Abnormal electrophysiological phenotypes and sleep deficits in a mouse model of Angelman Syndrome

All animals were housed in a temperature-controlled vivarium maintained on a 12:12-h light–dark cycle. All procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of California Davis and were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All experiments were performed on B6.129S7-Ube3a^tm1Alb/J (Ube3a) mice obtained from The Jackson Laboratory (Stock number 016590; Bar Harbor, ME, USA) and housed in a 24-h light–dark cycle (7 a.m.–7 p.m.), temperature controlled room and fed a standard diet of Teklad global 18% protein rodent diets (Envigo, Hayward, CA, USA). To maintain the colony, Ube3a^m+/p− male mice were paired with C57BL/6J wildtype females resulting in paternal transmission of the mutant allele that is silenced due to imprinting and litters with normal Ube3a expression. To create mice with maternal transmission of the mutant allele, Ube3a^m+/p+ (WT) male mice are paired with Ube3a^m−/p+ females resulting in Ube3a-del mice and their WT littermate controls. To identify mice, pups were labeled by paw tattoo on postnatal day 2–3 using non-toxic animal tattoo ink (Ketchum Manufacturing Inc., Brockville, ON, Canada). At postnatal day 2–7, tails of pups were clipped (1–2 mm) for genotyping, following the UC Davis IACUC policy regarding tissue collection. Genotyping was performed with REDExtract-N-Amp (Sigma-Aldrich, St. Louis, MO, USA) using primers Wildtype Forward: TCA ATG ATA GGG AGA TAA AAC A, Common: GAA AAC ACT AAC ATG GAG CTC and Mutant Forward CTT GTG TAG CGC CAA GTG C.

Three cohorts of mice were used in this study. All subjects were bred in our facility with a deletion of Ube3a (Ube3a-del) inherited from the maternal allele resulting in Ube3a^m−/p+ mice generated from breeding Ube3a^m+/p− females with Ube3a^m+/p+ males, termed Ube3a-del hereafter. Cohort 1 consisted of 20 WT and 8 Ube3a-del mice (5 Ube3a-del/9 WT males and 3 Ube3a-del/11 WT females) that were observed for 30 min before and after an administration of pentylenetetrazole (PTZ; 80 mg/kg; i.p.) for behavioral seizure characterization (SKU: P6500, Sigma-Aldrich, St. Louis, MO, USA). EEG data were acquired in Cohorts 2 and 3 where animals were anesthetized and implanted with a wireless telemetry device designed to measure electroencephalogram (EEG) and electromyogram (EMG) in freely moving animals (Data Science International, New Brighton, MN). Cohort 2 consisted of 7 Ube3a-del animals (3 males, 4 females) and 3 WT (2 males, 3 females) that were recorded for 24 h before administration of a lethal dose of pentylenetetrazole (80 mg/kg; i.p.) to observe EEG before and after seizure induction. Cohort 3 consisted of 7 Ube3a-del mice (4 males, 3 females) and 5 WT (4 males, 1 female) that were recorded for 72 h to collect sleep data. All animals were between 8 and 12 weeks old, and experimenters were blind to genotype. Subjects were implanted with the EEG device 7 days prior to data acquisition then, on day 8, began testing. All EEG recordings were collected in the subject animal’s home cage in a temperature-controlled testing room maintained on a 12:12-h light–dark cycle. All animals were littermates and singly housed after EEG implantation to avoid possible device displacement due to cage-mate interactions.

Seizure induction studies were conducted using 80 mg/kg pentylenetetrazole delivered intraperitoneally. Before administration, subjects were observed for 30 min and weighed to determine the appropriate solution volume. For those that were previously implanted, the weight of the implant (4.0 g) was subtracted from their total weight. Dosing was conducted in the morning (10:00–11:00) in a dim (~ 30 lx) holding room. Directly after administration of the convulsant, subjects were placed in a clean, empty cage where subsequent seizure stages were live-scored for 30 min. Seizure stages were scored using latencies to (1) first jerk/Straub’s tail, (2) loss of righting, (3) generalized clonic-tonic seizure, and (4) death. First jerk/Straub’s tail, previously described by Straub et al. (1911), was identified as a tonic dorsal extension of the tail usually accompanied by a jerk or jump of the animal’s entire body. Loss of righting was defined by the absence of both fore- and hindlimb paws from the surface of the cage bottom for > 1 s. Generalized clonic-tonic seizures were identified as loss of righting followed by phases of rigidity and forelimb/hindlimb spasms. Time to each stage was taken in seconds and analyzed across genotype.

Wireless EEG transmitters were implanted in anesthetized test animals using continuous isoflurane (2–4%). A 2–3 cm midline incision was made over the skull and trapezius muscles and then expanded to expose the subcutaneous space. Implants were placed in the subcutaneous pocket lateral to the spine to avoid discomfort of the animal and displacement due to movement. Attached to the implant were 4 biopotential leads made of a nickel-cobalt-based alloy insulated in medical-grade silicone, making up two channels that included a signal and reference lead. These leads were threaded toward the cranial part of the incisions for EEG and EMG placement. The periosteum was cleaned from the skull using a sterile cotton-tip applicator and scalpel; then, two 1-mm-diameter burr holes were drilled (1.0 mm anterior and 1.0 mm lateral; − 3.0 mm posterior and 1.0 mm lateral) relative to bregma. This lead placement allowed for measurement of EEG activity across the frontal cortical area. Steel surgical screws were placed in the burr holes, and the biopotential leads were attached by removing the end of the silicone covering and tying the lead to its respective screw. Once in place, the skull screws and lead connections were secured using dental cement. For EMG lead placement, the trapezius muscles of the animal were exposed, and each lead was looped through and sutured to prevent displacement. Finally, the incision was sutured using non-resorbable suture material and the animals were placed in a heated recovery cage where they received carprofen (5 mg/kg; i.p.) directly after surgery and 24 h post-surgery as an analgesic. Subjects were individually caged with ad libitum access to food and water for 1 week before EEG acquisition and monitored daily to ensure proper incision healing and recovery. Each implantation surgery took < 45 min, and no fatalities were observed.

After a 1-week recovery from surgical implantation, individually housed mice were assigned to PhysioTel RPC receiver plates that transmitted data from the EEG implants to a computer via the data exchange matrix using Ponemah software (Data Sciences International, St. Paul, MN). EEG and EMG data were collected at a sampling rate of 500 Hz with a 0.1 Hz high-pass and 100 Hz low-pass bandpass filter. Activity, temperature and signal strength were collected at a sampling rate of 200 Hz. Data acquired in Ponemah was read into Python and further processed with a bandpass filter from 0 to 50 Hz to focus on our frequencies of interest.

Seizure susceptibility was evaluated in Cohort 1 by latency to (1) first myoclonic jerk and/or Straub’s tail, (2) loss of righting reflex, (3) generalized clonic-tonic seizure and (4) death after administration of PTZ (80 mg/kg; i.p.). A reduction in latency indicated susceptibility, while an increase in latency indicated resistance. Ube3a-del animals exhibited seizure susceptibility via reduced latency to first jerk and generalized clonic-tonic seizure (Fig. 1a, b: t₍₂₆₎ = 2.287, p = 0.031; t₍₂₆₎ = 2.627, p = 0.014), compared to WT. No differences were detected in loss of righting reflex or death (Additional file 1: Fig. 1a, b: t₍₂₆₎ = 1.461, p = 0.156; t₍₂₆₎ = 1.333, p = 0.194). Hyperexcitability and seizure susceptibility were further analyzed by implanting Cohort 2 subjects with a wireless telemetric device that captured continuous EEG (Fig. 1c, d). Epileptiform activity, such as spiking, is sufficient in the detection and diagnosis of epilepsy. As expected, Ube3a-del mice in both Cohort 2 and Cohort 3 exhibited increased spiking activity during baseline recording compared to WT controls (Fig. 1g, h: t₍₁₀₎ = 3.435, p = 0.006; t₍₈₎ = 3.132, p = 0.014).

Rhythmic delta activity is a consistent spectral signature of AS [37, 38] and has been previously reported in mouse models [31, 32], but not via a single-channel electrode placed in the skull via a wireless telemetric system. To test whether our methodology was able to capture EEG signal sensitivity that would also detect increased delta activity, we evaluated spectral power dynamics of WT and Ube3a-del mice. When comparing power spectral densities across 0–50 Hz frequencies, Ube3a-del mice displayed robust elevated delta power (Fig. 2a: F_{(1, 20)} = 6.432, p = 0.020; t₍₂₀₎ = 2.763, p = 0.012 for delta). When densities were binned into delta, theta, alpha, beta and gamma frequency groups, delta power in Ube3a-del mice was significantly higher than WT littermate controls (Fig. 2c: F_{(1, 20)} = 5.862, p = 0.025; t₍₂₀₎ = 2.763, p = 0.012 for delta).

Delta-specific dynamics were further examined by looking at total delta, relative delta and total power across the entire recording. Ube3a-del mice exhibited higher total and relative delta power compared to WT across the entire recording (Fig. 2d, e: t₍₂₀₎ = 2.763, p = 0.012; t₍₂₀₎ = 4.089, p = 0.0001). Interestingly, total power was also higher in Ube3a-del animals, likely a result of the robust increase in delta (Fig. 2f: t₍₂₀₎ = 2.564, p = 0.019).

As delta activity is elevated in sleep stages, particularly slow-wave sleep, delta activity was also analyzed across light–dark cycle to control for the possible influence of sleep–wake cycles. When looking at the light cycle, elevated delta activity was detected across all 0–50 Hz power spectral densities (Fig. 3a: F_{(1, 20)} = 8.263, p = 0.009; t₍₂₀₎ = 3.020, p = 0.007 for delta). Furthermore, both relative and total delta power were significantly higher in Ube3a-del animals as well as total power compared to WT (Fig. 3b–d: t₍₂₀₎ = 3.042, p = 0.006; t₍₂₀₎ = 2.278, p = 0.034; t₍₂₀₎ = 2.989, p = 0.007). Similarly, in the dark cycle, Ube3a-del mice displayed elevated delta activity (Fig. 3e: t₍₂₀₎ = 2.178, p = 0.042 for delta), with trends toward significantly increased PSDs across the 0–50 Hz frequency bins (Fig. 3e: F_{(1, 20)} = 4.027, p = 0.059). Unsurprisingly, higher total and relative delta power was observed in Ube3a-del animals; however, no change in total power was detected (Fig. 3f–h: t₍₂₀₎ = 2.191, p = 0.041; t₍₂₀₎ = 2.823, p = 0.011; t₍₂₀₎ = 1.60, p = 0.125).

Sleep data were acquired in Cohort 3 animals and subsequently parsed into 4 distinct sleep–wake stages: active wake, wake, slow-wave sleep, and paradoxical sleep. A wake state was characterized by a low-amplitude, high-frequency signal with low-EMG tone, while an active wake state was distinguished by high-EMG tone. Sleep was divided into either a slow-wave sleep state or a paradoxical sleep state. Slow-wave sleep was defined by having a high-amplitude, low-frequency signal with elevated delta power and low-EMG tone, while paradoxical sleep had a low-amplitude, low-frequency signal with elevated theta power and low-EMG tone. Ube3a-del mice displayed reduced mean time in paradoxical sleep (Fig. 4b: t₍₁₀₎ = 2.91, p = 0.016) and took longer to reach paradoxical sleep stages compared to WT (Fig. 4d: t₍₁₀₎ = 3.691, p = 0.004). Furthermore, trends toward reduced mean time in slow-wave sleep were detected in Ube3a-del mice (Fig. 4d: t₍₁₀₎ = 1.931, p = 0.0823), contributing to a significantly reduced total sleep time compared to WT littermate controls (Fig. 4e: t₍₁₀₎ = 2.741, p = 0.021).

Sleep parameters were further analyzed by light–dark cycles. The first 24-h time period was sectioned into 2-h time bins starting at 12:00 a.m. (0–2 time of day) where counts of each sleep stage were scored as well as the percent time of a given sleep stage. Both wake and active wake stages were combined for awake analysis. A trend toward increased percent awake time was detected in Ube3a-del mice compared to WT littermate controls (Fig. 5a: F_(1,10) = 3.173, p = 0.105) that was consistent across both the light and dark cycles (Fig. 5b: F_{(1, 6)} = 4.105, p = 0.089). No change in awake counts was observed across time bins or light–dark cycles between genotypes (Fig. 5c: F_(1,10) = 0.465, p = 0.511; Fig. 5d: F_(1,6) = 465, p = 0.521). Interestingly, Ube3a-del animals had reduced percent paradoxical sleep time across time bins (Fig. 5e: F_(1,10) = 12.72, p = 0.005) that was observed for both the light and dark cycle compared to WT littermate controls (Fig. 5f: F_(1,6) = 28.02, p = 0.002; WT and Ube3a-del by light cycle p = 0.009; WT and Ube3a-del by dark cycle p = 0.014). Expectedly, Ube3a-del mice showed decreased paradoxical sleep counts across time bins (Fig. 5g: F_(1,10) = 12.71, p = 0.005) that was reduced in both light and dark cycles compared to WT (Fig. 5h: Genotype effect F_(1,6) = 28.01, p = 0.002; WT and Ube3a-del by light cycle p = 0.009; WT and Ube3a-del by dark cycle p = 0.014). Finally, the percent time in slow-wave sleep was not significantly different between genotypes (Fig. 5i: F_(1,10) = 0.399, p = 0.542), or across light–dark cycle (Fig. 5j: F_(1,6) = 1.310, p = 0.296). No significant differences in slow-wave sleep counts were detected across both time bins (Fig. 5k: F_(1,10) = 0.397, p = 0.543) and between light–dark cycles (Fig. 5l: Genotype effect F_(1,6) = 1.302, p = 0.297).

We wanted to identify, validate and quantify sleep spindles in the Ube3a-del mouse, adding another clinically relevant functional phenotype for therapeutic testing. First, a 10–15 Hz bandpass filter was first applied, followed by a Butterworth filter (3 Hz first stopband, 10 Hz first passband, 15 Hz second passband, 22 Hz second stopband, 24 dB attenuation level) for the frequency bands of interest. Next, the root-mean square (RMS) of the filtered signal was calculated with a 750-ms window to smooth the EEG trace before cubing the entire signal to amplify the signal–noise ratio. To detect spindles, a lower threshold (1.2 × mean-cubed RMS) was used to determine the start and end of a spindle, while an upper threshold (3.5 × mean-cubed RMS) was used to identify the peak of a spindle. Ube3a-del mice exhibited less sleep spindles total compared to WT littermate controls (Fig. 6a: t₍₁₀₎ = 2.357, p = 0.04). To control for sleep time we first divided spindle count by total sleep time. Curiously, Ube3a-del mice had significantly higher spindle production during their shorter sleep periods compared to WT controls (Fig. 6b: t₍₁₀₎ = 2.361, p = 0.04). As additional controls for sleep, spindle count was correlated with total sleep time (Fig. 6c: F_(1,3) = 1.322, p = 0.334), mean time in slow-wave sleep (Fig. 6d: F_(1,3) = 4.723, p = 0.118), mean time in paradoxical sleep (Fig. 6e: F_(1,3) = 5.422, p = 0.102) and latency to paradoxical sleep (Fig. 6f: F_(1,3) = 0.913, p = 0.410) in Ube3a-del animals. No significant correlations were detected between spindle count and any other sleep metric. Spindle count and spiking count correlations were also analyzed, though no significant correlation was detected (Fig. 6h: F_(1,3) = 0.077, p = 0.800). Interestingly, spindle count was negatively correlated with elevated delta power, where animals with higher delta power had lower spindle counts (Fig. 6g: F_(1,3) = 59.44, p = 0.005). Additional correlation studies showed no significant relationship between spiking, delta and sleep phenotypes (Additional file 2: Fig. 2a–j).

Additional file 1: Supplemental Figure 1. Ube3a-del mice exhibited similar latencies to loss of righting and death after convulsant administration. Seizure susceptibility measures were observed for 30 min after an i.p. injection of 80 mg/kg PTZ. While reduced latencies to both first jerk and generalized clonictonic seizures were observed in Ube3a-del mice Figure 1 (a, b), no genotype differences were detected in either A latency to loss of righting or B death. *p < 0.05, Student’s t-test between genotype.Additional file 1: Supplemental Figure 2. No significant correlations were detected across spiking events and delta rhythmicity, sleep metrics and delta rhythmicity, or spiking and sleep in Ube3a-del mice. Spiking events and delta power were correlated in A Cohort 3 Ube3a-del mice and B Cohort 2 Ube3a-del mice, with no significant relationship detected. Similarly, when analyzing C–F mean time in paradoxical sleep, mean time in slow-wave sleep, latency to paradoxical sleep and total sleep time with delta power and G–J mean time in paradoxical sleep, mean time in slow-wave sleep, latency to paradoxical sleep and total sleep time with spiking events in Cohort 3 animals, no significant correlations were found. *p < 0.05, linear regression for correlation across behaviors.