High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains

Sleep and Activity Patterns in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD.

Methods

We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data.

Results

This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives.

Conclusions

The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Circadian clock in choroid plexus is resistant to immune challenge but dampens in response to chronodisruption

The choroid plexus (ChP) in the brain ventricles has a major influence on brain homeostasis. In this study, we aimed to determine whether the circadian clock located in ChP is affected by chronodisruption caused by misalignment with the external light/dark cycle and/or inflammation. Adult mPer2Luc mice were maintained in the LD12:12 cycle or exposed to one of two models of chronic chronodisruption - constant light for 22-25 weeks (cLL) or 6-hour phase advances of the LD12:12 cycle repeated weekly for 12 weeks (cLD-shifts). Locomotor activity was monitored before the 4th ventricle ChP and suprachiasmatic nuclei (SCN) explants were recorded in real time for PER2-driven population and single-cell bioluminescence rhythms. In addition, plasma immune marker concentrations and gene expression in ChP, prefrontal cortex, hippocampus and cerebellum were analyzed. cLL dampened the SCN clock but did not shorten the inactivity interval (sleep). cLD-shifts had no effect on the SCN clock, but transiently affected sleep duration and fragmentation. Both chronodisruption protocols dampened the ChP clock. Although immune markers were elevated in plasma and hippocampus, levels in ChP were unaffected, and unlike the liver clock, the ChP clock was resistant to lipopolysaccharide treatment. Importantly, both chronodisruption protocols reduced glucocorticoid signaling in ChP. The data demonstrate the high resistance of the ChP clock to inflammation, highlighting its role in protecting the brain from neuroinflammation, and on the other hand its high sensitivity to chronodisruption. Our results provide a novel link between human lifestyle-induced chronodisruption and the impairment of ChP-dependent brain homeostasis.

Behavioral phenotyping based on physical inactivity can predict sleep in female rats before, during, and after sleep disruption

BACKGROUND

A noninvasive method that can accurately quantify sleep before, during, and after sleep disruption (SD) has not been validated in female rats across their estrous cycle. In female rats, we hypothesized that the duration of physical inactivity (PIA) required to predict sleep would 1) change with the differences in baseline sleep between the circadian and estrous cycle phases and 2) predict sleep and the change in sleep (Δsleep) before, during, and after SD independent of circadian and estrous cycle phase.

NEW METHODS

EEG, EMG, physical activity and estrous cycle phase were measured in female Sprague-Dawley rats before, during, and after SD. Sleep was determined by two methods [EEG/EMG and a duration of continuous PIA (i.e., PIA criterion)]. Reliability between the methods was tested with a previously validated criterion (40 s). Sensitivity analyses and criterion-related validity analyses for sleep during SD and recovery were conducted across multiple PIA criteria (10 s-120 s). Predictability between the two methods and Δsleep was calculated.

RESULTS/COMPARISON WITH EXISTING METHODS

Three criteria (10 s, 20 s, 30 s) predicted baseline sleep independent of circadian and estrous cycle phase. Sleep during SD and recovery were predicted by two criteria (30 s and 10 s). Δsleep between study periods was not reliably predicted by a single PIA criterion.

CONCLUSION

PIA predicted sleep independent of estrous cycle phase in female rats. However, the specific criterion was dependent upon the study period (before, during, and after SD) and circadian phase. Thus, prior work validating a PIA criterion in male rodents is not applicable to the female rat.

Genome-wide association in Drosophila identifies a role for Piezo and Proc-R in sleep latency

Sleep latency, the amount of time that it takes an individual to fall asleep, is a key indicator of sleep need. Sleep latency varies considerably both among and within species and is heritable, but lacks a comprehensive description of its underlying genetic network. Here we conduct a genome-wide association study of sleep latency. Using previously collected sleep and activity data on a wild-derived population of flies, we calculate sleep latency, confirming significant, heritable genetic variation for this complex trait. We identify 520 polymorphisms in 248 genes contributing to variability in sleep latency. Tests of mutations in 23 candidate genes and additional putative pan-neuronal knockdown of 9 of them implicated CG44153, Piezo, Proc-R and Rbp6 in sleep latency. Two large-effect mutations in the genes Proc-R and Piezo were further confirmed via genetic rescue. This work greatly enhances our understanding of the genetic factors that influence variation in sleep latency.

Clinicopathologic Dissociation: Robust Lafora Body Accumulation in Malin KO Mice Without Observable Changes in Home-cage Behavior

Lafora Disease (LD) is a syndrome of progressive myoclonic epilepsy and cumulative neurocognitive deterioration caused by recessively inherited genetic lesions of EPM2A (laforin) or NHLRC1 (malin). Neuropsychiatric symptomatology in LD is thought to be directly downstream of neuronal and astrocytic polyglucosan aggregates, termed Lafora bodies (LBs), which faithfully accumulate in an age-dependent manner in all mouse models of LD. In this study, we applied home-cage monitoring to examine the extent of neurobehavioral deterioration in a model of malin-deficient LD, as a means to identify robust preclinical endpoints that may guide the selection of novel genetic treatments. At 6 weeks, ~6-7 months and ~12 months of age, malin deficient mice ("KO") and wild type (WT) littermates underwent a standardized home-cage behavioral assessment designed to non-obtrusively appraise features of rest/arousal, consumptive behaviors, risk aversion and voluntary wheel-running. At all timepoints, and over a range of metrics that we report transparently, WT and KO mice were essentially indistinguishable. In contrast, within WT mice compared across timepoints, we identified age-related nocturnal hypoactivity, diminished sucrose preference and reduced wheel-running. Neuropathological examinations in subsets of the same mice revealed expected age dependent LB accumulation, gliosis and microglial activation in cortical and subcortical brain regions. At 12 months of age, despite the burden of neocortical LBs, we did not identify spontaneous seizures during an electroencephalographic (EEG) survey, and KO and WT mice exhibited similar spectral EEG features. Using an in vitro assay of neocortical function, paroxysmal increases in network activity (UP states) in KO slices were more prolonged at 3 and 6 months of age, but were similar to WT at 12 months. KO mice displayed a distinct response to pentylenetetrazole, with a greater incidence of clonic seizures and a more pronounced post-ictal suppression of movement, feeding and drinking behavior. Together, these results highlight a stark clinicopathologic dissociation in a mouse model of LD, where LBs accrue substantially without clinically meaningful changes in overall wellbeing. Our findings allude to a delay between LB accumulation and neurobehavioral decline: one that may provide a window for treatment, and whose precise duration may be difficult to ascertain within the typical lifespan of a laboratory mouse.

What have we learned about sleep from selective breeding strategies?

Selective breeding is a classic technique that enables an experimenter to modify a heritable target trait as desired. Direct selective breeding for extreme sleep and circadian phenotypes in flies successfully alters these behaviors, and sleep and circadian perturbations emerge as correlated responses to selection for other traits in mice, rats, and dogs. The application of sequencing technologies to the process of selective breeding identifies the genetic network impacting the selected trait in a holistic way. Breeding techniques preserve the extreme phenotypes generated during selective breeding, generating community resources for further functional testing. Selective breeding is thus a unique strategy that can explore the phenotypic limits of sleep and circadian behavior, discover correlated responses of traits having shared genetic architecture with the target trait, identify naturally-occurring genomic variants and gene expression changes that affect trait variability, and pinpoint genes with conserved roles.

Life-long dietary restrictions have negligible or damaging effects on late-life cognitive performance: A key role for genetics in outcomes

Several studies report that caloric restriction (CR) or intermittent fasting (IF) can improve cognition, while others report limited or no cognitive benefits. Here, we compare the effects of 20% CR, 40% CR, 1-day IF, and 2-day IF feeding paradigms to ad libitum controls on Y-maze working memory (WM) and contextual fear memory (CFM) in a large population of Diversity Outbred mice that model the genetic diversity of humans. While CR and IF interventions improve lifespan, we observed no enhancement of working memory or CFM in mice on these feeding paradigms, and report 40% CR to be damaging to recall of CFM. Using Quantitative Trait Loci mapping, we identified the gene Slc16a7 to be associated with CFM outcomes in aged mice on lifespan promoting feeding paradigms. Limited utility of dieting and fasting on memory in mice that recapitulate genetic diversity in the human population highlights the need for anti-aging therapeutics that promote cognitive function, with the neuronal monocarboxylate transporter MCT2 encoded by Slc16a7 highlighted as novel target.

Home-cage behavior in the Stargazer mutant mouse

In many childhood-onset genetic epilepsies, seizures are accompanied by neurobehavioral impairments and motor disability. In the Stargazer mutant mouse, genetic disruptions of Cacng2 result in absence-like spike-wave seizures, cerebellar gait ataxia and vestibular dysfunction, which limit traditional approaches to behavioral phenotyping. Here, we combine videotracking and instrumented home-cage monitoring to resolve the neurobehavioral facets of the murine Stargazer syndrome. We find that despite their gait ataxia, stargazer mutants display horizontal hyperactivity and variable rates of repetitive circling behavior. While feeding rhythms, circadian or ultradian oscillations in activity are unchanged, mutants exhibit fragmented bouts of behaviorally defined "sleep", atypical licking dynamics and lowered sucrose preference. Mutants also display an attenuated response to visual and auditory home-cage perturbations, together with profound reductions in voluntary wheel-running. Our results reveal that the seizures and ataxia of Stargazer mutants occur in the context of a more pervasive behavioral syndrome with elements of encephalopathy, repetitive behavior and anhedonia. These findings expand our understanding of the function of Cacng2.

Synaptic dysfunction connects autism spectrum disorder and sleep disturbances: A perspective from studies in model organisms

Sleep disturbances (SD) accompany many neurodevelopmental disorders, suggesting SD is a transdiagnostic process that can account for behavioral deficits and influence underlying neuropathogenesis. Autism Spectrum Disorder (ASD) comprises a complex set of neurodevelopmental conditions characterized by challenges in social interaction, communication, and restricted, repetitive behaviors. Diagnosis of ASD is based primarily on behavioral criteria, and there are no drugs that target core symptoms. Among the co-occurring conditions associated with ASD, SD are one of the most prevalent. SD often arises before the onset of other ASD symptoms. Sleep interventions improve not only sleep but also daytime behaviors in children with ASD. Here, we examine sleep phenotypes in multiple model systems relevant to ASD, e.g., mice, zebrafish, fruit flies and worms. Given the functions of sleep in promoting brain connectivity, neural plasticity, emotional regulation and social behavior, all of which are of critical importance in ASD pathogenesis, we propose that synaptic dysfunction is a major mechanism that connects ASD and SD. Common molecular targets in this interplay that are involved in synaptic function might be a novel avenue for therapy of individuals with ASD experiencing SD. Such therapy would be expected to improve not only sleep but also other ASD symptoms.

High-throughput visual assessment of sleep stages in mice using machine learning

STUDY OBJECTIVES

Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice.

METHODS

We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods.

RESULTS

Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration.

CONCLUSIONS

We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

The dihydropyrimidine dehydrogenase gene contributes to heritable differences in sleep in mice

Many aspects of sleep are heritable, but only a few sleep-regulating genes have been reported. Here, we leverage mouse models to identify and confirm a previously unreported gene affecting sleep duration-dihydropyrimidine dehydrogenase (Dpyd). Using activity patterns to quantify sleep in 325 Diversity Outbred (DO) mice-a population with high genetic and phenotypic heterogeneity-a linkage peak for total sleep in the active lights off period was identified on chromosome 3 (LOD score = 7.14). Mice with the PWK/PhJ ancestral haplotype at this location demonstrated markedly reduced sleep. Among the genes within the linkage region, available RNA sequencing data in an independent sample of DO mice supported a highly significant expression quantitative trait locus for Dpyd, wherein reduced expression was associated with the PWK/PhJ allele. Validation studies were performed using activity monitoring and EEG/EMG recording in Collaborative Cross mouse strains with and without the PWK/PhJ haplotype at this location, as well as EEG and EMG recording of sleep and wake in Dpyd knockout mice and wild-type littermate controls. Mice lacking Dpyd had 78.4 min less sleep during the lights-off period than wild-type mice (p = 0.007; Cohen's d = -0.94). There was no difference in other measured behaviors in knockout mice, including assays evaluating cognitive-, social-, and affective-disorder-related behaviors. Dpyd encodes the rate-limiting enzyme in the metabolic pathway that catabolizes uracil and thymidine to β-alanine, an inhibitory neurotransmitter. Thus, data support β-alanine as a neurotransmitter that promotes sleep in mice.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .

Compared Heritability of Chronotype Instruments in a Single Population Sample

It is well established that the oldest chronotype questionnaire, the morningness-eveningness questionnaire (MEQ), has significant heritability, and several associations have been reported between MEQ score and polymorphisms in candidate clock genes, a number of them reproducibly across populations. By contrast, there are no reports of heritability and genetic associations for the Munich chronotype questionnaire (MCTQ). Recent genome-wide association studies (GWAS) from large cohorts have reported multiple associations with chronotype as assessed by a single self-evaluation question. We have taken advantage of the availability of data from all these instruments from a single sample of 597 participants from the Brazilian Baependi Heart Study. The family-based design of the cohort allowed us to calculate the heritability (h²) for these measures. Heritability values for the best-fitted models were 0.37 for MEQ, 0.32 for MCTQ, and 0.28 for single-question chronotype (MEQ Question 19). We also calculated the heritability for the two major factors recently derived from MEQ, “Dissipation of sleep pressure” (0.32) and “Build-up of sleep pressure” (0.28). This first heritability comparison of the major chronotype instruments in current use provides the first quantification of the genetic component of MCTQ score, supporting its future use in genetic analysis. Our findings also suggest that the single chronotype question that has been used for large GWAS analyses captures a larger proportion of the dimensions of chronotype than previously thought.

High-throughput measurement of fibroblast rhythms reveals genetic heritability of circadian phenotypes in diversity outbred mice and their founder strains

Circadian variability is driven by genetics and Diversity Outbred (DO) mice is a powerful tool for examining the genetics of complex traits because their high genetic and phenotypic diversity compared to conventional mouse crosses. The DO population combines the genetic diversity of eight founder strains including five common inbred and three wild-derived strains. In DO mice and their founders, we established a high-throughput system to measure cellular rhythms using in vitro preparations of skin fibroblasts. Among the founders, we observed strong heritability for rhythm period, robustness, phase and amplitude. We also found significant sex and strain differences for these rhythms. Extreme differences in period for molecular and behavioral rhythms were found between the inbred A/J strain and the wild-derived CAST/EiJ strain, where A/J had the longest period and CAST/EiJ had the shortest. In addition, we measured cellular rhythms in 329 DO mice, which displayed far greater phenotypic variability than the founders—80% of founders compared to only 25% of DO mice had periods of ~ 24 h. Collectively, our findings demonstrate that genetic diversity contributes to phenotypic variability in circadian rhythms, and high-throughput characterization of fibroblast rhythms in DO mice is a tractable system for examining the genetics of circadian traits.

Identification of Genes Contributing to a Long Circadian Period in Drosophila Melanogaster

The endogenous circadian period of animals and humans is typically very close to 24 h. Individuals with much longer circadian periods have been observed, however, and in the case of humans, these deviations have health implications. Previously, we observed a line of Drosophila with a very long average period of 31.3 h for locomotor activity behavior. Preliminary mapping indicated that the long period did not map to known canonical clock genes but instead mapped to multiple chromosomes. Using RNA-Seq, we surveyed the whole transcriptome of fly heads from this line across time and compared it with a wild-type control. A three-way generalized linear model revealed that approximately two-thirds of the genes were expressed differentially among the two genotypes, while only one quarter of the genes varied across time. Using these results, we applied algorithms to search for genes that oscillated over 24 h, identifying genes not previously known to cycle. We identified 166 differentially expressed genes that overlapped with a previous Genome-wide Association Study (GWAS) of circadian behavior, strongly implicating them in the long-period phenotype. We tested mutations in 45 of these genes for their effect on the circadian period. Mutations in Alk, alph, CG10089, CG42540, CG6034, Kairos (CG6123), CG8768, klg, Lar, sick, and tinc had significant effects on the circadian period, with seven of these mutations increasing the circadian period of locomotor activity behavior. Genetic rescue of mutant Kairos restored the circadian period to wild-type levels, suggesting it has a critical role in determining period length in constant darkness.

Natural selection on sleep duration in Drosophila melanogaster

Sleep is ubiquitous across animal species, but why it persists is not well understood. Here we observe natural selection act on Drosophila sleep by relaxing bi-directional artificial selection for extreme sleep duration for 62 generations. When artificial selection was suspended, sleep increased in populations previously selected for short sleep. Likewise, sleep decreased in populations previously selected for long sleep when artificial selection was relaxed. We measured the corresponding changes in the allele frequencies of genomic variants responding to artificial selection. The allele frequencies of these variants reversed course in response to relaxed selection, and for short sleepers, the changes exceeded allele frequency changes that would be expected under random genetic drift. These observations suggest that the variants are causal polymorphisms for sleep duration responding to natural selection pressure. These polymorphisms may therefore pinpoint the most important regions of the genome maintaining variation in sleep duration.

Noninvasive three-state sleep-wake staging in mice using electric field sensors

STUDY OBJECTIVE

Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors.

METHODS

Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested.

RESULTS

EF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG.

CONCLUSIONS

EF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.

Recent advances in understanding the genetics of sleep

Sleep is a ubiquitous and complex behavior in both its manifestation and regulation. Despite its essential role in maintaining optimal performance, health, and well-being, the genetic mechanisms underlying sleep remain poorly understood. Here, we review the forward genetic approaches undertaken in the last four years to elucidate the genes and gene pathways affecting sleep and its regulation. Despite an increasing number of studies and mining large databases, a coherent picture on “sleep” genes has yet to emerge. We highlight the results achieved by using unbiased genetic screens mainly in humans, mice, and fruit flies with an emphasis on normal sleep and make reference to lessons learned from the circadian field.