Design
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 Ube3am-/p+ mice generated from breeding Ube3am+/p- females with Ube3am+/p+ males, termed Ube3a-del throughout the study and Figures. 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 minutes following administration of pentylenetetrazole (PTZ; 80 mg/kg; i.p.) for behavioral seizure characterization (SKU: P6500, Sigma Aldrich, St. Louis, MO, USA). EEG data was 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 (4 males, 3 females) and 3 WT (2 males, 1 females) that were recorded for 24 hours 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 5 Ube3a-del mice (4 males, 1 females) and 7 WT (3 males, 4 females) that were recorded for 72 hours to collect sleep data. All animals were between 8-12 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 light-dark cycle. All animals were littermates and singly housed after EEG implantation to avoid possible device displacement due to cage-mate interactions.
PTZ Administration
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 (4.0g) was subtracted from their total weight. Dosing was conducted in the morning (10:00-11:00) in a dim (~30 lux) 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-sec. 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.
EEG Implantation
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, 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-Colbalt based alloy insulated in medical-grade silicone, making up two channels that included a signal and reference lead. These leads were threaded towards 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 1mm diameter burr holes were drilled (1.0mm anterior and 1.0mm lateral; -3.0mm posterior and 1.0mm 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 Carpofen (5mg/kg; i.p.) directly after surgery and 24 hours 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.
EEG Data Acquisition, Processing and Analysis
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 0-50 Hz to focus on our frequencies of interest.
Power spectral density analysis
Spectral analysis was performed in Python using MEG and EEG Analysis and Visualization (MNE) open-source software. Frequency bands were defined as delta 0.5-4 Hz, theta 5-9 Hz, alpha 9-12 Hz, beta 13-30 Hz, and gamma 30-50 Hz. Spectral power was analyzed using the Welch’s Method which windows over the signal and averages across spectral samples. For power spectral densities (PSD) investigated in Cohort 2, analysis started 3 hours into recording and finished 3 hours prior to the end of recording and PTZ administration, resulting in an 18-hour sampling window. PSD analysis in Cohort 3 also began 3 hours into recording but continued over the three-day recording resulting in a 69-hour sampling window. No statistical difference was detected in PSD within genotype between samples, therefore both cohorts were combined. Total delta power was determined by adding the density data detected in the 0.5-4 Hz frequency range while total power summed all the power spectral density data in the 0.5-50 Hz frequency range. Relative delta frequencies were calculated by dividing total delta power by total power per animal and averaging across genotype.
Spiking analysis
For spiking analysis, baseline EEG data was segmented into 30-second windows where the mean amplitude was calculated per window. Spiking analysis was conducted in data collected in the 24 hours prior to PTZ administration in Cohort 2 and all of the data collected in Cohort 3. In a first pass assessment, potential spike data was demarcated as any point 2.5 standard deviations above or below the mean amplitude of a given window. To determine true spike events, the data was then filtered for peaks which were defined as points where the three data points prior to and following the peak were increasing and decreasing in amplitude, respectively to the potential peak of interest. If activity was detected during a 30-sec window, that data was not included in the spike count to avoid possible movement artifact. Similar to PSD analyses, the first and final three hours were removed from the spiking data for both cohorts. Spiking activity could not be combined between cohorts 2 and 3 as the difference in recording time (24 versus 72 hours) greatly contributed to the number of spikes detected.
Sleep analysis
Sleep in mice was assessed using EEG/EMG signals and automatically binned with Neuroscore software (Data Sciences International, St. Paul, MN) into active wake, wake, slow-wave sleep, or paradoxical sleep states. 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. EEG data was segmented into 1-sec windows and the sleep stage was determined by Neuroscore. We defined a scoring epoch of 30-sec and, if at least 50% of the epoch was predominantly one type of sleep stage, that epoch was marked with the majority stage. If a 50% criterion was not reached, then that epoch was not included in analysis. Sleep/wake stages were evaluated in Cohort 3 for the entirety of the acquisition period as this cohort did not conclude with seizure induction. Mean time in a sleep state was calculated by averaging the time spent in each bout of that state. Sleep latency was defined as the average latency to a sleep state from either active wake or wake. Total sleep time was summed across the entirety of the recording from sleep state bouts.
For sleep parameter analysis across light-dark cycles, the first 24-hr time period was sectioned into 2-hr time bins starting at 12:00 am (0-2 time of day). Paradoxical sleep and slow-wave sleep were evaluated separately, while active wake and wake stages were combined into awake readouts. To determine frequency of each sleep stage across time bins, sleep stage scorings were summed for awake, paradoxical sleep, and slow-wave sleep then divided by total scored stages per bin. To further quantify by across light-dark cycles, percent time across 0-7 and 19-24 time bins was summed and considered “dark cycle.” The sum of the percent time across time bins 7-19 was considered the “light cycle.” Time was summed for awake, paradoxical sleep, and slow-wave sleep, this is the duration metric. Similarly, duration was quantified across light-dark cycles where time bins 0-6 and 18-24 were summed and considered the “dark cycle” and time bins 6-18 were considered the “light cycle.”
Sleep spindle analysis
To identify and analyze sleep spindles, we developed a custom Python script modified from a study designed to validate automated sleep spindle detection [36]. Briefly, a bandpass filter with cutoff frequencies of 10 and 15 Hz was applied to include the mouse spindle peak frequency of 11 Hz [37]. Additionally, a Butterworth filter (3 Hz first stopband, 10 Hz first passband, 15 Hz second passband, 22 Hz second stopband, 24 dB attenuation level) was used to further filter 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 x mean-cubed RMS) was used to determine the start and end of a spindle while an upper threshold (3.5 x mean-cubed RMS) was used to identify the peak of a spindle. Finally, a spindle had to be longer than 0.5 sec and less than 10 sec for detection. Spindle detection was analyzed for the entirety of the acquisition period of 72 hours in Cohort 3.
Statistical analysis
All statistical analyses were performed in Prism (Version 8, GraphPad Software, San Diego, CA, USA) and data is shown as mean ± standard error. All data sets were tested for outliers using the Rout test with Q=1%. For seizure susceptibility (Figure 1A-B, Supplemental Figure 1A-B), spiking activity (Figure 1G-H), power spectral comparisons (Figure 2D-F), light-dark power spectral comparisons (Figure 3B-D and 3F-H), sleep parameters (Figure 4B-E), and spindles (Figure 6A) Student’s t-tests were used to test significance and t, degrees of freedom and p-values are reported. Two-way ANOVAs were used to analyze power spectral density differences between genotypes and the Holm-Sidak multiple comparison posthoc test was used for each frequency band (Figure 2A, C and Figure 3A, E). Additionally, two-way ANOVAs were used to analyze percent time and duration in sleep stages across time bins and between genotypes (Figure 5A, C, E, G, I, K). F, degrees of freedom, and p-values are reported. Mixed effects models were used to analyze percent time and duration in sleep stages between genotypes and light-dark cycles (Figure 5B, D, F, H, J, L) and Tukey’s multiple comparison posthoc test was used for post hoc analysis. F, degrees of freedom, and p-values are reported for mixed effects models and p-values are reported for multiple comparisons. Finally, simple linear regression was used for all correlation data (Figure 6B-F, and Supplemental Figure 2A-J). F, degrees of freedom, and p-values are reported in the text and R2 values are provided in the figures. All statistics are provided in the text and “*” indicates p < 0.05.
Subjects
All animals were housed in a temperature-controlled vivarium maintained on a 12:12 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-Ube3atm1Alb/J (Ube3a) mice obtained from The Jackson Laboratory (Stock number 016590; Bar Harbor, ME, USA) and housed in a 24-h light-dark cycle (7am–7pm), temperature controlled room and fed a standard diet of Teklad global 18% protein rodent diets (Envigo, Hayward, CA, USA). To maintain the colony, Ube3am+/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, Ube3am+/p+ (WT) male mice are paired with Ube3am-/p+ females resulting in Ube3a-del mice and their WT littermate controls. To identify mice, pups were labelled 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.
Design
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 Ube3am-/p+ mice generated from breeding Ube3am+/p- females with Ube3am+/p+ males, termed Ube3a-del throughout the study and Figures. 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 minutes 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 was 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 hours 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 hours to collect sleep data. All animals were between 8-12 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 light-dark cycle. All animals were littermates and singly housed after EEG implantation to avoid possible device displacement due to cage-mate interactions.
PTZ Administration
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 (4.0g) was subtracted from their total weight. Dosing was conducted in the morning (10:00-11:00) in a dim (~30 lux) 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-sec. 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.
EEG Implantation
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, 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-Colbalt based alloy insulated in medical-grade silicone, making up two channels that included a signal and reference lead. These leads were threaded towards 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 1mm diameter burr holes were drilled (1.0mm anterior and 1.0mm lateral; -3.0mm posterior and 1.0mm 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 Carpofen (5mg/kg; i.p.) directly after surgery and 24 hours 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.
EEG Data Acquisition, Processing and Analysis
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 0-50 Hz to focus on our frequencies of interest.
Power spectral density analysis
Spectral analysis was performed in Python using MEG and EEG Analysis and Visualization (MNE) open-source software. Frequency bands were defined as delta 0.5-4 Hz, theta 5-9 Hz, alpha 9-12 Hz, beta 13-30 Hz, and gamma 30-50 Hz. Spectral power was analyzed using the Welch’s Method which windows over the signal and averages across spectral samples. For power spectral densities (PSD) investigated in Cohort 2, analysis started 3 hours into recording and finished 3 hours prior to the end of recording and PTZ administration, resulting in an 18-hour sampling window. PSD analysis in Cohort 3 also began 3 hours into recording but continued over the three-day recording resulting in a 69-hour sampling window. No statistical difference was detected in PSD within genotype between samples, therefore both cohorts were combined. Total delta power was determined by adding the density data detected in the 0.5-4 Hz frequency range while total power summed all the power spectral density data in the 0.5-50 Hz frequency range. Relative delta frequencies were calculated by dividing total delta power by total power per animal and averaging across genotype.
Spiking analysis
For spiking analysis, baseline EEG data was segmented into 30-second windows where the mean amplitude was calculated per window. Spiking analysis was conducted in data collected in the 24 hours prior to PTZ administration in Cohort 2 and all of the data collected in Cohort 3. In a first pass assessment, potential spike data was demarcated as any point 2.5 standard deviations above or below the mean amplitude of a given window. To determine true spike events, the data was then filtered for peaks which were defined as points where the three data points prior to and following the peak were increasing and decreasing in amplitude, respectively to the potential peak of interest. If activity was detected during a 30-sec window, that data was not included in the spike count to avoid possible movement artifact. Similar to PSD analyses, the first and final three hours were removed from the spiking data for both cohorts. Spiking activity could not be combined between cohorts 2 and 3 as the difference in recording time (24 versus 72 hours) greatly contributed to the number of spikes detected.
Sleep analysis
Sleep in mice was assessed using EEG/EMG signals and automatically binned with Neuroscore software (Data Sciences International, St. Paul, MN) into active wake, wake, slow-wave sleep, or paradoxical sleep states. 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. EEG data was segmented into 1-sec windows and the sleep stage was determined by Neuroscore. We defined a scoring epoch of 30-sec and, if at least 50% of the epoch was predominantly one type of sleep stage, that epoch was marked with the majority stage. If a 50% criterion was not reached, then that epoch was not included in analysis. Sleep/wake stages were evaluated in Cohort 3 for the entirety of the acquisition period as this cohort did not conclude with seizure induction. Mean time in a sleep state was calculated by averaging the time spent in each bout of that state. Sleep latency was defined as the average latency to a sleep state from either active wake or wake. Total sleep time was summed across the entirety of the recording from sleep state bouts.
For sleep parameter analysis across light-dark cycles, the first 24-hr time period was sectioned into 2-hr time bins starting at 12:00 am (0-2 time of day). Paradoxical sleep and slow-wave sleep were evaluated separately, while active wake and wake stages were combined into awake readouts. To determine frequency of each sleep stage across time bins, sleep stage scorings were summed for awake, paradoxical sleep, and slow-wave sleep then divided by total scored stages per bin. To further quantify by across light-dark cycles, percent time across 0-7 and 19-24 time bins was summed and considered “dark cycle.” The sum of the percent time across time bins 7-19 was considered the “light cycle.” Time was summed for awake, paradoxical sleep, and slow-wave sleep, this is the duration metric. Similarly, duration was quantified across light-dark cycles where time bins 0-6 and 18-24 were summed and considered the “dark cycle” and time bins 6-18 were considered the “light cycle.”
Sleep spindle analysis
To identify and analyze sleep spindles, we developed a custom Python script modified from a study designed to validate automated sleep spindle detection [36]. Briefly, a bandpass filter with cutoff frequencies of 10 and 15 Hz was applied to include the mouse spindle peak frequency of 11 Hz [37]. Additionally, a Butterworth filter (3 Hz first stopband, 10 Hz first passband, 15 Hz second passband, 22 Hz second stopband, 24 dB attenuation level) was used to further filter 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 x mean-cubed RMS) was used to determine the start and end of a spindle while an upper threshold (3.5 x mean-cubed RMS) was used to identify the peak of a spindle. Finally, a spindle had to be longer than 0.5 sec and less than 10 sec for detection. Spindle detection was analyzed for the entirety of the acquisition period of 72 hours in Cohort 3.
Statistical analysis
All statistical analyses were performed in Prism (Version 8, GraphPad Software, San Diego, CA, USA) and data is shown as mean ± standard error. All data sets were tested for outliers using the Rout test with Q=1%. For seizure susceptibility (Figure 1A-B, Supplemental Figure 1A-B), spiking activity (Figure 1G-H), power spectral comparisons (Figure 2D-F), light-dark power spectral comparisons (Figure 3B-D and 3F-H), sleep parameters (Figure 4B-E), and spindles (Figure 6A) Student’s t-tests were used to test significance and t, degrees of freedom and p-values are reported. Two-way ANOVAs were used to analyze power spectral density differences between genotypes and the Holm-Sidak multiple comparison posthoc test was used for each frequency band (Figure 2A, C and Figure 3A, E). Additionally, two-way ANOVAs were used to analyze percent time and duration in sleep stages across time bins and between genotypes (Figure 5A, C, E, G, I, K). F, degrees of freedom, and p-values are reported. Mixed effects models were used to analyze percent time and duration in sleep stages between genotypes and light-dark cycles (Figure 5B, D, F, H, J, L) and Tukey’s multiple comparison posthoc test was used for post hoc analysis. F, degrees of freedom, and p-values are reported for mixed effects models and p-values are reported for multiple comparisons. Finally, simple linear regression was used for all correlation data (Figure 6B-F, and Supplemental Figure 2A-J). F, degrees of freedom, and p-values are reported in the text and R2 values are provided in the figures. All statistics are provided in the text and * indicates p < 0.05.