Bruce F. O’Hara

Gene Expression in the Brain across the Hibernation Cycle

The purpose of this study was to characterize changes in gene expression in the brain of a seasonal hibernator, the golden-mantled ground squirrel, Spermophilus lateralis, during the hibernation season. Very little information is available on molecular changes that correlate with hibernation state, and what has been done focused mainly on seasonal changes in peripheral tissues. We produced over 4000 reverse transcription-PCR products from euthermic and hibernating brain and compared them using differential display. Twenty-nine of the most promising were examined by Northern analysis. Although some small differences were observed across hibernation states, none of the 29 had significant changes. However, a more direct approach, investigating expression of putative hibernation-responsive genes by Northern analysis, revealed an increase in expression of transcription factors c-fos, junB, and c-Jun, but not junD, commencing during late torpor and peaking during the arousal phase of individual hibernation bouts. In contrast, prostaglandin D2 synthase declined during late torpor and arousal but returned to a high level on return to euthermia. Other genes that have putative roles in mammalian sleep or specific brain functions, including somatostatin, enkephalin, growth-associated protein 43, glutamate acid decarboxylases 65/67, histidine decarboxylase, and a sleep-related transcript SD464 did not change significantly during individual hibernation bouts. We also observed no decline in total RNA or total mRNA during torpor; such a decline had been previously hypothesized. Therefore, it appears that the dramatic changes in body temperature and other physiological variables that accompany hibernation involve only modest reprogramming of gene expression or steady-state mRNA levels.

Resolvins AT-D1 and E1 differentially impact functional outcome, post-traumatic sleep, and microglial activation following diffuse brain injury in the mouse.

Traumatic brain injury (TBI) is induced by mechanical forces which initiate a cascade of secondary injury processes, including inflammation. Therapies which resolve the inflammatory response may promote neural repair without exacerbating the primary injury. Specific derivatives of omega-3 fatty acids loosely grouped as specialized pro-resolving lipid mediators (SPMs) and termed resolvins promote the active resolution of inflammation. In the current study, we investigate the effect of two resolvin molecules, RvE1 and AT-RvD1, on post-traumatic sleep and functional outcome following diffuse TBI through modulation of the inflammatory response. Adult, male C57BL/6 mice were injured using a midline fluid percussion injury (mFPI) model (6-10min righting reflex time for brain-injured mice). Experimental groups included mFPI administered RvE1 (100ng daily), AT-RvD1 (100ng daily), or vehicle (sterile saline) and counterbalanced with uninjured sham mice. Resolvins or saline were administered daily for seven consecutive days beginning 3days prior to TBI to evaluate proof-of-principle to improve outcome. Immediately following diffuse TBI, post-traumatic sleep was recorded for 24h post-injury. For days 1-7 post-injury, motor outcome was assessed by rotarod. Cognitive function was measured at 6days post-injury using novel object recognition (NOR). At 7days post-injury, microglial activation was quantified using immunohistochemistry for Iba-1. In the diffuse brain-injured mouse, AT-RvD1 treatment, but not RvE1, mitigated motor and cognitive deficits. RvE1 treatment significantly increased post-traumatic sleep in brain-injured mice compared to all other groups. RvE1 treated mice displayed a higher proportion of ramified microglia and lower proportion of activated rod microglia in the cortex compared to saline or AT-RvD1 treated brain-injured mice. Thus, RvE1 treatment modulated post-traumatic sleep and the inflammatory response to TBI, albeit independently of improvement in motor and cognitive outcome as seen in AT-RvD1-treated mice. This suggests AT-RvD1 may impart functional benefit through mechanisms other than resolution of inflammation alone.

Increased fragmentation of sleep–wake cycles in the 5XFAD mouse model of Alzheimer’s disease

Sleep perturbations including fragmented sleep with frequent night-time awakenings and daytime naps are common in patients with Alzheimer’s disease (AD), and these daily disruptions are a major factor for institutionalization. The objective of this study was to investigate if sleep-wake patterns are altered in 5XFAD mice, a well-characterized double transgenic mouse model of AD which exhibits an early onset of robust AD pathology and memory deficits. These mice have five distinct human mutations in two genes, the amyloid precursor protein (APP) and Presenilin1 (PS1) engineered into two transgenes driven by a neuron specific promoter (Thy1), and thus develop severe amyloid deposition by 4 months of age. Age matched (4–6.5 months old) male and female 5XFAD mice were monitored and compared to wild-type littermate controls for multiple sleep traits using a non-invasive, high throughput, automated piezoelectric system which detects breathing and gross body movements to characterize sleep and wake. Sleep-wake patterns were recorded continuously under baseline conditions (undisturbed) for 3 days and after sleep deprivation of 4 hours, which in mice produces a significant sleep debt and challenge to sleep homeostasis. Under baseline conditions, 5XFAD mice exhibited shorter bout lengths (14% lower values for males and 26% for females) as compared to controls (p<0.001). In females, the 5XFAD mice also showed 12% less total sleep than WT (p<0.01). Bout length reductions were greater during the night (the active phase for mice) than during the day, which does not model the human condition of disrupted sleep at night (the inactive period). However, the overall decrease in bout length suggests increased fragmentation and disruption in sleep consolidation that may be relevant to human sleep. The 5XFAD mice may serve as a useful model for testing therapeutic strategies to improve sleep consolidation in AD patients.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

BACKGROUND Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW METHOD Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. RESULTS Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING METHODS Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. CONCLUSIONS This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Light Pulses Do Not Induce C-Fos or Per1 in the SCN of Hamsters That Fail to Reentrain to the Photocycle

Circadian activity rhythms of most Siberian hamsters (Phodopus sungorus sungorus) fail to reentrain to a 5-h phase shift of the light-dark (LD) cycle. Instead, their rhythms free-run at periods close to 25 h despite the continued presence of the LD cycle. This lack of behavioral reentrainment necessarily means that molecular oscillators in the master circadian pacemaker, the SCN, were unable to reentrain as well. The authors tested the hypothesis that a phase shift of the LD cycle rendered the SCN incapable of responding to photic input. Animals were exposed to a 5-h phase delay of the photocycle, and activity rhythms were monitored until a lack of reentrainment was confirmed. Hamsters were then housed in constant darkness for 24 h and administered a 30-min light pulse 2 circadian hours after activity onset. Brains were then removed, and tissue sections containing the SCN were processed for in situ hybridization. Sections were probed with Siberian hamster c-fosand per1mRNA probes because light rapidly induces these 2 genes in the SCN during subjective night but not at other circadian phases. Light pulses induced robust expression of both genes in all animals that reentrained to theLDcycle, but no expression was observed in any animal that failed to reentrain. None of the animals exhibited an intermediate response. This finding is the first report of acute shift in a photocycle eliminating photosensitivity in the SCN and suggests that a specific pattern of light exposure may desensitize the SCN to subsequent photic input.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearmans rho 0.43-0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

Experimental and computational analysis of mouse sleep-wake dynamics

Materials and methods In this study we explore the utility of a noninvasive method based on the signal from a piezoelectric sensor on the cage floor for scoring sleep-wake behavior in mice. It was previously demonstrated that the piezo signal can accurately discriminate sleep from wake activity; however, this was verified mostly by visual observation. Here we perform a more objective validation by correlating piezo measurements with EMG activity, which is dramatically suppressed during sleep. Furthermore, the piezo sensor is sensitive to respiration-related thoracic movements. Since breathing is relatively irregular in REM sleep compared to non-REM, we extract piezo features that reflect breathing regularity to try to distinguish between these sleep states.