated by MACS reveal that microglia and astrocytes induce hypothalamic inflammation via different processes under high-fat diet conditions[END_REF] , likely limiting neuronal injury during this initial phase. Despite the resolution of hypothalamic inflammation within the week following WD introduction, continuous WD feeding will ultimately lead to a chronic and sustained inflammation in the hypothalamus [START_REF] Thaler | Obesity is associated with hypothalamic injury in rodents and humans[END_REF][START_REF] Sugiyama | Hypothalamic glial cells isolated by MACS reveal that microglia and astrocytes induce hypothalamic inflammation via different processes under high-fat diet conditions[END_REF] . Interestingly, diet-induced obesity (DIO)-resistant mice display early hypothalamic inflammation and gliosis but with a lesser intensity [START_REF] Terrien | Reduced central and peripheral inflammatory responses and increased mitochondrial activity contribute to dietinduced obesity resistance in WSB/EiJ mice[END_REF] and a different pattern compared to DIO-prone mice [START_REF] Morari | Fractalkine (CX3CL1) Is Involved in the Early Activation of Hypothalamic Inflammation in Experimental Obesity[END_REF] . Thus, greater intensity and/or altered resolution of early WD-induced hypothalamic inflammation could trigger later metabolic alterations. Furthermore, reduced hypothalamic microglia expansion prevents the characteristic increase in energy intake during the first week of WD consumption [START_REF] André | Inhibiting Microglia Expansion Prevents Diet-Induced Hypothalamic and Peripheral Inflammation[END_REF] , suggesting that early hypothalamic inflammation is involved in the hyperphagia observed upon WD introduction. Likewise, the intensity of this early hyperphagic phase seems predictive of rodent obesity propensity on the long term [START_REF] Dourmashkin | Model for predicting and phenotyping at normal weight the long-term propensity for obesity in Sprague-Dawley rats[END_REF][START_REF] Vaanholt | Factors influencing individual variability in high fat diet-induced weight gain in out-bred MF1 mice[END_REF] .

The factors initiating hypothalamic inflammation during short-term WD are ill defined. Several in vitro studies reported a direct action of nutrients such as saturated fatty acid and glucose in initiating proinflammatory cytokines release in various brain cell types [START_REF] Gupta | Saturated long-chain fatty acids activate inflammatory signaling in astrocytes: Fatty acids and glial inflammation[END_REF][START_REF] Hsieh | Acute glucose fluctuation impacts microglial activity, leading to inflammatory activation or self-degradation[END_REF] . A proteomic analysis of mice hypothalami after 3 days on WD reported changes in proteins involved in cellular stress and mitochondrial function, indicative of oxidative stress [START_REF] Mclean | A high-fat diet induces rapid changes in the mouse hypothalamic proteome[END_REF] . Oxidative stress and inflammation are closely related pathophysiological processes, one of which can be easily induced by another. Reactive oxygen species (ROS) generated in brain tissues can modulate synaptic and non-synaptic communication between neurons and result in neuro-inflammation and cell death [START_REF] Popa-Wagner | ROS and Brain Diseases: The Good, the Bad, and the Ugly[END_REF] . Thus, early oxidative stress may play a role in the induction of hypothalamic inflammation. A role for gut microbiota is also suspected. Indeed, several studies suggested a link between gut dysbiosis and inflammation in several areas of the brain [START_REF] Spielman | Unhealthy gut, unhealthy brain: The role of the intestinal microbiota in neurodegenerative diseases[END_REF][START_REF] Li | Dietary fructose-induced gut dysbiosis promotes mouse hippocampal neuroinflammation: a benefit of short-chain fatty acids[END_REF] . Fecal transfer from anxiety-and depression-like behaviors prone rats that exhibited gut dysbiosis and ventral hippocampus inflammation recapitulated rats behavior and central inflammation [START_REF] Pearson-Leary | The gut microbiome regulates the increases in depressive-type behaviors and in inflammatory processes in the ventral hippocampus of stress vulnerable rats[END_REF] , suggesting a possible causal link between gut dysbiosis and central inflammation. In the hypothalamus, no such relationship has been proved so far but a recent study in obese patients highlighted a significant association between hypothalamic inflammation, evaluated by magnetic resonance imaging, and specific gut microorganisms [START_REF] Kreutzer | Hypothalamic Inflammation in Human Obesity Is Mediated by Environmental and Genetic Factors[END_REF] . Long-term WD consumption induces a decrease in gut microbiota diversity and profound compositional changes [START_REF] Hamilton | Changes in intestinal barrier function and gut microbiota in high-fat diet-fed rats are dynamic and region dependent[END_REF][START_REF] Rizzatti | Proteobacteria: A Common Factor in Human Diseases[END_REF] . Short-term adaptation of the gut microbiota to the WD switch is also expected since gut microbiota is highly responsive to acute dietary changes. Indeed, 8hrs under WD are sufficient to induce significant composition changes in Human microbiota-colonized mice [START_REF] Turnbaugh | The Effect of Diet on the Human Gut Microbiome: A Metagenomic Analysis in Humanized Gnotobiotic Mice[END_REF] . Thus, we hypothesized that acute dietary switch from chow to WD results in early changes in microbiota composition and/or function that participates directly or indirectly to early hypothalamic inflammation in rats. To test this hypothesis, we determined how hypothalamic response as well as the gut microbiota composition and activity evolved during the first days following WD exposure, using 16S rRNA gene sequencing and caecal content and hypothalamic metabolomics analysis in conventional rats. Data integration tools were then used to highlight the potential links between microbiota changes and hypothalamic metabolic pathways associated to a proinflammatory context. Lastly, we used germ-free rats to investigate the potential connection between the gut microbiota and the early adaptation of hypothalamus to WD.

Methods

Animals

Animal protocols received written agreement from local ethics committees (Institutional Animal care and Use Committee UC Davis (experiment 1), APAFIS#2015102618593445 (experiment 2) and APAFIS#903-2015061809202358V3.33 (experiment 3)). Sample size was estimated following previous experiments, according to the variability of obtained results for each type of analysis. Animals were randomized in groups of similar body weight at the beginning of each experiment. No blinding was done during animal experiments, but the samples treatment was blinded. For all experiments the rats were housed individually under a 12:12h light/dark cycle and maintained at 22±2°C. The light cycle was 7am-7pm. The animals were euthanized between 9am-12am. Experiment 1. Ten male Wistar rats (9 weeks old, Harlan San Diego) had ad libitum access to water and chow diet (Purina Lab Diet 5001 rodent diet, fat 13% of energy, Supplementary table 1). After acclimatization period (1 week), rats were divided in two groups and fed ad libitum either chow or WD (Research Diet 12451, New Brunswick, USA, fat 45% of energy, Supplementary Table 1) for 6 weeks.

Food intake (kcal and kcal.g -1 of body weight per day) and body weight were measured weekly. After 6 weeks, rats were fasted overnight before euthanasia. Whole-blood glycemia was evaluated using Accu-check. Plasma was obtained by centrifugation (4000 rpm, 10 min, 4°C) and stored at -80°C. Fat pads (mesenteric, epididymal and retroperitoneal fat) were dissected and weighted to evaluate visceral adiposity.

Experiment 2. Thirty-three Wistar male rats (8-9 weeks old; Janvier Labs, Le Genest-Saint-Isle, France) had ad libitum access to water and chow diet (Special Diets Services, Rat and Mouse no. 1). Food consumption (kcal and kcal.g -1 of body weight per day) and body weights were recorded daily. Food intake was expressed as a percentage of the control food intake (the mean of the daily food intake during the three days preceding dietary change) for each animal. Groups of 6 to 7 rats were euthanized either before the dietary switch (T0) or 2h (T2H), 1 (TD1), 2 (TD2) and 4 days (TD4) after WD introduction. Animals were fasted for 12h then fed for 2h with their respective diet before euthanasia. The 2hr-food intake was similar among the various WD-fed and chow-fed groups. Hypothalami and caecal contents were sampled and stored at -80°C. Experiment 3. Nine germ-free (obtained from the germ-free rodent facility Anaxem, Micalis Institute, France) and 10 conventional male Fisher rats (Charles River, Arbresle, France) aged 8 weeks were housed individually for 3 weeks for acclimatization in sterile and non-sterile incubators, respectively.

Environmental sampling followed by bacterial culture tests were performed regularly to assess sterility.

During acclimatization, rats were fed sterilized (45kGy irradiation) chow (SAFE A03; Safe, Augy, France, fat 13.5% of energy, Supplementary Table 1) and sterilized water ad libitum. Germ-free and conventional rats were then divided into two groups receiving either irradiated chow or irradiated WD (45kGy irradiation) (SAFE 245HF, Safe, Augy, France, fat 45.9% of energy, Supplementary Table 1) for 2 days. Food intake of the four groups (kcal and kcal.g -1 of body weight per day) was recorded by weighting the food every 24 hours. The animals had access to food until euthanasia. After euthanasia, hypothalami were stored in RNA-later buffer at -80°C before gene expression analysis and CONV rats caecal contents stored at -80°C.

Plasma Lipopolysaccharides Binding Protein (LBP)

Plasmatic LBP levels were determined via ELISA kit according to the manufacturer's recommendations (Biometec, Greifswald, Germany).

Determination of caecal microbiota composition

Total DNA extraction and V3-V4 PCR were performed as already described [START_REF] Arnaud | Post-natal codevelopment of the microbiota and gut barrier function follows different paths in the small and large intestine in piglets[END_REF] .

Hypothalamus and caecal content metabolomic profiles

Frozen hypothalami were analyzed by mass spectrometry (ultra-high-performance liquid chromatography/tandem accurate mass spectrometry) performed by Metabolon Inc. (Durham, USA)(details in Supplementary methods 1). Caecal water samples were obtained from caecal contents as described in Supplementary methods 2. Untargeted caecal metabolomics profiles were acquired using gaz chromatography coupled to a high-resolution mass spectrometer (QToF 7200 ;Agilent) (INRAE, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France) as described previously [START_REF] Gao | Development of a Quantitative Metabolomic Approach to Study Clinical Human Fecal Water Metabolome Based on Trimethylsilylation Derivatization and GC/MS Analysis[END_REF] . Data were processed under the Galaxy web-based platform Workflow4Metabolomics 38 to yield a data matrix containing variables (retention times, m/z ratio) and peak intensities corrected for batch effects (details in Supplementary methods 2).

Hypothalamic gene expression levels

Total RNA extraction, reverse transcription and qPCR from hypothalami of germ-free animals and their conventional controls were performed as described previously [START_REF] Zhang | Fructose malabsorption induces cholecystokinin expression in the ileum and cecum by changing microbiota composition and metabolism[END_REF] . Expression levels of genes were determined by qPCR using SYBR Green chemistry or TaqMan gene expression assays (Supplementary Table 2).

Relative expression levels of target genes were calculated using the 2 -ΔΔCT method, considering the geometric mean of housekeeping genes to determine the ΔCT and the chow-fed conventional and germ-free rats group as reference groups to determine the ΔΔCT for the conventional and the germ-free groups, respectively. Samples were excluded when considered as outliers following Dixon statistical test.

Statistics and data integration

Statistical analysis in experiments 1 and 3 were performed using Mann-Whitney tests between chow and WD groups and Spearman correlations using Prism GraphPad software v.7.00 (GraphPad Software, San Diego, USA). ANOVA or Kruskal-Wallis, followed by Tukey's or Dunn's HSD posthoc tests when significant, respectively, were used for experiment 2 when comparing time-groups, also using Prism GraphPad. Results were considered significant when p<0.05.

Analyses of reads obtained after 16S rRNA gene sequencing from caecal content are detailed in the Supplementary methods 3. PLS-DA were performed on normalized metabolomic data using the mixOmics R package v.6.10.1 and details of the analysis are provided in the Supplementary method 3.

To perform data integration, the web application MiBiOmics [START_REF] Zoppi | MiBiOmics: An interactive web application for multi-omics data exploration and integration[END_REF] , based on Weighted Correlation Network Analysis method (WGCNA) [START_REF] Langfelder | WGCNA: an R package for weighted correlation network analysis[END_REF] , was used and described in the Supplementary methods 3.

Data availabity

R code and raw metabolomic data used in this study is available upon request to the corresponding author. Raw microbiota sequences are available at Sequence Read Archive (BioProject ID 602836).

Results

First week energy intake after dietary switch to WD was predictive of long-term obesity parameters

Several studies have shown that the intensity of the hyperphagic phase at normal weight following WD introduction is predictive of propensity to develop obesity on the long term [START_REF] Dourmashkin | Model for predicting and phenotyping at normal weight the long-term propensity for obesity in Sprague-Dawley rats[END_REF][START_REF] Vaanholt | Factors influencing individual variability in high fat diet-induced weight gain in out-bred MF1 mice[END_REF] . We confirmed that rats switched to WD displayed a first phase of increased energy intake followed by normalization of energy intake (Fig. 1 A (kcal.g -1 of body weight) and Supplementary Fig. 1 (kcal)). After 6 weeks of WD, they exhibited greater body weight gain (Fig. 1B), final body weight (572±15 vs. 513±11g, P=0.01), visceral adiposity (5.8±0.3 vs. 3.7±0.4%, P=0.002), plasma LBP (0.63±0.07 vs. 0.42±0.04 μg.ml -1 , P=0.02) and a tendency for greater fasted glycemia (275.0±46.7 vs. 166.6±18.1 mg.dl -1 , P=0.06). We also confirmed that first week energy intake was positively correlated to final body weight (Fig. 1C) and visceral adiposity (Fig. 1D). Interestingly, first week energy intake was also positively correlated to fasted glycemia (Fig. 1E) and plasma LBP (Fig. 1F) measured at week 6. Thus, the intensity of this phase of increased energy intake upon dietary switch to WD seems predictive of later obesity parameters but also of metabolic and systemic inflammatory parameters.

The WD switch induced a rapid change in hypothalamic metabolomic profile and impacted hypothalamic pathways involved in redox homeostasis and cell remodeling

We investigated the broad adaptation of the hypothalamus upon dietary switch to WD by analyzing the evolution of hypothalamic metabolome at different time-points after a switch to WD in rats. Rats had a standard response in terms of energy intake and 4 days of WD resulted in an asymptomatic period without increase in visceral adiposity or body weight differences (Supplementary Fig. 2).

Multivariate analysis revealed that hypothalamic metabolome evolved very rapidly in response to WD. Indeed, T0 and T2H hypothalamic metabolomes were significantly different from each other's (Fig. 2A). The metabolic adaptation continued between T2H and TD1 and persisted after TD1 even if the changes were less severe than during the first 24h following the diet switch (Supplementary Fig. 3).

Metabolites significantly explaining the differences between times groups were selected according to their Variable Importance in Projection (VIP) values, which represent how much they contribute to the obtained projection in Fig. 2A. The metabolites with criteria of VIP value>1 for the two first components emphasized that metabolomic changes occurred mainly in remodeling of cell membrane processes, arginine metabolism and hypothalamic redox homeostasis system (Supplementary Table 3).

Several metabolites of those pathways displayed significant levels variations or tendencies to vary across time, confirming their importance (Supplementary Table 4). First, WD introduction led to trending increase in oxidized glutathione (GSSG), suggestive of elevated oxidative stress (Fig. 2B).

This was supported by significant changes in compounds involved in glutathione turnover suggesting hypothalamic redox homeostasis impairment. The decrease in glutamate, glycine, 5-oxoproline and γglutamyl-tryptophan (γ-glutamyl-AA) indicated the use of the amino acids implicated in the glutathione synthesis and recycling while the significant increase in 2-hydroxybutyrate and ophthalmate suggested increased demand on the glutathione system. WD also led to a rapid and sustained increase in 13-hydroxy-9,11-octadecadienoic acid + 9-Hydroxy-10,12-octadecadienoic acid indicating a disruption of redox homeostasis. The increase in homoarginine levels in association with a decrease in citrulline, ornithine, and polyamines (putrescine, spermidine and spermine) suggested a modification of arginine metabolism potentially interfering with nitric oxide (NO) synthesis (Fig. 2C).

Membranes are primarily composed of glycerophospholipids conjugated to choline, ethanolamine, inositol, and serine. The decrease in phosphocholine, phosphatidylcholine and acylglycerophosphorylcholine as well as the increase in diacylglycerol, cytidine 5'-diphosphocholinecholine, 5'-diphosphocholine -ethanolamine, cytidine monophosphate, glycerophosphorylcholine and glycerophosphorylethanolamine revealed some degree of membrane remodeling in the hypothalamus in response to WD (Fig. 2D).

The WD switch induced a rapid change in caecal microbiota composition and caecal content metabolomic profile.

Multivariate analysis revealed that caecal microbiota composition evolved within 24h after the first exposure to WD (Figs 3A-C). Caecal microbiota composition, measured by Bray-Curtis β-diversity index (Fig. 3A), differed significantly from TD1 and onwards with significant differences between T0 or T2H and TD1, TD2 and TD4. The Shannon index indicated a significant decrease in diversity starting from TD1 (Fig. 3A). At the phylum level (Supplementary Fig. 4) only Proteobacteria relative abundance increased significantly at TD1 when compared to T0 and T2H and did not vary significantly afterwards. Analysis of family relative abundance variations across time (Supplementary Table 5) revealed a significant increase in Desulfovibrionaceae (Proteobacteria phylum) and Tannerellaceae (Bacteroidetes phylum) from TD2 to TD4 while Lactobacillaceae (Firmicutes phylum) decreased significantly between T0/T2H and TD2/TD4 (Fig. 3B). Therefore, exposure to WD rapidly lowered gut microbiota diversity and shifted bacterial population towards a higher prevalence of "proinflammatory" bacteria. Caecal content metabolome analysis revealed marked significant changes in profile as soon as T2H and between T2H and TD1 (Fig. 3C) likely pointing out the influx of new food compounds into the lower gut. Despite a less severe evolution of the caecal metabolome after TD1, it kept changing afterward as indicated by the significant difference between TD1 and TD4 highlighting the adaptation of the microbiota and intestinal cell host metabolism to the WD.

Integrative network analysis revealed potential links between caecal microbiota and hypothalamic redox homeostasis regulation

To unravel potential links between microbiota adaptation to WD and early hypothalamic oxidative stress, integrative network analysis was performed between microbiota composition, caecal metabolome and hypothalamic metabolome. Metabolites of redox homeostasis pathway were among the best drivers of time group separation. We therefore identified hypothalamic sub-networks containing them using the MiBiOmics application (2-hydroxybutyrate and ophthalmate in one subnetwork and 5-oxoproline and γ-glutamyl glycine in a second one). These two sub-networks were positively or negatively correlated to a caecal metabolomic sub-network (Fig. 4). This caecal metabolomic sub-network, as well as the two hypothalamic sub-networks, correlated positively and negatively to several caecal microbiota sub-networks. One was composed of 38 OTUs (Supplementary Table 6) and contained bacteria that correlated positively with hypothalamic ophthalmate and 2hydroxybutyrate and negatively with 5-oxoproline and γ-glutamyl glycine through the previously mentioned caecal metabolites. OTUs correlating negatively with ophthalmate and 2-hydroxbutyrate and positively with 5-oxoproline and γ-glutamyl glycine were members of four different microbiota sub-networks of various numbers of OTUs (from 12 to 241). Sub-networks of bacteria that were negatively correlated with hypothalamic oxidative stress markers were positively correlated with αdiversity indices.

Germ-free rats displayed lower energy intake and lower hypothalamic oxidative stress and inflammation activation after 2 days on WD

Data integration suggested that the link between hypothalamic oxidative stress and microbiota was driven by several subnetworks of bacteria. Therefore, we used germ-free rats to investigate if removing microbiota signaling would alter early energy intake as well as hypothalamic oxidative and inflammatory response to WD. GF rats energy intake during the 5 days preceding WD introduction was not significantly different from that of CONV rats (0.26±0.02 vs 0.26±0.02 kcal.g -1 of body weight, respectively). Similar to CONV rats, GF rats exhibited an increase in energy intake upon dietary switch to WD (Fig. 5A (kcal.g-1 of body weight) and Supplementary Fig. 5 (kcal)). However, this increase was significantly lower in GF than in CONV rats (P=0.03, Fig. 5A). Gut microbiota composition analyses in CONV rats showed a similar evolution to the one observed in experiment 2 (Supplementary Fig. 6) indicating a robust behavioral and microbial response to WD in the different experiments.

We then analyzed the expression of several genes involved in hypothalamic redox homeostasis. In CONV, but not in GF rats, mRNA levels of SOD2 and GLRX, involved in neutralization of superoxide byproducts of oxidative phosphorylation in the mitochondria and reduction of disulfides in target proteins, respectively, were increased after 2 days of WD compared to chow diet (P=0.04 and 0.02, respectively, Fig. 5B). This activation of redox maintenance machinery in CONV rats, but not GF ones, reinforces the idea that microbiota is involved in oxidative stress following the dietary switch to WD.

Finally, we investigated the hypothalamic expression of pro-inflammatory cytokines. In CONV rats, IL-6 mRNA level, but not that of other cytokines or inflammatory markers, increased after 2 days of WD compared to chow (P=0.02, Fig. 5C). In GF rats, none of these markers appeared altered.

Discussion

Oxidative stress: an early hypothalamic response to WD exposure

The hypothalamic response to short-term WD exposure at the metabolite scale revealed the immediate host response to the dietary switch, with the oxidative stress response being one of the main pathways quickly modified. First, disturbances in glutathione metabolism were observed after only 1 day of WD and the levels of oxidized glutathione increased after 2 days. The decreases in γ-glutamyl-AA and 5oxoproline are probably due to their metabolism into glutathione in response to oxidative stress [START_REF] Lu | Regulation of glutathione synthesis[END_REF] .

Ophthalmate, which markedly increased after 2 days of WD consumption, is also an oxidative stress biomarker linked to the glutathione metabolism [START_REF] Dello | Systematic review of ophthalmate as a novel biomarker of hepatic glutathione depletion[END_REF] . Glutathione is the most important brain antioxidant and its alteration is known to contribute to neurodegenerative diseases [START_REF] Johnson | Dysregulation of Glutathione Homeostasis in Neurodegenerative Diseases[END_REF] . Therefore, in the context of WD exposure, dysregulation of glutathione homeostasis may be a key component involved in longterm impairment of hypothalamic functions. Increased homoarginine levels after 4 days of WD could also contribute to oxidative stress through ROS, NO or peroxynitrite formation [START_REF] Schieber | ROS Function in Redox Signaling and Oxidative Stress[END_REF] . Lastly, oxidative stress elevation is also confirmed at the molecular level by the increase in sod2 and glrx expression levels after 2 days of WD consumption in conventional rats. These two genes encode anti-oxidant proteins and are overexpressed in response to oxidative stress elevation [START_REF] Hanschmann | Thioredoxins, Glutaredoxins, and Peroxiredoxins-Molecular Mechanisms and Health Significance: from Cofactors to Antioxidants to Redox Signaling[END_REF] .

Acutely, production of physiological levels of ROS in the hypothalamus is necessary to maintain energy homeostasis as they participate to brain lipid and glucose sensing [START_REF] Leloup | Mitochondrial Reactive Oxygen Species Are Required for Hypothalamic Glucose Sensing[END_REF][START_REF] Benani | Role for Mitochondrial Reactive Oxygen Species in Brain Lipid Sensing: Redox Regulation of Food Intake[END_REF] . However, increased ROS level beyond physiological concentration can induce the production of transcription factors involved in inflammation [START_REF] Hussain | Oxidative Stress and Inflammation: What Polyphenols Can Do for Us? Oxidative[END_REF] . Therefore, this early oxidative stress response could be one of the regulator of the early inflammation associated with WD consumption previously reported [START_REF] Morari | Fractalkine (CX3CL1) Is Involved in the Early Activation of Hypothalamic Inflammation in Experimental Obesity[END_REF][START_REF] Thuc | Hypothalamic Inflammation and Energy Balance Disruptions: Spotlight on Chemokines[END_REF] .

Microbiota adaptation to WD: the setup of a pro-inflammatory intestinal environment

Caecal microbiota composition and activity were highly and rapidly impacted by the dietary switch.

Caecal microbiota composition evolved towards a pro-inflammatory bacterial environment with an increase in Desulfovibrionaceae (Proteobacteria) relative abundance within 24h. Proteobacteria as Gram-negative bacteria and Desulfovibrionaceae as sulfate-reducing bacteria contribute to inflammation in the intestinal epithelium [START_REF] Rizzatti | Proteobacteria: A Common Factor in Human Diseases[END_REF][START_REF] Shin | Proteobacteria: microbial signature of dysbiosis in gut microbiota[END_REF] . Tannerellaceae abundance increased in a similar manner. Tannerellaceae are endotoxin-producers and a member of this family, Parabacteroides distasonis, has been reported to be a microbial marker of inflammatory bowel disease in human [START_REF] Lopetuso | Gut Microbiota in Health, Diverticular Disease, Irritable Bowel Syndrome, and Inflammatory Bowel Diseases: Time for Microbial Marker of Gastrointestinal Disorders[END_REF] .

Two days of WD consumption induced a drastic decrease of Lactobacillaceae relative abundance.

Lactobacillaceae, and particularly members of the Lactobacillus genus, participate to the maintenance of intestinal barrier integrity by modulating tight-junction proteins expression [START_REF] Karczewski | Regulation of human epithelial tight junction proteins by Lactobacillus plantarum in vivo and protective effects on the epithelial barrier[END_REF] . Their decrease could contribute to bacteria and/or pro-inflammatory bacterial components passage across the intestinal epithelium, leading to systemic inflammation.

A potential link between specific bacteria and early hypothalamic oxidative stress following WD revealed by two different strategies: germ free animals and data integration

Unlike in conventional rats, dietary switch from chow to WD in GF rats did not impact expression levels of genes involved in oxidative stress and inflammation. In conventional rats, the hypothalamic inflammatory response to WD remained modest since only IL-6 expression was upregulated.

Hypothalamic inflammatory response to WD has also been associated by others to a modest increase in expression of IL-6 after 1d to 3d of WD and no change in IL-1β and TNF-α [START_REF] Thaler | Obesity is associated with hypothalamic injury in rodents and humans[END_REF][START_REF] Waise | One-day highfat diet induces inflammation in the nodose ganglion and hypothalamus of mice[END_REF][START_REF] Baufeld | High-fat diet-induced brain regionspecific phenotypic spectrum of CNS resident microglia[END_REF] . The lack of change in the GF rats suggests that the presence of gut microbiota participates to the early hypothalamic stress encountered shortly after WD introduction. Data integration methods have been recently emphasized as new strategies to identify candidate biomarkers (genes, metabolites or bacteria) involved in studied processes [START_REF] Moschen | Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower[END_REF][START_REF] Zhang | Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis[END_REF] . Here, we used a network-based integrative approach that highlighted sub-networks of bacteria associated with hypothalamic early oxidative stress through caecal metabolites. The first highlighted bacterial subnetwork contains E. fergusonii and F. plautii, associated with human intestinal inflammatory disease [START_REF] Gaastra | Escherichia fergusonii[END_REF][START_REF] Armstrong | Host immunoglobulin G selectively identifies pathobionts in pediatric inflammatory bowel diseases[END_REF] . F. plautii also displayed invasive capacity in human intestinal immune cells [START_REF] Armstrong | Host immunoglobulin G selectively identifies pathobionts in pediatric inflammatory bowel diseases[END_REF] . Therefore, both bacteria could enhance intestinal inflammation and gut barrier disruption in response to WD and be distant actors of increased hypothalamic oxidative stress. A large sub-network, negatively correlated to hypothalamic oxidative stress metabolites, contained 11 members of the Lactobacillus genus whose lower abundance may be associated with alteration of the gut barrier function which could participate to hypothalamic oxidative stress induction [START_REF] Karczewski | Regulation of human epithelial tight junction proteins by Lactobacillus plantarum in vivo and protective effects on the epithelial barrier[END_REF] . Sub-networks of bacteria that were negatively correlated with hypothalamic oxidative stress markers were positively correlated with α-diversity indices, and vice-versa, underlying the importance of a diverse gut microbiota to protect against stress and potentially pathogenic bacteria settlement [START_REF] Kriss | Low diversity gut microbiota dysbiosis: drivers, functional implications and recovery[END_REF] . Interestingly, no members from the Tannerellaceae and Desulfovibrionaceae families, whose relative abundance was significantly impacted by WD introduction, were linked to hypothalamic elevated oxidative stress. This highlights the strength of WGCNA subnetwork selection, which identifies potential small bacteria ecosystems that evolve together, and is not only based on differences in relative abundance values. The unknown caecal metabolites highlighted by this approach are also potential key actors in the microbiota-guthypothalamus axis and oxidative stress but further work is needed to identify them. The mechanisms of action of "the pro-inflammatory intestinal environment" described in the current study in response to short WD exposure remain unknown. In addition to a potential passage of pro-inflammatory bacteria or bacterial components into the circulation, pro-inflammatory bacteria may also signal to the hypothalamus through the vagus nerve via nodose ganglion inflammation as described previously in the context of short-term exposure to WD in mice [START_REF] Waise | One-day highfat diet induces inflammation in the nodose ganglion and hypothalamus of mice[END_REF] .

In conclusion, the present study suggests that early hypothalamic adaptation to WD is strongly linked to gut microbiota changes. Hypothalamus rapidly undergoes oxidative stress at the metabolite and molecular scale during WD switch that could induce hypothalamic inflammation. At the same time, caecal microbiota quickly evolves towards a composition encountered in pro-inflammatory intestinal diseases, which could participate to an early systemic and/or hypothalamic inflammation. Data integration revealed a potential link between gut microbiota members and oxidative stress through unknown caecal metabolites. This link highlights the importance of the intestinal bacterial ecosystem in its entirety, being modulated in response to dietary change and acting, as a network of diverse but cooperating bacteria, on host physiology.

List of abbreviations

LBP: Lipopolysaccharides binding protein; OTU: Operational Taxonomic Unit; PLS-DA: partial least square-discriminant analysis; ROS: reactive oxygen species; VIP: Variable Importance Projection; WD: Western diet; WGCNA: weighted gene correlation network analysis.

Figures legendsFigure 1 .

 1 Figures legends

Figure 2 .

 2 Figure 2. Hypothalamic metabolome evolution during adaptation to WD in conventional rats.

Figure 3 .

 3 Figure 3. Evolution of caecal microbiota diversity and composition during early adaptation to

Figure 4 .

 4 Figure 4. Tripartite network showing links between caecal microbiota, caecal metabolome and

Figure 5 .

 5 Figure 5. Germ-free and conventional rat response to 2 days of WD consumption. Rats daily

Acknowledgments

ACCEPTED MANUSCRIPT

We thank Mathilde Guerville and Annaëlle Sinquin as well as Helen Raybould. We thank Isabelle Nogret for her technical help. Caecal Metabolomic analysis were performed within the metaboHUB French infrastructure (ANR-INBS-0010). This work was partly funded by a grant from INRAE AlimH division.

Competing Interests

All authors have no financial or personal conflict of interest to declare.

Supplementary information is available at International Journal of Obesity's website.


Jean-Rémy Chardonnet
email: jean-remy.chardonnet@ensam.eu

Mohammad Ali Mirzaei

Frédéric Merienne

Influence of navigation parameters on cybersickness in virtual reality

Keywords: Cybersickness, navigation, parameters, navigation device, virtual reality

Cybersickness remains a major challenge in the virtual reality community. It occurs mainly when navigating in a 3D immersive virtual environment. Several parameters are known to influence the users' cybersickness level while navigating, that can be either technological or neuro-psychological. This study investigates two of these parameters that are the distance from a virtual barrier and the choice of the navigation interface.

An experiment was performed for each of these parameters to evaluate their influence on the variation of cybersickness. For each experiment, participants were asked to navigate in a large virtual room with walls that were textured with a black and white lined pattern to voluntarily exacerbate cybersickness. The level of cybersickness was collected through subjective (Simulator Sickness Questionnaire) and behavioral (evolution of postural sway) measurements. Results allow drawing suggestions for optimal navigation, so that cybersickness can be significantly reduced, thus providing with enhanced user experience.

Introduction

With the rapid development of virtual reality (VR) and the introduction on the market of low-cost head-mounted displays and interaction devices, major challenges still need to be addressed so that VR technologies can be massively spread in various application fields. Among these, cybersickness represents a highly critical issue and is still receiving much attention. A strong parallel was done for years with research on motion sickness and visually-induced motion sickness (VIMS) (e.g., [START_REF] Bos | A theory on visually induced motion sickness[END_REF][START_REF] Oman | Motion sickness: a synthesis and evaluation of the sensory conflict theory[END_REF], as symptoms of cybersickness can be very similar: cold sweat, belching, retching, pallor, headache, nausea, possible vomiting.

In the virtual reality field, three main theories try to explain the origins of cybersickness.

The first theory is the well-known sensory conflict, stating that "motion sickness is a selfinflicted maladaptation phenomenon which occurs at the onset and cessation of conditions of sensory rearrangement when the pattern of inputs from the vestibular system, other proprioceptors and vision is at variance with the stored patterns derived from recent transactions with the spatial environment" ( (Reason & Brand, 1975), pp. 274-275). This theory is well accepted in the VR community, e.g., [START_REF] Kolasinski | Simulator Sickness in Virtual Environments[END_REF][START_REF] Akiduki | Visualvestibular conflict induced by virtual reality in humans[END_REF][START_REF] Kemeny | From driving simulation to virtual reality[END_REF]. The second theory compares sickness to food poisoning, stipulating that undesirable stimulations found in some virtual environments (VE) can affect the visuo-vestibular system in such a way that the body considers that it has ingested toxic substances [START_REF] Treisman | Motion sickness : an evolutionary hypothesis[END_REF]. This theory is less admitted in the VR community since it lacks prediction power and does not explain why some cybersick users do not have emetic reactions. The third and last theory is postural stability, stating that subjects who cannot maintain their balance may experience sickness (Riccio & Stroffegen, 1991). In recent years, this theory has been the center of attention and is taken into consideration in the VR community, e.g., [START_REF] Cobb | Measurement of postural stability before and after immersion in a virtual environment[END_REF][START_REF] Kennedy | Postural instability induced by virtual reality exposure: development of a certification protocol[END_REF][START_REF] Murata | Effects of duration of immersion in a virtual reality environment onpostural stability[END_REF][START_REF] Chardonnet | Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality[END_REF].

Different parameters are involved in the occurrence of cybersickness that can be either technological or neuro-psychological. Latency (Wilson, 2016), the field of view [START_REF] Lin | Effects of field of view on presence, enjoyment, memory, and simulator sickness in a virtual environment[END_REF][START_REF] Sharples | Virtual reality induced symptoms and effects (VRISE): Comparison of head mounted display (HMD), desktop and projection display systems[END_REF], low frame rates [START_REF] Laviola | A discussion of cybersickness in virtual environments[END_REF], an inappropriate adjustment of navigation parameters [START_REF] So | Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment[END_REF], navigation interfaces [START_REF] Mestre | Evaluation of navigation interfaces in virtual environments[END_REF] or an incorrect selection of scene contents [START_REF] Lo | Cybersickness in the presence of scene rotational movements along different axes[END_REF] are factors that are often investigated. For example, latency jitter introduced in a head-mounted display was shown to significantly increase self-reported cybersickness [START_REF] Stauffert | Effects of Latency Jitter on Simulator Sickness in a Search Task[END_REF]. Predictive compensation for head movements can be a solution to lower latency and thus to reduce cybersickness [START_REF] Buker | The Effect of Apparent Latency on Simulator Sickness While Using a See-Through Helmet-Mounted Display: Reducing Apparent Latency With Predictive Compensation[END_REF]. Cybersickness can also be related to circular and linear vection, image velocities or pseudo Coriolis and Purkinje effects. Past research showed that vection may be a prerequisite for VIMS [START_REF] Keshavarz | Vection and visually induced motion sickness: how are they related?[END_REF], but the relation between vection and cybersickness appears to be in fact much more complex [START_REF] Palmisano | Vection and cybersickness generated by headand display motion in the Oculus Rift[END_REF], though vection change was shown to significantly increase cybersickness [START_REF] Bonato | Vection change exacerbates simulator sickness in virtual environments[END_REF].

In this paper, we focus on the effects of navigation parameters, namely the distance to a virtual barrier and the navigation interface, on the level of cybersickness. Past research also studied the effect of navigation speeds and provided some recommendations in terms of optimal thresholds for translational and rotational speeds in a VE to limit cybersickness [START_REF] Kemeny | How to avoid simulation sickness in virtual environments during user displacement[END_REF][START_REF] So | Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment[END_REF]. Notably, [START_REF] So | Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment[END_REF] showed that depending on the navigation speeds, onset times of cybersickness can be significantly affected, but not the ratio between the increase of sickness and the duration of exposure. Much effort has been put in evaluating the effect of rotational speeds, as it is known to strongly encourage the occurrence of sickness (see for example [START_REF] Hu | Motion Sickness and Electrogastrographic Activity As a Function of Speed of Rotation of an Optokinetic Drum[END_REF][START_REF] Farmani | Viewpoint Snapping to Reduce Cybersickness in Virtual Reality[END_REF]). Here we chose to concentrate on translational movements to keep it simple. Other work considered rather acceleration than speed as being highly influent on cybersickness [START_REF] Lloarch | Simulator sickness and Presence using HMDs: comparing use of a game controller and a position estimation system[END_REF][START_REF] Lavalle | Virtual Reality[END_REF][START_REF] Hsiao | Effect of Acceleration on Simulator Sickness in Virtual Reality[END_REF]. Especially, LaValle (2017) stated than acceleration was even a bigger contributor to cybersickness than other factors because of a strong induced vection. Therefore, controlling accelerations should be more efficient to reduce cybersickness, as proposed for example by [START_REF] Plouzeau | Using cybersickness indicators to adapt navigation in virtual reality: a pre-study[END_REF], rather than controlling speeds, as for example in [START_REF] Argelaguet Sanz | Adaptive Navigation for Virtual Environments[END_REF].

Regarding navigation interfaces, past work reported a major effect on cybersickness. For example, [START_REF] Chen | 6DoF navigation in virtual worlds: comparison of joystick-based and head-controlled paradigms[END_REF] compared a classical joystick-based interface and steering-based navigation, and found significantly lower cybersickness levels with a steering-based paradigm than with joystick-based interaction. The result was explained by (i) a big difference in visuovestibular stimulation (joystick-based interaction does not encourage stimulating the inner ear, to the contrary of steering-based paradigms), and (ii) a better control of movements when using the steering-based method than with a joystick-based interface (navigation tends to not be smooth with joystick-based interfaces). [START_REF] Coomer | Evaluating the effects of four VR locomotion methods: joystick, arm-cycling, point-tugging, and teleporting[END_REF] compared four navigation techniquesjoystick, teleportation, point-tugging and arm-cyclingand obtained significant differences among them in terms of cybersickness levels with joystick-based methods being much more prone to cybersickness than teleportation and arm-cycling. Natural gesture-based techniques are another well-considered solution for an improved VR experience. Among them, walking-in-place (WIP) was much studied over the past years [START_REF] Slater | The Virtual Treadmill: A Naturalistic Metaphor for Navigation in Immersive Virtual Environments[END_REF], as it allows an enhanced sensation of walking with higher presence than joystick-based techniques [START_REF] Razzaque | Redirected walking in place[END_REF]. However, some work revealed WIP to generate more cybersickness [START_REF] Usoh | Walking > walking-in-place > flying, in virtual environments[END_REF].

We contribute to this research by exploring the influence of the distance from a virtual object and the navigation interface itself. We have not considered other parameters, as we wanted to stick to navigation implementation matters rather than neuro-psychological considerations. However, it is clear that cross effects of parameters influencing cybersickness exist. In this study, the level of cybersickness was estimated based on the features proposed in [START_REF] Chardonnet | Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality[END_REF]. In their study, the shape, the area and the difference between low frequency and high frequency components of the user's postural sway were introduced as a set of efficient features to estimate and predict cybersickness during navigation in a virtual environment. Results suggested that when cybersickness occurs, the projection of the user's center of gravity, as measured by a balance board, describes a shape that evolves from an ellipse to a circle and grows in size. Moreover, the analysis of the postural sway signals in the frequency domain reveals an emergence of high frequency components in the presence of cybersickness (low frequency components represent voluntary movements while high frequency components indicate involuntary movements; the limit between low and high frequency components is set to 1Hz). The difference between low and high frequency components tends to increase as cybersickness intensifies. Furthermore, this difference was shown to strongly correlate to cybersickness scores as measured by the well-known Simulator Sickness Questionnaire (SSQ) [START_REF] Kennedy | Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness[END_REF].

Compared to recent work focusing on using head-mounted displays exclusively, we chose to use a CAVE system that (i) allows not being totally shielded from reality and (ii) allows a wider field of view, these factors being considered as contributing to the occurrence of cybersickness [START_REF] Lin | Effects of field of view on presence, enjoyment, memory, and simulator sickness in a virtual environment[END_REF][START_REF] Sharples | Virtual reality induced symptoms and effects (VRISE): Comparison of head mounted display (HMD), desktop and projection display systems[END_REF]. Note that here we do not aim at proposing methods to reduce cybersickness, as it is beyond the scope of this paper. Nonetheless, we will provide some suggestions for future research.

Throughout this paper and for each experiment, postural sway as considered in Chardonnet et al.'s work [START_REF] Chardonnet | Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality[END_REF] and cybersickness scores as measured by the SSQ will be considered as metrics for evaluating the influence of the above-mentioned navigation parameters on cybersickness:

• Postural sway signals will be analyzed in the frequency domain after performing a Fast Fourier Transform in order to get the corresponding spectra. The differences between low and high frequency components will be computed. The spectra are limited to 0-20Hz [START_REF] Technoconcept | Techno-concept balance board manual[END_REF]. In fact, since body movements are not extremely fast due to the body's natural inertia, high frequencies cannot extend to higher than a specific limit to avoid strong noise. On the other hand, movements can contain 0Hz components, which means that the body is in a standing posture without any voluntary and involuntary movements.

• Cybersickness scores will be calculated in a slightly different way than the one provided by the SSQ scoring guideline: each rated score of each symptom for each symptom cluster (nausea, oculomotor, disorientation) will be added, each cluster score will be then multiplied by their specific weight factor, all the three subscores will be finally summed up weighted by 3.74. This will lead to possibly higher values than 300 (this value being defined by [START_REF] Kennedy | Configural scoring of simulator sickness, cybersickness and space adaptation syndrome: similarities and differences[END_REF] as the maximal value for SSQ scores).

The paper is organized as follows. We first present in Section 2 the VR setup and the navigation interfaces we chose to use and compare. Then in Section 3 and Section 4 two experiments are described corresponding to the study of the distance from a virtual barrier and the navigation interface, respectively. We conclude in Section 5, giving some perspectives.

Navigation interfaces

Here we present two navigation interfaces that were used in the different experiments, that correspond to two main families of interfaces: a device-based navigation interface (DBNI) and a natural gesture-based navigation interface (NGBNI).

Virtual reality setup

Before describing the different navigation interfaces, the virtual reality setup that was used throughout the experiments, is presented. It consists of a 3x3x3m four-sided CAVE system with a 1400x1050px resolution per side. Stereoscopic image is projected through two Projection Design F30SX+ per side. An ART infra-red-based optical tracking system is used to track users in position and rotation, as well as interaction devices.

A homemade software development platform was developed in C++ to manage the connections within the whole VR equipment. Application development was achieved through JavaScript. OpenSceneGraph was used to render 3D virtual environments, on top of OpenGL.

MPI and four NVidia Quadroplex GPUs allow projecting the generated model at 60fps. Devices are connected through Virtual Reality Peripheral Network (VRPN). The overall latency is estimated at around 40ms.

Device-Based Navigation Interface (DBNI)

The first navigation interface is based on a Flystick, a wand-like device with an embedded joystick. Joystick-based devices are widely used for navigation purposes [START_REF] Bowman | 3D User Interfaces: Theory and Practice[END_REF] and are well-known among gamers, which justifies our choice for considering them in this study. Users are capable of moving forward/backward and turn to the left/right up to ±15°, if required. The Flystick has five buttons, one joystick handle, an infra-red-based optic tracker (position and orientation) and a trigger button. Figure 1 demonstrates the upper view of a Flystick with a label on each button. The joystick handle sends forward/backward commands to the navigation system. Rotations are by default commanded by turning the handle to the left and right (first option). However, the navigation system can also perform rotational movements by reading the instantaneous orientation of the optical trackers attached to the Flystick and redirecting user movements along the Flystick orientation (second option). The navigation task is initiated and terminated when users push the buttons labeled "Start" and "Stop", respectively.

The additional "+" and "-" buttons were not used in this study.

Natural Gesture-Based Navigation Interface (NGBNI)

The second interface is based on natural body movements, which allows for increased naturalness of navigation in virtual environments [START_REF] Usoh | Walking > walking-in-place > flying, in virtual environments[END_REF]. Body movements include walking, hand and head movements. Here a Microsoft Kinect for Xbox 360 was used. It captures users' motions and a FAAST VRPN server [START_REF] Suma | Faast: The flexible action and articulated skeleton toolkit[END_REF] Movements are processed by mother wavelets to improve real-time user gesture analysis [START_REF] Mirzaei | Improvement of the realtime gesture analysis by a new mother wavelet and the application for the navigation inside a scale-one 3D system[END_REF]. Users' natural walking controls the translational movement speed, which means that, the faster one walks, the higher navigation speed. Rotational movements (Fig. 2.b) are commanded when the left/right hand moves above the shoulder: when the left hand is raised, users rotate to the left and conversely when the right hand is raised, they rotate to the right.

Rotational speed is controlled by the height of the hand, which means that, the higher the hands, the faster the rotations.

Experiment 1: effect of the distance from the virtual barrier on cybersickness

The first parameter considered in this study is the distance from a virtual barrier and its effects on the level of cybersickness during navigation. Other parameters such as the navigation speed in the virtual scene are kept constant. This study focuses only on translational movements not to bias results, as rotations are known to have a strong influence on generating sickness [START_REF] Hu | Motion Sickness and Electrogastrographic Activity As a Function of Speed of Rotation of an Optokinetic Drum[END_REF]. Translational motion is defined as straight movements from one point to another without any rotation along the path in-between. A translational movement in VEs can be expressed by 𝑉 𝐿 = 𝑓 𝐿 (𝑣, 𝑑) where independent variables 𝑣 and 𝑑 represent the velocity (m.s -1 ) and the distance (m) from a virtual barrier respectively.

Experimental design

The virtual environment consisted of a large hall (around 100 meters wide) with walls that were textured with a black and white lined pattern. This pattern was designed to voluntarily exacerbate cybersickness. Four rows of balls were lined up parallel to the virtual wall, indicated by "path 1", "path 2", "path 3" and "path 4" in Fig. 3, and were considered as path indicators.

Participants navigated along path 1 to path 4 using the DBNI mode. This mode was chosen as we suspected participants to be more familiar with such a device than with NGBNI. Path 1 to path 4 were adjusted 𝐷 1 = 1m, 𝐷 2 = 2m, 𝐷 3 = 3m and 𝐷 4 = 4m away from the virtual wall, respectively. The path indicators were placed 1.25 m above the ground to help users keep a constant distance from the virtual wall. The real setup is shown in Fig. 4. Navigation speed was kept constant and set to 2m.s -1 . Though this value is higher than the average walking speed, we set this speed to facilitate the generation of cybersickness. The hypothesis formulated for this experiment is that increasing the distance between the virtual barrier and the participants will result in lower cybersickness levels.

Participants

Ten subjects (8 males and 2 females) participated in the experiment. All participants were recruited among university members (students and staff). There was an individual briefing to give enough information about the test procedure and possible risks before the experiment. A consent form was signed before being exposed to the virtual environment. All subjects participated voluntarily in the experiment (no compensation was given). A demographic questionnaire was filled out by the subjects to find out their background and to evaluate their health condition. From this questionnaire, no health issue was reported by any of the subjects.

Protocol

Participants were required to navigate along each path in the CAVE. Postural sway was measured prior to the experiment and after each path completion, for 30 seconds each time. We used a TechnoConcept Stabilotest balance board that is able to track the evolution of the ground projection of the subjects' center of gravity with a sampling frequency of up to 40Hz [START_REF] Technoconcept | Techno-concept balance board manual[END_REF]. During recording, subjects were asked to stand still on the balance board while fixing a point displayed on a wall. Kennedy's Simulator Sickness Questionnaire (SSQ) [START_REF] Kennedy | Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness[END_REF] was collected after each path completion from each subject as psychological measurements.

The examiner could stop the experiment if the subjects were not feeling well. In addition, between each navigation round, subjects were able to rest for a few minutes (no more than 5 minutes) if they wished. We did not have any case of serious sickness during the experiment. Note that before the experiment, subjects were asked whether they felt any symptom that could underlie any sickness and whether they were in good condition. The reason for not requesting subjects to fill an SSQ prior to the test lies in its possibility of influencing the subjects' psychological state with regard to exposure to VR, as past research showed that preexposure sickness questionnaires could inflate cybersickness scores (Young et al., 2006). All subjects reported being in their usual state of fitness without any symptoms.

The entire experiment lasted around 20 minutes.

Results

Figure 5 shows the average difference between the postural sway's low frequency (LF) and high frequency (HF) components for each path. Recall that the larger the difference, the more sickness. A one-way ANOVA was conducted to compare the LF-HF difference for each path.

Normality checks and Levene's test were carried out and the assumptions met. Results showed a significant effect of the distance to the wall on the difference between LF and HF components, F(3,36)=4.719, p=.007<.05. Post-hoc comparisons using Student's t-tests revealed that sickness in path 1 (M=1.34, SD=0.12) was significantly higher than in path 4 (M=1.13, SD=0.11) (t(9)=7.27, p=.00005<.05). However, no significant difference was found between path 1 and path 2 (M=1.23, SD=0.16) (t(9)=2.08, p=.067), between path 1 and path 3 (M=1.29, SD=0.13) (t(9)=0.90, p=.394), as well as between path 2 and path 3 (t(9)=-0.89, p=.398). Last, sickness in path 4 was significantly lower than in path 3 (t(9)=2.92, p=.017<.05) but no significant difference was observed with path 2 (t(9)=1.99, p=.078).

Following the same methodology, the SSQ scores were analyzed. Normality checks and Levene's test were carried out and the assumptions met. A one-way ANOVA was conducted to compare the SSQ scores for each path. Results did not show any significant effect of the distance to the wall on the SSQ scores, F(3,36)=1.394, p=.260>.05 (see Fig. 6). However, cybersickness was observed to decrease on average as subjects navigated further away from the virtual barrier. Therefore, a comparison was done between path 1 (the closest to the wall) and path 4 (the farthest to the wall). A Student's t-test revealed a significant difference between these paths (Mpath1=288.02, SDpath1=109.85; Mpath4=175.77, SDpath4=125.78; t(9)=2.34, p=.044<.05), which indicates that closer distances induces more vection, thus more cybersickness. No significant differences were however detected between the other paths.

Discussion

Statistical analysis of the SSQ scores did not reveal any significant effect of distance on cybersickness, whereas postural sway showed opposite results, which suggests that the effect of distance may not be as important as expected. However, from both measures, a significant difference was observed between path 1 and path 4, which may indicate that when navigating close to virtual walls, cybersickness is much stronger. Here, the virtual wall was textured with a pattern that can easily trigger cybersickness. However, in normal virtual environments, such a pattern is not common; therefore, cybersickness might be less important than the one observed in this experiment. Much research studied the effect of such a pattern on eye movement, through the well-known optokinetic reflex, albeit mostly applied to rotational movements [START_REF] Nooij | Vection is the main contributor to motion sickness induced by visual yaw rotation: Implications for conflict and eye movement theories[END_REF]. However, we focused here on translational movements only and the required task was not to follow the pattern but navigation paths. Interestingly, the evolution of the LF-HF differences did not follow exactly the same pattern as for the SSQ scores, as a slight increase was observed on average between path 2 and path 3. Further studies with more participants should be carried out to confirm these findings and to determine their exact cause.

Our findings can also be explained by motion parallax. By definition, the farther away a subject is from an object (here a virtual wall), the slower he/she perceives motion along that object. Therefore, from a perceptual point of view, as speed was kept constant whatever the distance from the virtual wall was, the farther away from the barrier subjects navigated, the slower motion was perceived, which contributed to reduce cybersickness effects. As a result, one suggestion to alleviate cybersickness effects would be to adjust motion speed as a function of the distance from virtual obstacles. Another possibility could be to deform locally the geometry of the surrounding environment to lower vection, as proposed for example in [START_REF] Lou | Reducing Cybersickness by Geometry Deformation[END_REF].

Experiment 2: comparison between DBNI and NGBNI

The effect of the navigation interface is studied in this experiment. Two navigation interfaces, DBNI (Flystick) and NGBNI (WIP with gesture recognition using a Kinect), described in Section 2, are selected to represent the two main categories of navigation interfaces. When attempting to walk in a natural way in a virtual environment, the motion pattern perceived by sensory afferents matches the proprioceptive pattern, which in turn creates less sensory conflict at the onset and the cessation of the sensory rearrangement [START_REF] Chance | Locomotion mode affects the updating of objects encountered during travel: The contribution of vestibular and proprioceptive inputs to path integration[END_REF]. For that reason, it makes sense to draw up the hypothesis that natural gesture-based navigation interfaces will provoke less cybersickness compared to DBNI.

Experimental design

As in the previous study, this experiment consisted in moving along a straight path in a large virtual environment. The environment was the same as in the previous experiment, except that only one row of yellow balls was displayed parallel to the wall of the hall. The distance of the path to the virtual wall was set to 4 meters to stay consistent with the results of Experiment 1.

Navigation speed was kept the same as in Experiment 1: 2m.s -1 .

Participants

Seventeen subjects (12 males and 5 females) among university members were selected to participate in the experiment. There was an individual briefing to give enough information about the test procedure and possible risks before each experiment. A consent form was signed before being exposed to the virtual environment. All subjects participated voluntarily in the experiment (no compensation was given). A demographic questionnaire was filled out by the subjects to know their background and to evaluate their health From this questionnaire, no health issue was reported by any of the subjects. Additionally, all subjects reported being in their usual state of fitness without any symptom of sickness.

Protocol

The experiment was carried out as follows:

1. The postural sway of each subject was recorded for 30s before the experiment.

2. Subjects were invited to enter the CAVE and to navigate along the path, either with NGBNI or with DBNI. The navigation interface was attributed randomly. At the end of the path, subjects were removed from the CAVE.

3. The postural sway of each subject was recorded again for 30s. 4. Subjects were asked to fill out an SSQ. 5. The experiment was repeated with the other interface on another day to prevent accumulation effects.

The examiner could stop the experiment if the subjects were not feeling well. We did not have any case of serious sickness during the experiment.

The entire experiment lasted around 10 minutes.

Results

Figure 7 shows the average difference between the postural sway's LF and HF components for each interface type. As in the previous experiment, the larger the difference, the more sickness. A Student's t-test was conducted to compare the LF-HF difference for each interface. Normality checks and Levene's test were carried out and the assumptions met. Results revealed that the difference between LF and HF components was significantly higher with DBNI (M=1.61, SD=0.10) than with NGBNI (M=1.22, SD=0.14), t(16)=9.74, p<.001, which suggests that DBNI provokes more cybersickness than NGBNI. Similarly, a Student's t-test was conducted to compare the SSQ scores for each interface.

Normality checks and Levene's test were carried out and the assumptions met. The sickness score associated with DBNI (M=350.0, SD=50.63) was significantly higher than with NGBNI (M=150.0, SD=25.63), t(16)=14.53, p<.001 (see Fig. 8), indicating that DBNI does generate more cybersickness than NGBNI.

Discussion

A significantly smaller LF-HF difference was observed with NGBNI than with DBNI, which means that less cybersickness was induced by natural navigation interfaces. This result was confirmed by significantly lower cybersickness scores for NGBNI than for DBNI. NGBNI provide thus better navigation experience to users in terms of cybersickness compared to DBNI.

Several reasons can explain these results. First, when using DBNI devices, subjects hardly moved, therefore the likeliness of the sensory conflict to occur was much higher. Whereas, when using NGBNI devices, subjects needed to move their body, which stimulated the vestibular system and therefore led to less sensory conflict. As mentioned above, with NGBNI, the motion pattern perceived by sensory afferents is closer to the expected pattern. The navigation technique that we designed for NGBNI combines several gestures: walking in place for translations and arm gestures for rotations. However, past literature found walking in place not to be as effective as real walking, as it generates discomfort and physical fatigue [START_REF] Usoh | Walking > walking-in-place > flying, in virtual environments[END_REF]. In fact, though these methods are categorized as natural, their naturalness is questionable [START_REF] Norman | Natural user interfaces are not natural[END_REF] and users are required to learn the right gestures. One issue with natural gesture-based interfaces lies in gesture recognition algorithms that may not be robust enough and thus lead to wrong interaction commands. Then, despite their lack of naturalness, NGBNI may still appear more natural to navigate than DBNI for users who are not familiar with VR technologies, especially joystick-based interfaces, since natural interfaces imply less cognitive load [START_REF] Valli | The design of natural interaction[END_REF]. It may then induce less cybersickness, as past research found that users under high cognitive load usually report higher levels of sickness [START_REF] Pausch | A Literature Survey for Virtual Environments: Military Flight Simulator Visual Systems and Simulator Sickness[END_REF].

Taking into consideration users' profiles may be an interesting clue for selecting the right navigation paradigm and lowering cybersickness.

Conclusion and future work

Two experiments were performed to study the influence of two navigation parameters on cybersickness: distance from a virtual barrier and the interface used to navigate (natural gesturebased or device-based). We observed that: (i) navigating close to a virtual barrier seems to generate more cybersickness than being far away from it, (ii) using natural gesture-based interaction interfaces leads to less cybersickness than device-based interfaces.

From these results, several suggestions can be proposed to lower cybersickness. First, an automatic navigation speed controller can be derived to maintain low cybersickness levels and allow enhanced user experience. With such a controller, the navigation speed can be automatically adjusted depending on the situations subjects encounter. For instance, it can be calculated as a function of the distance from virtual obstacles. As an example, when subjects navigate close to a virtual barrier, speed decreases automatically; conversely, as they move away from the obstacle, speed increases progressively. Second, navigation systems could be smarter by adapting navigation to the users' profile. This would lead to customized virtual reality. These ideas will be explored in future studies. Some VR designers recommend setting constant navigation speeds, though past research showed that one possibility to alleviate cybersickness effects is to modify speed depending on the surrounding environment (Argelaguet, 2014), i.e., even though speed is not constant, cybersickness can be lowered, as long as the speed control is well designed.

In the first experiment, a joystick-based technique (DBNI) was solely used as we considered this technique to be much more widely employed in virtual reality than natural gesture-based techniques (NGBNI). However, it could be interesting to see whether NGBNI techniques could lead to results similar to those presented here. We keep this issue for a future study.

Here only translational movements were considered. Rotational movements should also be studied. However, a huge piece of work already exists on this topic, as it is well known that rotational motions are prone to sickness. Past research revealed that the effect of rotational velocity on cybersickness has a Gaussian distribution, with a mean located at 𝜔 = 60°. 𝑠 -1 [START_REF] Hu | Motion Sickness and Electrogastrographic Activity As a Function of Speed of Rotation of an Optokinetic Drum[END_REF]. Therefore angular velocities lower than 60°.s -1 should be selected, whatever the distance from a virtual obstacle.

We studied the effect of only two navigation parameters on cybersickness. However, many other factors should be taken into account such as the inter-pupillary distance, the system latency, the field of view, etc. These parameters cannot be studied all at once; however, we intend to focus on some of them, such as the inter-pupillary distance, to derive strategies to reduce cybersickness. We also believe that, as proposed above, taking into consideration the users' state and profile better, and thus their physiological characteristics, in real time can lead to enhanced user experience.

Fig. 1

 1 Fig. 1 Flystick and navigation functions assigned to each button.

  interprets these motions into gestures and streams them on VRPN. Translational movements (Fig. 2.a) are commanded and controlled by up/down movements of the feet by a walking-in-place (WIP) technique.

Fig. 2

 2 Fig. 2 Navigation based on natural gestures in the CAVE system: (a) walking forward, (b)

Fig. 3

 3 Fig. 3 Experimental design to study the effect of distance on cybersickness.

Fig. 4

 4 Fig. 4 Left: virtual environment displayed in the CAVE from the subject's viewpoint. Right:

Fig. 5

 5 Fig. 5 Difference of the postural sway's low and high frequency components for each path.

Fig. 6

 6 Fig. 6 Sickness scores for each path.

Fig. 7

 7 Fig. 7 Difference of the postural sway's low and high frequency components for DBNI and

Fig. 8

 8 Fig. 8 Sickness scores for DBNI and NGBNI.

Conference (VR 2006), pp. 97-102. doi: 10.1109/VR.2006.44.