Introduction

Fragile X syndrome (FXS) is a relatively common inherited cause of intellectual disability, with a prevalence of approximately 1 in 4,000 males and 1 in 8,000 females (Hersh & Saul, 2011). Males are typically more severely affected due to their single X chromosome. FXS is caused by an abnormal expansion of CGG trinucleotide repeats in the fragile X mental retardation 1 (FMR1) gene, which results in the transcriptional silencing of FMR1 gene and consequent reduction in the levels of the fragile X mental retardation protein (FMRP), an RNA-binding protein essential for synaptic plasticity and neuronal cytoarchitecture (Zalfa et al., 2006; Soden et al., 2010; Kute et al., 2019). There is currently no cure for FXS. Existing treatments are primarily symptomatic, targeting common manifestations such as social deficits, repetitive behaviors, and sleep disturbances (Budimirovic et al., 2022; Mendez & Mendez, 2024). Given the essential role of sleep and circadian rhythms in neural recovery and plasticity, a compelling question is whether improving sleep/wake cycles might alleviate other core symptoms of this disease.

Animal models are invaluable for elucidating the mechanisms underlying circadian and sleep disturbances as well as developing interventions aimed at restoring healthy sleep/wake cycles. Among the available models for FXS, the Fmr1 knockout (KO) mouse has been extensively validated for investigating disease mechanisms and is widely used in preclinical drug development, including for sleep-related outcomes (Thomas et al., 2012; Kazdoba et al., 2014; Kat et al., 2022; Saré et al., 2022; Martinez et al., 2024). Early studies have shown disrupted rhythmic activity in the light/dark (LD) cycle in the Fmr1/Fxr2 double KO and Fmr1 KO/Fxr2 heterozygous mice (Zhang et al., 2008), whilst more recent findings indicate impairments in both sleep (Saré et al., 2017) and activity rhythms (Bonasera et al., 2017; Angelakos et al., 2019) in Fmr1 KO mice, all consistent with the sleep/wake disturbances reported in FXS individuals (Kronk et al., 2009, 2010; Budimirovic et al., 2022).

Despite these findings, significant gaps remain in our understanding of the precise nature of the sleep and circadian deficits in Fmr1 KO mice, and whether targeted correction of these rhythms might mitigate behavioral symptoms. Furthermore, much of the behavioral research in Fmr1 KO mice has been conducted during the light phase—when mice are typically asleep—potentially confounding results due to sleep disruption.

In this study, we aimed at comprehensively characterizing sleep and circadian disturbances in the Fmr1 KO mice and examine their association with behavioral alterations. To avoid disrupting natural sleep patterns, we employed non-invasive home-cage monitoring systems to assess sleep and activity rhythms and conducted behavioral testing during the active (dark) phase. Importantly, the same animals were evaluated for both sleep/wake cycles and behavioral phenotypes, enabling direct correlations. Finally, because of the above mentioned evidence of circadian dysfunction, we tested the efficacy of a circadian-based intervention— scheduled feeding (6-hour feeding/18-hour fasting)—on improving sleep/wake rhythms, social behavior, and repetitive behaviors in these mutants.

Results

Fragmented Light-Phase Sleep in the Fmr1 KO Mice

To determine whether the Fmr1 KO exhibit deficits in immobility-defined sleep behavior, the animals were examined using a combination of video recording and mouse tracking system under a 12h:12h light/dark (LD) condition. Both the WT and mutant mice exhibited robust circadian rhythms in sleep with higher levels during the day and minimal sleep during the early dark phase (Fig. 1A). The amount of total sleep within the 24 hr cycle and the number of sleep bouts did not differ between genotypes, although the Fmr1 KO did exhibit a reduction in the average duration of each sleep bout (Table 1). A two-way ANOVA used to analyze the temporal pattern of sleep (1-hr bins) in WT and mutants revealed significant effects of time (F_{(23, 287)} = 31.94; P < 0.001) and genotype (F_{(23, 287)} = 11.95; P < 0.001), along with differences in day and night sleep parameters (Table 1). During the resting phase (lights on), the Fmr1 KO mutants slept significantly less (Fig. 1B) with more frequent sleep bouts (Fig. 1C) of shorter duration (Fig. 1D). The maximum sleep bout duration was about 20 minutes shorter in the mutant mice. The reduced sleep time, shorter bout length, and greater number of sleep bouts seen during the day are all evidence of a fragmented sleep pattern in the Fmr1 KO mutants. The negative findings should be viewed with caution as the sample size of n=6 per group is below ideal.

Fig. 1.

Table 1:

Disrupted diurnal and circadian activity rhythms in the Fmr1 KO Mice

Next, we investigated the presence of deficits in locomotor activity rhythms in the Fmr1 KO mice. Age- matched WT and Fmr1 KO were housed in cages equipped with running-wheels under 12h:12h LD cycles for 2 weeks and then released into constant darkness (DD) to measure their endogenous rhythms (Suppl. Fig. 1). Both genotypes exhibited robust daily and circadian rhythms in wheel running activity (Fig. 2A; Table 2). Some indication of the commonly reported hyper-activity in the Fmr1 KO mice (Sørensen et al., 2015) emerged in the LD cycle (Fig. 2B), although the activity levels over 24 hours did not differ significantly (Fig. 2C) between genotypes. Analysis of the diurnal activity patterns with two-way ANOVA confirmed significant effects of time (F_{(23, 287)} = 8.84; P = 0.003) and genotype (F_{(23, 287)} = 39.75; P < 0.001) on the locomotor activity. In LD (Fig. 2C), the mutants exhibited lower rhythm power with increased activity during lights-on and more variability in the activity onset. When released in DD (Fig. 2D), WT and mutants displayed very similar free-running period (tau), but again, the latter presented with weaker rhythm power and a higher cycle-to-cycle variability in activity onset (Table 2). Together, our data suggest that the Fmr1 KO mice exhibit deficits in the circadian regulation of locomotor activity and raise the possibility of impaired ability to synchronize to the light cycle.

Fig. 2:

Table 2:

Impaired Behavioral Response to Photic Cues in the Fmr1 KO Mice

The likelihood of the Fmr1 KO mice presenting deficits in their response to light was tested using four behavioral assays of the retinal input to the circadian system. First, the ability of exposure to light (4500K, 50lx) during the dark phase to suppress or “mask” the locomotor activity of the nocturnal animals was evaluated. For this assay, the level of locomotor activity between Zeitgeber Time (ZT) 14 and 15 (two hours after lights off) was first measured under baseline LD conditions (i.e. the day before), and no differences were observed between genotypes (WT: 2229 ± 162 revolutions per hour (rev/h); KO: 2639 ± 328 rev/h; t₍₁₈₎ = -1.123, P = 0.277). The next day the mice were exposed to light for 1 hour at this same phase and the levels of activity measured. The mutants exhibited a significantly reduced light-driven suppression of the locomotor activity as compared to WT, (Fig. 3A, B; Table 3), with significant effects of both genotype (F_{(1, 39)} = 12.002; P = 0.001, Two-way ANOVA) and light exposure (F_{(1, 39)} = 24.253; P < 0.001, Two-way ANOVA). Next, we determined the number of cycles needed by the mice to re-entrain to a 6-hours phase advance of the LD cycle. While the WT mice re-synchronized in 5.9 ± 0.6 days to the new phase, the Fmr1 KO mice required 11.3 ± 0.4 days to adjust to the new ZT 12 (Fig. 3C, D; Table 3). Third, we evaluated the ability of the mice to entrain to a skeleton photoperiod (SPP) in which the full 12 hours of light or dark are replaced by two 1-hour light exposures (ZT 0-1 & ZT 11-12) separated by 11 hours of dark (Fig. 4A). Mice were first entrained to the standard LD cycle and then released into the SPP for 2 weeks (Fig. 4A). The activity recordings showed that the WT stably entrained to this challenging environment and exhibited robust circadian rhythms with a period (tau) of 24.0 ± 0.0 hr (Table 3). Conversely, the Fmr1 KO exhibited a tau of 23.7 ± 0.2 hr with the shorter period driven by three mutants that failed to entrain to the SPP (Table 3). Compared to the WT, the mutants also showed reduced power of the rhythms, increased light-phase activity, and a larger onset variability (Fig. 4B). Finally, the direct light-induced phase shift of the circadian system of the mice was measured by exposing them to light (300 lx) for 15min while in constant darkness at circadian time (CT) 16. The WT showed a phase delay of 135.6 ± 26.9 min, whilst, in the Fmr1 KO mutants, the delay in the activity onset on the next day was about half the magnitude (64.0 ± 0.1 min; Fig. 4C, D; Table 3). These findings provide evidence for the presence of a defective circadian response to light in the Fmr1 KO mice.

Fig. 3:

Fig. 4:

Table 3.

Subtle deficits in the retinal afferent innervation to the SCN in the Fmr1 KO Mice

The results described above suggest that the Fmr1 KO mice present with an anomalous retinal input to the suprachiasmatic nucleus (SCN), the master circadian clock located in the anterior hypothalamus. Hence, to assess the integrity of this pathway, the WT and Fmr1 KO mice received a bilateral intravitreal injection of the fluorescence-conjugated neurotracer Cholera Toxin (β subunit), previously used to map the projections of the melanopsin-expressing intrinsically photoreceptive retinal ganglion cells (ipRGCs) from the retina to the SCN (Muscat et al., 2003; Hattar et al., 2006). At variance with the WT, a lower fluorescent signal could be observed in the mutant mice both laterally and medially to the ventral SCN (Fig. 5A), where the retino-hypothalamic fibers reach the nuclei, as well as beneath in the optic chiasm. Analysis of the intensity and distribution (Suppl. Fig. 2) of the labelled retino-hypothalamic processes reaching and entering the ventral SCN showed a reduction, particularly evident in the lateral side of both the left and right mutant SCN (Fig. 5B, C). Likewise, a subtle decrease in the intensity of the labelled fibers was found in the whole SCN (Table 4) of the Fmr1 KO mice as compared to WT.

Fig. 5:

Table 4.

A well-established test of the light input to the circadian system is the light-evoked cFos response in the SCN. In line with the results obtained with the Cholera Toxin suggesting an impaired light-pathway to the SCN in the mutants, the number of cFos positive cells in the SCN of the Fmr1 KO was greatly reduced (50%) in comparison to the WT mice (Fig. 5D, E; Table 4). Contrary to other models of neurodevelopmental disabilities (NDDs; Li et al., 2015; Lee et al., 2018), the Fmr1 KO mice did not display any histomorphometrical alteration of the SCN (Table 4). Based on these findings, we can surmise the presence of a compromised connectivity between the retina and the SCN in the mutants, which could provide, at least in part, a mechanism for their difficulty in responding to photic cues.

Social and Repetitive Behavior Deficits in Fmr1 KO Mice

Social deficits and stereotypic symptoms are hallmark problems in autism spectrum disorders, which we sought to evaluate during the active (dark) phase, between ZT 16-18, in the two genotypes. Social behavior was tested with the three-chamber and the five-trial social interaction tests (Fig. 6). In the first stage of the three- chambers test, both genotypes showed more interest toward the stranger mouse when given the choice between an inert object and a mouse. The direct comparison of the time spent with the object and the mouse did not show significant differences between the genotypes (Table 5). On the other hand, measurement of the social preference index (SPI) indicated that the Fmr1 KO mice exhibited a reduced interest in the conspecific compared to WT (Fig. 6A; Table 5). In the second testing stage, when the lifeless object was replaced with a second novel mouse and the first stranger became the familiar mouse (Fig. 6B), the mutants spent more time with the familiar mouse than the WT (Table 5). Again, the social novelty preference index (SNPI) confirmed that the Fmr1 KO exhibited reduced interest in novel mouse compared to WT mice (Fig. 6B; Table 5). The reduced time spent exploring the novel-mouse chamber suggest that the mutants were, perhaps, unable to distinguish the second novel mouse from the first, now familiar, mouse, along with decreased sociability. The possibility of impaired social recognition was further tested with the five-trial social test (Fig. 6C). In this test, the first stranger mouse becomes the familiar mouse after 4 exposures to the testing mouse. When the second mouse is introduced in the 5^th trial, the testing mouse typically shows a boosted interest in investigating the novel mouse. As expected, when the second stranger mouse was introduced to the testing animals in the 5^th trial, the WT showed elevated social behavior; whilst the mutants did not display increased interest in exploring the second novel mouse (Fig. 6C; Table 5). No genotypic differences in social interaction in the first four trials were observed, indicating that habituation was unaltered in the mutants (Suppl. Fig. 3).

Fig. 6:

Table 5.

Table 6.

Next, when the active-phase repetitive behavior was examined using the marble bury test, the Fmr1 KO spent more time digging and buried more marbles compared to WT (Fig. 6D, E; Table 5). When the 30-min trial was divided into three 10-min intervals, the repetitive digging behavior was significantly higher in the Fmr1 mutants compared with the WT in all three intervals. Two-way ANOVA demonstrated that there was a significant effect of genotype (F_{(1, 107)} = 11.04; P = 0.001) but not of the interval (F_{(2, 107)} = 0.42; P = 0.66). The Fmr1 KO mice also exhibited significantly more grooming behavior than their WT counterpart (Fig. 6F, Table 5).

Finally, we used these data to determine if disrupted sleep correlated with autistic-like behavioral deficits. Sleep duration (Fig. 6G, H; Table 6) exhibited a moderate to strong correlation with both social recognition and grooming time, while sleep fragmentation (measured by sleep bouts number) only correlated with the latter (Fig. 6J); the length of sleep bouts (Table 6) showed moderate correlation with both social recognition and repetitive behavior. In addition, a moderate correlation was seen between grooming time and the circadian parameters, rhythmic power and activity onset variability (Table 6). In short, our work suggests that even when tested during their circadian active phase, the Fmr1 KO mice exhibit robust repetitive and social behavioral deficits. Moreover, the shorter and more fragmented the daytime sleep, the more severe the behavioral impairment in the mutants.

Amelioration of the Behavioral Disruptions by Scheduled Feeding in the Fmr1 KO Mice

The correlations between fragmented sleep and the severity of other behavioral phenotypes support the possibility that interventions focused on improving circadian rhythms and sleep may benefit the observed deficits in the Fmr1 KO mice. Scheduled feeding can be a powerful regulator of circadian rhythms (Long and Satchidananda, 2022; Manoogian et al., 2022) and has been shown to be effective in several disease models (Whittaker et al., 2018; Wang et al., 2018; Gao et al., 2022; Gupta et al., 2022; Whittaker et al., 2023).To test whether circadian reinforcement could ameliorate the observed deficits in the mutants, a new cohort of WT and Fmr1 KO mice was subjected to a time-restricted feeding (TRF) protocol (6-h feeding/18-h fasting, Suppl. Fig. 1) for two weeks and compared to counterpart animals held on ad libitum feeding (ALF). Both genotypes were able to well adapt to the TRF regimen and ate as much as their ALF counterpart after 5 days; at the end of the two-weeks, the TRF groups weighed less than the ALF groups (Suppl. Fig. 4).

The TRF treatment benefited several aspects of the temporal patterning of activity and sleep (Fig. 7) in both genotypes. The scheduled feeding improved the power of the rhythms and markedly restored the cycle-to- cycle variability in the activity onset of the mutants to WT levels (Fig. 7C-E), while reducing fragmentation (less activity bouts) in both WT and KO (Table 7). Total cage activity was not altered by the intervention in either genotype. By the second week, TRF also improved sleep behavior in both genotypes (Fig. 7F, G), with the Fmr1 KO mice showing increased total daytime sleep (Fig. 7H; Table 7), reduced sleep fragmentation, i.e. fewer bouts (Fig. 7I) of longer bout durations (Fig. 7J) as compared to their ALF counterpart. In general, strong genotypic differences were present in the response to TRF with the WT mice showing a more robust sleep response (Table 7).

Fig. 7:

Table 7.

The benefits of TRF were also evident on other aberrant behaviors in the Fmr1 KO model, and after two weeks of treatment, this feeding regimen improved social memory recognition while reducing the inappropriate grooming behavior (Table 8). Scheduled feeding improved social memory in the KO mice, by restoring the preference for the novel over the familiar mice to WT-like level (Fig. 8A; Table 8), suggesting that, perhaps, the treated mutants were able to distinguish the novel mouse from the familiar mouse. Grooming behavior was also significantly reduced by TRF in the Fmr1 KO mice, which showed a significant reduction in the time spent self- grooming (Fig. 8B), without affecting their locomotor activity (Fig. 8C). These data suggest that TRF has ameliorating effects on sleep/wake rhythms as well as autistic-like behaviors in the Fmr1 KO model.

Fig. 8:

Table 8.

Attenuation of the Elevated Levels of Pro-inflammatory Markers in the Fmr1 KO Mice by TRF

An abnormal immune response has been suggested to play a role in FXS pathophysiology (Reynolds et al., 2021; Dias et al., 2023; Robinson-Agramonte et al., 2022). Hence, blood was collected at the end of the two weeks of the scheduled feeding regimen to investigate whether TRF would impact the profile of pro-inflammatory markers in the Fmr1 KO mice. The mutants on ALF displayed elevated levels of several cytokine in comparison with their WT counterpart (Table 9), in particular, Interleukin-12, (IL-12), Interferon-gamma (IFN_Ƴ), and the Chemokine Ligand-9 (CXCL-9). These genotypic differences were abolished in the TRF-treated groups (Fig. 9A). Scheduled feeding also repristinated the Fmr1 KO levels of IL-2 to WT levels. Furthermore, elevated IL-12 and IFN-γ levels were significantly associated with markers of poor sleep, such as higher activity in the light phase, shorter sleep time/sleep bout length, and higher numbers of sleep bouts (Table 10), as well as with social memory deficits and excessive grooming (Fig. 9B, C; Table 10). These data suggest that TRF-dampening of the pro-inflammatory signaling in Fmr1 KO mice provides a potential mechanism to explain, at in least part, the observed behavioral improvements.

Fig. 9:

Table 9.

Table 10.

Discussion

Sleep and circadian rhythm disruptions are prevalent in individuals with NDDs, including FXS. Common issues include difficulty initiating and maintaining sleep and irregular sleep patterns, which significantly impair the quality of life of FXS individuals and their families/caretakers (Budimirovic et al., 2022; Minhas et al., 2024).

Notably, individuals exhibiting more severe behavioral symptoms often experience more pronounced sleep problems (Schreck et al., 2004; Taylor et al., 2012; Kaufmann et al., 2024), suggesting that circadian and sleep dysfunction exacerbate core NDD features. These disturbances are detectable quite early, even in infants and toddlers carrying the FMR1 mutation (D’Souza et al., 2020), raising the possibility that disrupted sleep/wake cycles may be an initial and influential feature of disease expression. Mechanistically, such disruptions could stem from direct effects of FMR1 loss on sleep and circadian circuits, or indirectly from secondary symptoms such as anxiety, sensory hypersensitivity, and repetitive behaviors.

Animal models of FXS, such as the Fmr1 KO mouse, are essential for dissecting the underlying mechanisms and testing interventions (Sandoval et al., 2024). In the present study, we demonstrate that loss of Fmr1 results in disrupted sleep and circadian rhythms, including higher fragmentation of the rest-phase sleep and abnormal diurnal activity patterns. These disruptions were associated with deficits in social memory and increased repetitive behaviors. Importantly, we found that a circadian-based intervention (TRF) not only improved rhythmicity and sleep consolidation but also ameliorated behavioral deficits and reduced peripheral inflammatory markers.

Circadian and Sleep Disturbances in Fmr1 KO Mice

Our behavioral sleep assay revealed reduced sleep duration and increased fragmentation during the light/rest phase in Fmr1 KO mice (Fig. 1), consistent with a previous report by Saré and colleagues (2017). In contrast, Westmark et al. (2023), using EEG, did not observe baseline sleep differences between genotypes, possibly reflecting methodological differences or subtle phenotypic variability. A key gap in the field is the lack of studies probing sleep homeostasis in Fmr1 KO mice; future studies using sleep deprivation protocols may reveal underlying vulnerabilities. Notably, the Fmr1 KO mice exhibit neural oscillation abnormalities, including increased resting gamma power and enhanced auditory-evoked responses, paralleling the EEG signatures in FXS patients (Jonak et al., 2024).

Assessment of circadian locomotor activity revealed weakened rhythmicity, elevated daytime activity, and a greater onset variability in Fmr1 KO mice (Fig. 2), consistent with prior studies (Bonasera et al., 2017; Angelakos et al., 2019). These deficits persisted in constant darkness (DD), indicating a compromised endogenous circadian clock. Differences in activity rhythms measured in LD conditions could be driven by the circadian clock or the direct effects of light and dark. Although earlier studies using Fmr1/Fxr2 double mutants reported severe rhythm loss (Zhang et al., 2008), the present study demonstrates that the loss of Fmr1 alone is sufficient to impair circadian regulation.

Impaired Light Response and SCN Connectivity in the Fmr1 KO Mice

To explore the basis of the observed dysregulations, we evaluated the photic input to the circadian system. Fmr1 KO mice displayed impaired negative masking, delayed re-entrainment to phase shifts, blunted light- induced phase shifts, and poor entrainment to a skeleton photoperiod (Figs. 3–4). These behavioral deficits suggest a compromised signal transmission from the retina to the SCN via the ipRGCs. Such hypothesis was confirmed by anatomical and functional assays; indeed, the ipRGC innervation of the SCN was lessened in the Fmr1 KO mice (Fig. 5A), and the light-evoked c-Fos induction substantially reduced (Fig. 5B). Given that the FMRP is expressed in the ipRGCs (Zhang et al., 2020), its loss may underline the deficient light response observed in the mutants. Interestingly, this reduced sensitivity to circadian light input contrasts with the general sensory hypersensitivity typical of FXS, highlighting a unique dissociation in sensory processing pathways (Rais et al., 2018). Whether these circadian-specific photic impairments are present in FXS patients warrants further investigation.

Despite these light input abnormalities, the Fmr1 KO mice did not exhibit a shortened free-running period (tau) in DD (Fig. 2D; Angelakos et al., 2019), nor were the rhythmic expressions of the clock genes altered (Zhang et al., 2008). These findings suggest that the core molecular clock remains intact, and the circadian dysfunction may arise from an altered excitatory/inhibitory (E/I) balance or impaired network synchronization within the SCN (Olde Engberink et al., 2023). Hence, our present results reinforce that loss of Fmr1 impairs the circadian system at the level of input and output regulation rather than at the core molecular oscillation.

Sleep and Behavior Correlate in the Fmr1 KO Mice

The Fmr1 KO model reproduces core behavioral deficits of FXS, including impaired social recognition and increased repetitive behaviors (Melancia & Trezza, 2018; Kat et al., 2022). By analyzing sleep, circadian activity, and behavior in the same animals, we identified significant correlations: mice with more disrupted sleep and weaker circadian rhythms exhibited more severe social and repetitive behavior impairments (Fig. 6). These findings parallel clinical studies showing that disrupted sleep predicts next-day behavioral difficulties in FXS and autism spectrum disorders (Cohen et al., 2018; Robinson-Shelton & Malow, 2016; Schwichtenberg et al., 2022; Minhas et al., 2024). Although correlational, these results suggest that targeting circadian and sleep dysfunction may offer a novel route for ameliorating some behavioral phenotypes present in certain NDDs.

Time-Restricted Feeding as a Therapeutic Strategy

Given the link between circadian disruption and behavior, we tested whether restoring rhythmicity could improve outcomes. TRF, a circadian-aligned feeding schedule, significantly improved the power of the rhythms, reduced cycle-to-cycle (onset) variability, and consolidated daytime sleep (Fig. 7). Importantly, TRF reduced repetitive behavior and improved social memory and interactions in the Fmr1 KO mice (Fig. 8), establishing proof-of-principle that circadian-based behavioral interventions can ameliorate core phenotypes of FXS.

Ketogenic diets have previously been shown to improve both sleep and seizure control in the Fmr1 KO mice, albeit with limited effects on other behaviors (Westmark et al., 2020, 2023, 2024). TRF, by contrast, appears to directly impact both behavioral and physiological endpoints, potentially through its entraining effects on peripheral and central clocks. Prior studies have shown that TRF improves behavior in aging and animal models of neurodegenerative disorders (Wang et al., 2018; Whittaker et al., 2018, 2023), but this is the first report to demonstrate benefits in a model of NDDs.

TRF Reduces Inflammatory Signatures in Fmr1 KO Mice

An emerging literature suggests that immune dysregulation accompanies FXS pathology (Ashwood et al., 2010; Careaga et al., 2014; Bertrand et al., 2021). In the present study, TRF reduced the abnormal levels of IL- 12 and IFN-γ in the Fmr1 KO mice (Fig. 9A; Table 9), and the increased levels of these cytokines correlated with both sleep disturbances and behavioral impairments (Fig. 9B, C; Table 10). While most cytokines did not differ between genotypes, the normalization of IL-12 and IFN-γ levels following TRF supports a mechanistic link between inflammation and behavioral symptoms in FXS. These results are consistent with prior evidence implicating IL-12 in social and repetitive behavior in autism spectrum disorders (Ashwood et al., 2011; Fallah et al., 2020) and suggest that circadian-based interventions may exert part of their benefit by dampening low- grade inflammation.

Whether TRF exerts direct anti-inflammatory effects in the central nervous system remains unknown. Future studies should assess microglia activation, blood–brain barrier integrity, and central cytokine levels following TRF in the Fmr1 KO and related models. Moreover, defining the causal role of specific cytokines such as IL-12 and IFN-γ in behavioral regulation may offer novel therapeutic targets for FXS and related disorders.

Methods

Animals and Time-restricted feeding (TRF) paradigm

All experimental procedures were approved by the UCLA Animal Research Committee and conformed to guidelines from the UCLA Division of Laboratory Animal Medicine (DLAM) and the National Institutes of Health (NIH). Wild-type (WT) (JAX ID: 000664) and Fmr1 KO mice (Fmr1^tm4Cgr) on the C57BL/6J background (JAX ID: 003025) were acquired from the Jackson laboratory (Bar Harbor, ME). These mutants have a neomycin resistance cassette replacing exon 5 of the fragile X mental retardation syndrome 1 (Fmr1) gene. The studies were carried out in male adult mice (3 to 5 months old) singly housed in light-tight ventilated cabinets in temperature- and humidity-controlled conditions, with ad libitum access to food and water unless otherwise stated. Cages were equipped with either running wheel or infrared motion sensors to monitor the sleep/wake behavior.

TRF was conducted as previously described (Wang et al 2018; Whittaker et al 2018; 2023). Briefly, WT and Fmr1 KO mice were singly housed and entrained to a 12h:12h light/dark (LD) cycle (300 lx vs 0 lx respectively) for a minimum of 2 weeks, then randomly assigned to one of two feeding conditions: food (standard chow) available ad libitum (ad lib) or available for 6 hr during the middle of the active phase from Zeitgeber Time (ZT) 15 to ZT 21. By definition ZT0 and ZT12 are, respectively, the onsets of lights turning on and off. Scheduled feeding was achieved by manually adding and removing food from the mouse cages, and a careful examination was carried out to ensure no small food fragments were dropped and remained in the cages.

Food consumption was measured by weighing food at the beginning and the end of the feeding cycles (ALF: 24 hr vs TRF: 6 hr). The ALF and TRF mice groups were held in these conditions for at least two weeks (14 days) plus 3 additional days to measure immobility-based sleep behavior (Suppl. Fig. 1).

This study used two different cohorts of mice for a total of 135 mice. The experiments shown in Fig. 1 through 6 were designed to compare the circadian phenotype of the Fmr1 KO mice to the WT and were carried out between 2017-2020 before the Covid pandemic research shutdown. After the restart of research, new cohorts of mice were obtained from Jackson Laboratory, and used to test the effects of scheduled feeding on the mutants’ behavioral and circadian deficits (Fig. 7 through 9).

Immobility-Based Sleep Behavior

Sleep Behavior was assessed under a 12h:12h LD cycle by video recording in combination with an automated mouse tracking analysis software system (Anymaze, Stoelting Co., Wood Dale, IL) as previously described (Lee et al., 2018; Whittaker et al., 2018). Sleep was defined as the periods when 95% of the animal remained immobile for at least 40 seconds or longer, a threshold shown to correlate with >99% accuracy to EEG-defined sleep (Fisher et al., 2016). For each animal, continuous recordings were performed over 5 days and data from two consecutive days with the fewest artifact-free epochs were averaged and used for further analysis. Data were exported in 1-min bins and analyzed separately for the light and dark phases. Total sleep time was determined by summing the immobility durations in the rest phase (ZT 0-12) or active phase (ZT 12-24). Sleep fragmentation was determined by the number of sleep bouts, which were operationally defined as episodes of continuous immobility with a sleep count greater than 3 per minute, persisting for at least 60 secs.

Locomotor cage activity rhythms

Methods used to characterize the sleep/wake cycles have been previously described (Lee et al., 2018; Wang et al., 2018; Whittaker et al., 2018). Single housed WT and the Fmr1 KO mice were habituated to a 12h:12h LD cycle for two weeks to ensure entrainment to the appointed LD schedule before data collection.

For the phenotypical comparisons between WT and Fmr1 KO, cage activity was recorded with running wheel sensors for at least 14 days in LD, then, after the assessment of sleep behavior (3 days), the animals were released for two additional weeks into constant darkness (12h:12h dark-dark, DD) to obtain measures of the endogenous rhythms (free-running activity, Suppl. Fig. 1). To investigate the effects of TRF, cage activity rhythms were monitored using overhead passive infrared (IR) motion sensors for two weeks (Wang et al., 2018; Whittaker et al., 2018).

Activity data were collected via the VitalView data recording system (Mini Mitter, Bend, OR) and ten days of recordings were used to obtain the period (tau), the rhythmic power as well as the waveform presentations. The analysis was conducted using the El Temps (A. Diez-Nogura, Barcelona, Spain) and ClockLab (Actimetrics, Lafayette Instruments, Lafayette, IN) programs as previously described (Lee et al 2018; Wang et al., 2018; Whittaker et al., 2018). The periodogram generated by the El Temps uses χ² test with a threshold of 0.001 significance, from these the amplitude of the periodicities is calculated at the circadian harmonic to obtain the power of the rhythmicity. The rhythmic power, or percentage of variation (%V), provides a measure of the strength of the mouse’s periodicity corrected for the activity amount and normalized to the percentage of variance derived from peak significance (P=0.05). The amount of cage activity over a 24hr period was averaged over 10 days and reported here as the arbitrary units (a.u.)/h. Fragmentation and imprecision of the daily onset of sleep/wake cycles were determined using ClockLab. Fragmentation was determined by the number of activity bouts per day, with one bout counted when activity was separated by a gap of 21 min or more (maximum gap: 21 min; threshold 3 counts/min). Imprecision, a measure of cycle-to- cycle variability or onset variability, was determined by calculating the daily variability in the time of activity onset from a best-fit regression line drawn through 10 days of activity in both LD and DD conditions using the ClockLab program (Actimetrics, Wilmette, IL, United States).

Photic Regulation of Circadian Behavior

Cohorts of age-matched WT and Fmr1 KO mice were single housed in cages with running wheels under a 12h:12h LD cycle (300 lx, 4500K) for two weeks and, after stable entrainment, exposed to four behavioral assays to test the photic regulation of their circadian system.

1. Photic suppression on nocturnal activity (negative light masking): mice were exposed to 1 hr of white light (300 lx, 4500K) at ZT 14 to determine their response to light exposure during the dark period (masking of nocturnal activity). The animals’ activity level, measured as the number of wheel revolutions, during the light pulse was compared to the level at the same hour (ZT 14 to 15) on the day before the exposure. Data are reported as percent of suppression= % [(rev during the 1-hr light exposure) – (rev during the dark baseline)]/(rev during the dark baseline).

2. Re-entrainment to a 6hr advance of the LD cycle: the mice underwent a 6-hr phase advance. The activity levels were continuously monitored by the wheel-running system and the activity onset determined with VitalView. The ability of the animals to re-entrain was quantified by the difference between the new ZT 12 (lights-off) and the onset of the running activity for each subsequent recording day. A mouse was considered fully re-entrained when the activity onset aligned to the new lights-off time for the consecutive 5 days.

3. Skeleton Photoperiod (SPP): the mice were transferred from the 12h:12h LD cycle to an SPP consisting of 1hL:11hD:1L:11hD cycles. Mice were exposed to the 1-hr light (300 lx) at the beginning (ZT 0) and the end (ZT 11) of the original light phase. The mice were kept in this photic condition for at least 2 weeks and activity was recorded and analyzed to determine rhythm power, period (tau) and other basic activity rhythm parameters using ClockLab.

4. Light-induced phase shift of activity rhythms: Mice were released into DD for 10 days. The circadian time (CT) of their free-running activity rhythms was determined using VitalView and El Temps software. The time of activity onset under DD was defined as CT 12. On day 11, the mice were exposed to light (300 lx, 4500K) at CT 16 for 15 min, and after the light pulse, they were held in DD for an additional 10 days. The best- fit lines of the activity onsets of sleep/wake cycles before and after light exposure were measured and compared.

Behavioral Assays

All these tests were conducted under dim red light (<2 lx) during the dark (active) phase between 2 and 4 h after lights- off (ZT 14–16) to avoid sleep disruptions after the sleep and activity recordings had been completed (Suppl. Fig. 1). Animals were habituated to the testing environment for at least 30 minutes prior to the behavioral assays.

The Stereotypic Behavior was assessed using two tests:

1. The marble burying test was used to evaluate repetitive digging behavior (Yrigollen et al., 2019). An array of 4 by 6 marbles was placed in the testing arena over a layer of 4-cm deep shavings. The testing mouse was introduced to the arena from the corner and its behavior recorded for 30 mins. After that, the testing mouse was carefully removed from the arena with special precaution to not move the marbles and returned to the home cages. The number of marbles buried more than ⅔ of their area was counted, and these were cleaned with 70% ethanol before the next use. Time spent on digging was manually scored by an observer masked to the experimental groups, whilst the distance travelled was derived from the automatic mouse- tracking system (Anymaze software, Stoelting Co., Wood Dale, IL).

2. The grooming test was conducted as in our previous work (Wang et al 2020). The mouse was introduced to the testing arena and its behavior recorded by a camcorder for 30 mins. The time spent on self- grooming, defined as the cleaning, licking, or washing of the limbs, tail, and body surface areas, typically from a head to tail direction, but excluding bouts of scratching, was manually scored by an observer masked to the experimental groups. The distance travelled was derived from the automatic mouse-tracking system (Anymaze software).

Social Behavior was assessed using two tests:

1. The three-chamber test was performed as previously described (Wang et al 2020). The testing mice were allowed to freely explore an arena with three chambers where the central chamber remained empty. When being habituated to the three-chamber arena, both the WT and the mutants explored the arena evenly with no preference toward the left or the right chamber (left/right ratio: WT: 0.95 ± 0.14; P = 0.43 by paired t- test. Fmr1 KO: 0.82 ± 0.1; P = 0.085 by paired t-test). The three-chamber test consisted of two parts: the first stage assessed the preference of social approach toward the stranger mouse, and the second stage assessed the ability of social discrimination of the testing mouse. In the first stage (social approach), an up-turned metal- grid pencil cup was placed in the side chambers: one was left empty as the novel object (the object chamber), while a never-met stranger mouse matched by sex, age, and genotype with the testing mouse was placed in the second up-turned cup (the social chamber). Thus, the testing mouse was tested for its preference between the object chamber and the social chamber in this first testing stage. In the second stage (social discrimination), the first stranger mouse and the cup remained the same, while a second stranger mouse matched by sex, age, and genotype with the testing mouse was placed in the second up-turned cup. In other words, the social chamber in the first stage became a familiar chamber in the second stage, and the object chamber of the first stage a novel chamber in the second stage. Thus, the preference between the familiar chamber and the novel chamber of the testing mouse was assessed in this second stage. Time spent in each chamber and the distance travelled were derived from the automatic mouse-tracking system (Anymaze software).

2. The 5-trial social test was conducted as previously described (Mineur et al., 2006). The testing mouse was first habituated to the arena for 30 mins and then introduced to a never-met stranger mouse for 4 trials. The testing mouse was allowed to explore and interact with the stranger mouse for 2 mins in each trial, then it was introduced to a second stranger mouse during the 5th trial for 2 mins. The resting interval between trials was 5 mins. Active social behaviors such as physical contacts (e.g. crawling over, social grooming), nose-to-nose sniffing, nose-to-anus from the testing mouse to the stranger mice were scored manually. Testing mice as well as stranger mice that showed aggressive behavior were withdrawn from the experiment.

Retinal-SCN Connectivity: Injection, Visualization and Analyses of the Neuroanatomical tracer Cholera Toxin

WT and Fmr1 KO mice (4 months-old) received a bilateral injection of CholeraToxin (β subunit) conjugated to Alexa Fluor™ 555 Conjugate (catalog number: C34776; Invitrogen™, Carlsbad, CA). Prior to the injections, the animals received a drop of a local ophthalmic anesthetic (Proparacaine HCl, 0.5%, Sandoz, Holzkirchen Germany) and an intraperitoneal (i.p.) injection of a non-steroidal anti-inflammatory drug (Carprofen, Zoetis, Parsippany-Troy Hills, NJ). The mice were then anesthetized with isoflurane and a 30G needle (BD PrecisionGlide^TM Needle; Becton Dickinson, Franklin Lakes NJ) was inserted at a 45° angle into the sclera and the vitreous chamber, towards the base of the retina to allow leakage of vitreous humor. The Cholera Toxin (2 μg in 2μl of sterile PBS) was injected into the vitreous chamber using a 32G Hamilton syringe (Hamilton, Reno NV). The needle was left in place for ten seconds before being retracted. Seventy-two hours after the injection, the mice were euthanized with isoflurane (30%–32%) and transcardially perfused with phosphate-buffered saline (PBS, 0.1 M, pH 7.4) containing 4% (w/v) paraformaldehyde (PFA, Sigma). The brains were rapidly dissected out, post-fixed overnight in 4% PFA at 4°C, and cryoprotected in 15% sucrose.

Sequential coronal sections (40-50 μm) containing the left and right SCN were mounted, and the cover-slips applied with a drop of Vectashield-containing DAPI (4’,6-diamidino-2-phenylinodole; catalog number: H-1200; Vector Laboratories, Burlingame, CA). Sections were visualized on a Zeiss AxioImager M2 microscope equipped with an AxioCam MRm and the ApoTome imaging system, and images acquired with the Zeiss Zen software and a 10x objective to include both left and right SCN. Two methods of analyses were carried out on the images of 5 consecutive sections per animal containing the middle SCN (Lee et al., 2018). First, the relative intensity of the Cholera Toxin fluorescent processes was quantified in the whole SCN, both left and right separately, by scanning densitometry using the Fiji image processing package of the NIH ImageJ software (https://imagej.net). A single ROI of fixed size (575.99 μm x 399.9 μm, width x height) was used to measure the relative integrated density (mean gray values x area of the ROI) in all the images. The values from the left and right SCN were averaged per section and 5 sections per animal were averaged to obtain one value per animal. Second, the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the nuclei, hence, the distribution of the Cholera Toxin fluorescent signal was also obtained for each left and right ventral SCN separately in the same 4-5 consecutive sections per animal using the Profile Plot Analysis feature of ImageJ (Lee et al., 2018). Briefly, a rectangular box of fixed size (415.38μm x 110.94 μm, width x height, Suppl. Fig. 2) to include the ventral part of the SCN was set for each side, and a column plot profile was generated whereby the x-axis represents the horizontal distance through the SCN (lateral to medial for the left and medial to lateral for the right; Suppl. Fig. 2) and the y-axis represents the average pixel intensity per vertical line within the rectangular box. Subsequent processing of the resulting profiles was performed for left and right SCN images separately. To average the profiles of the 5 sections and obtain a single curve per animal, fifth-order polynomial curves were fit to best estimate the position of the intensity peak on the x-axis and, using this position, the original y-axis values were aligned and averaged arithmetically [1 profile per section (either left or right), 5 sections per animal]. Analyses were performed by two observers masked to the genotype of the animals. Data are shown as the average profile ± SD of 3 animals per genotype.

Photic induction of cFos in the SCN and cFos-positive Cell Counting

A separate cohort of male WT and Fmr1 KO mice (3–4 months-old) was housed in DD conditions and exposed to light (300 lx, 4500K, 15 min) at CT 16. Forty-five mins later, the mice were euthanized with isoflurane (30%–32%) and transcardially perfused with phosphate-buffered saline (PBS, 0.1 M, pH 7.4) containing 4% (w/v) PFA. The brains were rapidly dissected out, post-fixed overnight in 4% PFA at 4°C, and cryoprotected in 15% sucrose until further processing. Sequential coronal sections (40-50 μm), containing the middle SCN, were collected on a cryostat (Leica, Buffalo Grove, IL) and further processed for cFos immunofluorescence as previously described (Wang et al., 2020; 2023; Longcore et al., 2024). Briefly, free- floating coronal sections, paired on the rostral-caudal axis and containing both the left and right SCN, were blocked for 1 h at room temperature (1% Bovine Serum Albumin, 0.3% Triton X-100, 10% normal donkey serum in 1xPBS) and then incubated overnight at 4°C with a rabbit polyclonal antiserum against cFos (1:1000, RRID: AB_2247211; Cell Signaling) followed by a Cy3-conjugated donkey-anti-rabbit secondary antibody (Jackson ImmunoResearch Laboratories, Bar Harbor, ME). Sections were mounted and coverslips applied with Vectashield mounting medium containing DAPI, and visualized on a Zeiss AxioImager M2 microscope (Zeiss, Thornwood NY) equipped with a motorized stage, an AxioCam MRm and the ApoTome imaging system. Z- Stack Images (35 images; 34mm, 1.029mm interval) of both the left and right middle SCN were acquired with a 20X objective using the Zeiss Zen digital imaging software, and two observers masked to the experimental groups performed the cell counting. The boundaries of the SCN were visualized using the DAPI nuclear staining and the cells immuno-positive for cFos counted with the aid of the Zen software tool ‘marker’ in three to five consecutive sections. The numbers obtained from the left and right SCN were averaged to obtain one value per section, and those from three-five sections averaged to obtain one value per animal and are presented as the mean ± standard deviation (SD) of 4 animals per genotype.

Histomorphometrical analyses of the SCN

Photographs of DAPI-stained sections generated from the WT and Fmr1 KO mice as described above were used to estimate the area, the perimeter, height and width of the SCN as previously reported (Li et al., 2015; Lee et al., 2018). For each animal, the four measurements were performed in three consecutive sections containing the middle SCN and acquired with a 10X objective and the Zen software. Measurements (in μm) of both the left and right SCN were obtained with the auxilium of the AxioVision software (Zeiss, Pleasanton, CA, USA). Because the borders of the DAPI-defined SCN are somewhat arbitrary, measurements were performed independently by two observers masked to the genotype of the animals. The area of the SCN in the three sections was summed, whilst the perimeter, height and width were averaged to obtain one value per side. No significant differences were found between the left and right SCN, therefore the values of the left and right SCN were averaged to obtain one value per animal. Data are shown as the mean ± standard deviation (SD) of 6 animals per genotype.

Blood sampling and measurements of plasma immune molecules

Blood was collected (∼0.5 mL per animal) in the beginning of the light phase via cheek puncture into microvette tubes coated with EDTA (Sarstedt, Numbrecht, Germany; Suppl. Fig. 1). Tubes were gently inverted a few times and placed immediately on wet ice. Within 1 h following collection, samples were centrifuged at 2500 rpm for 15 min at 4° C and the plasma collected into prelabeled Eppendorf tubes (Fisher Scientific, Hampton, NH), and immediately stored at -80° C until further processing using the Luminex Multiplex Assay at the UCLA Immune Assessment Core (https://www.uclahealth.org/pathology/services-immunoassays).

Statistical Analyses

Data analyses were performed using SigmaPlot 14.5 (Grafiti LLC, Palo Alto, CA) or Prism 10 (GraphPad Software, La Jolla, CA). The impact of the loss of Fmr1 on the waveforms of sleep/wake cycles was analyzed using repeated measures two-way analysis of variance (ANOVA) with time and genotype factors. While a two-way ANOVA with genotype and treatment as factors was used for the differences in sleep bouts between light phase and dark phase. The Holm-Sidak’s multiple comparisons test was applied to determine significant differences among the groups. The effect of TRF on the activity waveforms was analyzed using a three-way ANOVA with genotype, feeding regimen, and time as factors followed by Holm-Sidak’s multiple comparisons test. The datasets were examined for normality (Shapiro–Wilk test) and equal variance (Brown–Forsythe test); genotypic differences in the behavioral tests were determined by Student t-test or the Mann-Whitney test. Correlations between circadian/sleep parameters and other behaviors were examined by applying the Pearson correlation analysis. Normal distribution of the histomorphological datasets and the relative intensity of the Cholera Toxin fluorescent processes in the whole SCN were assessed using the Shapiro-Wilk test, and since these did not pass the normality test, a two-tailed Mann-Whitney test was employed to identify significant differences between groups. The effect of the loss of FMRP on the photic induction of cFos cells was assessed by one-way ANOVA followed by Bonferroni’s multiple comparisons test. Values are reported as the mean ± standard error of the mean (SEM) or mean ± standard deviation (SD).

Differences were determined significant if P < 0.05.

Data availability

Any data will be available upon request. The raw data will be also deposited in Dryad, University of California Curation Center (UC3), California Digital Library.

Additional files

Supplementary Figures