The striatum receives dense dopaminergic inputs from the substantia nigra pars compacta (SNc), and partially from the ventral tegmental area (VTA) and dorsal raphe nucleus (DRN) (Stratford and Wirtshafter, 1990; Bjorklund and Dunnett, 2007; Wall et al., 2013; Poulin et al., 2018). Recent evidence reveals that dopaminergic neurons in the SNc, VTA, and DRN are pivotal for the initiation and maintenance of wakefulness (Eban-Rothschild et al., 2016; Cho et al., 2017; Oishi et al., 2017a; Yang et al., 2018). Optogenetic or chemogenetic stimulation of dopaminergic neurons in the SNc, VTA, or DRN induces robust wakefulness (Eban-Rothschild et al., 2016; Cho et al., 2017; Oishi et al., 2017a; Yang et al., 2018). The calcium activity of dopaminergic neurons is demonstrated to be high during wakefulness and correlates with state transitions (Eban-Rothschild et al., 2016; Cho et al., 2017). However, previous studies showed that dopaminergic neurons in the SNc and VTA not only release dopamine but also co-release either glutamate or γ-aminobutyric acid (GABA) (Chuhma et al., 2004; Hnasko et al., 2010; Tritsch et al., 2012; Kim et al., 2015). In addition, activation of dopaminergic fibers in striatal slices rapidly inhibits the action potential firing of striatal medium spiny neurons (MSNs) via the release of the inhibitory transmitter GABA (Tritsch et al., 2012). Early electrophysiological findings suggest that dopaminergic neurons in the VTA and SNc do not change their mean firing rate and pattern across sleep– wake states in rats and cats (Trulson et al., 1981; Miller et al., 1983; Steinfels et al., 1983; Trulson and Preussler, 1984). The lesion of VTA and SNc dopaminergic neurons in cats results in a lack of behavioral arousal but not the alteration of electrocortical waking (Jones et al., 1973). Despite numerous studies devoted to how dopaminergic neurons and dopamine receptors are vital for wakefulness, the field still lacks straightforward and detailed evidence to support that dopamine itself plays a role in the sleep–wake cycle. To address this question, methods with high temporal resolution are needed to monitor the variation of striatal dopamine levels across the sleep–wake cycle.

Classical analytical approaches such as intracerebral microdialysis and electro-chemical voltammetry have been used for the quantitative measurement of extracellular dopamine concentrations, but they provide poor temporal resolution. Using intracerebral microdialysis with a 2-min temporal resolution, a previous study found that dopamine concentrations in the nucleus accumbens (NAc) and prefrontal cortex (PFC) are higher during both the awake state and REM sleep compared to non-REM (NREM) sleep in rats (Lena et al., 2005). Another study using voltammetry at a 5-min resolution showed that the striatal dopamine voltammetric peak is higher in cats while awake than asleep (Trulson, 1985). In addition, extracellular dopamine levels in mouse striatal slices oscillates across the light/dark cycle (Ferris et al., 2014). The above methods have provided useful insights about the release of dopamine transmitter, but poor temporal resolution in freely moving animals still presents a significant limitation. Recently, Patriarchi et al. (2018) engineered a genetically encoded fluorescent dopamine sensor, dLight1.1, which is capable of tracking dopamine transients with high temporal resolution in freely moving animals. The dLight1.1 sensor is developed by replacing the third intracellular loop on D1R with a circularly permuted GFP (cpGFP) and permits the tracking of dopamine levels by detecting cpGFP fluorescence without activating D1Rs signaling cascades downstream.

In our current study, we employed an optimized variant of this dopamine sensor called dLight1.1, which is suitable for in vivo studies. We detected the dLight1.1 fluorescent signals using fiber photometry, while simultaneously collecting polysomnographic recordings in freely behaving mice after environmental or pharmacological manipulations. We found that striatal dopamine levels were at their highest during wakefulness, lower during NREM sleep, and lowest during REM sleep. We also revealed that striatal dopamine levels were correlated with sleep-state transitions. Furthermore, dopamine levels were enhanced in the striatum following the sudden opening of the home-cage door but did not respond to a high-frequency auditory stimulus whether asleep or awake. Moving the mice from their home cage to a new cage also caused striatal dopamine release. Finally, the wake-promoting agent modafinil, but not caffeine, induced the release of striatal dopamine. Taken together, our results provided strong evidence that striatal dopamine levels correlated with wakefulness and could respond to defined stimuli and stimulants.

# MATERIALS AND METHODS

# Ethics Statement

This study was carried out in accordance with the principles of China Regulations on the Administration of Laboratory Animals, the Decree NO.2 of National Science and Technology Commission of the People's Republic of China. The protocol was approved by the Committee on the Ethics of Animal Experiments of Fudan University (permit number: 20140226-024).

### Animals

Male, specific pathogen-free (SPF), inbred C57BL/6 mice (10– 14 weeks old weighing 20–25 g) were obtained from the Shanghai Laboratory Animal Center, Chinese Academy of Sciences (SLAC, Shanghai, China). The mice were housed at a constant temperature (22 ± 0.5◦C and humidity (55 ± 5%), under an automatically controlled 12/12 h light/dark cycle (lights on at 7:00 a.m., illumination intensity ≈ 100 lux) (Zhang et al., 2017). Food and water were available ad libitum. Every effort was made to minimize animal suffering, and the minimum number of animals required to generate reliable scientific data was used.

# Virus Preparation

fnins-13-00242 March 15, 2019 Time: 16:15 # 3

The adeno-associated virus (AAV) plasmid pAAV-CAGdLight1.1 was a gift from Lin Tian (Addgene plasmid # 111067) (Patriarchi et al., 2018). A recombinant AAV vector carrying the dLight1.1 element (AAV-CAG-dLight1.1) was serotyped with AAV9 coat proteins and packaged by Taitool Bioscience Company (Shanghai, China). The final viral concentration was 5 × 1012 genome copies per mL. Aliquots of virus were stored at −80◦C until stereotaxic injection.

# Viral Microinjection and Optical-Fiber Cannula Implantation

Adult mice were anesthetized with pentobarbital (intraperitoneal, 80 mg/kg) and 1% lidocaine hydrochloride (subcutaneous, under the scalp). After shaving the fur on the head and sterilizing the skin with 75% ethanol, the mice were placed on a stereotaxic frame (RWD Life Science, China). The skull surface was cleaned with sterile saline on a sterilized cotton swab. Small craniotomy burr holes were made and 100 nL of the AAV-CAG-dLight1.1 virus was unilaterally microinjected through a fine glass pipette into the dorsal striatum (anteroposterior (AP): 0.80 mm, mediolateral (ML): +1.5 mm, dorsoventral (DV): −2.5 mm), according to the Allen Mouse Brain Atlas (Dong, 2008). The virus injection was administered over a 5-min period using nitrogen-gas pulses of 20 psi delivered through an air compression system (Picospritzer III, Parker Hannifin Corp.) as previously described (Yuan et al., 2017; Luo et al., 2018). At the end of the infusion, the pipette was kept in situ for at least 5 min and then withdrawn slowly. After injections, the mice used for in vivo fiber photometry experiments were implanted with an optical fiber cannula (Fiber core 200 µm, 0.37 numerical aperture (NA), Newdoon, China) into the dorsal striatum. The fiber cannula was implanted 0.2 mm above the virus injection site. After injection and implantation, the mice were placed on a heating pad for post-operative recovery. Mice were housed for at least 2 weeks after injections for complete recovery and to allow viral expression prior to any experiments.

# Electroencephalogram/Electromyogram (EEG/EMG) Electrode Implantation

As previously described (Yuan et al., 2017), the EEG/EMG electrode consists of two stainless steel screws with wire leads for EEG recording and two Teflon-coated stainless-steel wires (Cooner Wire, United States) for EMG recording. To implant the electrode, two small craniotomy holes were made in the frontal (AP: +1.5 mm, ML: −0.7 mm) and parietal (AP: −1.5 mm, ML: −1.0 mm) regions with a cranial drill. The EEG electrodes were screwed into the craniotomy holes and the EMG wires were bilaterally placed into the trapezius muscles. All the electrodes were attached to a mini-connector and fixed to the skull with dental cement.

# Polysomnographic Recording and Analysis

After 2 weeks of post-operative recovery, each animal was connected to an EEG/EMG recording cable in a recording apparatus (transparent barrel) and habituated for 3 days before polysomnographic recordings were conducted. The uninterrupted, synchronous recordings of EEG and EMG were performed by means of a slip ring, which was designed to let the mice move freely. Cortical EEG and neck EMG signals were amplified and filtered (Biotex Kyoto, Japan. EEG, 0.5– 30 Hz; EMG, 20–200 Hz), digitized at a sampling rate of 512 Hz, and recorded by a Power 1401 digitizer and Spike2 software (CED, Cambridge, United Kingdom). The Spike2 data were then converted to text format for the analysis of vigilance states using SleepSign software (Kissei Comtec, Nagano, Japan). After the experiment was completed, the EEG/EMG data were automatically classified off-line using 4 s epochs for wakefulness, REM sleep, and NREM sleep using SleepSign software according to standard criteria (Huang et al., 2005). These automatically defined classifications were checked manually and corrected if necessary. Wakefulness was defined as periods of desynchronized, low-amplitude EEG and heightened EMG activity with phasic bursts; NREM sleep was defined as periods of synchronized, high-amplitude, low-frequency (delta band: 0.5–4 Hz) EEG and low EMG activity (compared with wakefulness) without phasic bursts; REM sleep was defined as periods with a pronounced theta rhythm (6–10 Hz) and no EMG activity.

### Fiber Photometry

Following the 2-week recovery period from the virus injection and implantation surgery, dLight1.1 fluorescence emission was recorded with a fiber photometry system (Thinkerbiotech, Nanjing, China) using methods similar to previous studies (Li et al., 2016; Luo et al., 2018). The fiber photometry was performed at 8:00–18:00. Briefly, to record fluorescent signals, the beam from a 488-nm laser (OBIS 488LS, Coherent, United States) was reflected by a dichroic mirror (MD498; Thorlabs), focused by a 10× objective lens (NA = 0.3, Olympus), and then coupled to an optical commutator (Doric Lenses, Canada). An optical fiber (230 mm optical density [O.D.], NA = 0.37, 1 m long) guided the light between the commutator and the implanted optical fiber. The laser power was adjusted at the tip of the optical fiber to a low level of 10–20 µW, to minimize bleaching. The dLight1.1 fluorescent signal was bandpass-filtered (MF525- 39, Thorlabs) and collected by a photomultiplier tube (R3896, Hamamatsu). An amplifier (C7319, Hamamatsu) was used to convert the photomultiplier tube current output into voltage signals, which was further filtered through a low-pass filter (40 Hz cut-off; Brownlee 440). The photometry analog voltage signals were digitalized at 512 Hz and recorded by a Power 1401 digitizer and Spike2 software (CED, Cambridge, United Kingdom) simultaneously with polysomnographic recordings.

Photometry data were analyzed by customized Matlab software (Matlab, 2016a, MathWorks, United States) as described in our previous study (Luo et al., 2018). In brief, the photometry data were exported from Spike2 software in Matlab format for further analysis. The signal data were smoothed with a moving average filter (0.2 s span). For each session, the photometry signal F was converted to 1F/F by calculating 1F/F = (F − Fmean)/Fmean, where Fmean is the average

fluorescence in recording episode. We recorded data for 4–10 h per session and calculated the averaged 1F/F during periods of wakefulness, NREM, and REM sleep. For the analysis sleep-state transitions, we identified each state transition and aligned 1F/F with a ±60 s window before and after the switch point. For stimuli analysis, the photometry signal was aligned with a ±20 s window before and after the event onset. For the modafinil, caffeine, and cage-change experiments, we recorded signals for 6 h (from 1 h before to 5 h after the administration of drugs or the cage change) and calculated the averaged 1F/F value pre- and post-treatment.

### Auditory Tone and Door-Opening Test

To examine whether striatal dopamine levels respond to external stimuli, a high-frequency auditory tonal stimulus (70 dB, 2–4 kHz, 10 s duration) or a sudden door-opening stimulus was applied to mice during NREM sleep or wake periods as previously described (Cho et al., 2017). The loudspeaker was placed on top of the recording cage about 50 cm above the mouse and the intensity of the auditory tone inside the cage was calibrated with a sound meter (Uni-Trend UT350, Dongguan, China). The auditory stimulus and door-opening were both performed suddenly (without warning) when the mouse was either asleep or awake. Then the mice were allowed to rest without disturbance for 5–10 min before the next stimulation. Each type of stimulus was repeated at least three times for each mouse.

### Pharmacological Treatments

One hour following the onset of the photometry recording, modafinil (Sigma-Aldrich, United States) was dissolved in sterile saline containing 10% DMSO and 2% (w/v) cremophor and administered intraperitoneally at doses of 45 and 90 mg/kg. Caffeine (Alfa Aesar, United Kingdom) was dissolved in sterile saline and given intraperitoneally at a dose of 15 mg/kg. Both drugs were prepared fresh, immediately before use.

# Histology

Histological verification of viral expression was performed as described previously (Luo et al., 2018). After all the experiments were completed, the mice were deeply anesthetized with an overdose of pentobarbital and transcardially perfused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde (PFA) in PBS. Then, the brains were post-fixed in 4% PFA in 0.1 M phosphate buffer (PB; pH 7.4) for 6 h. Next, the brains were then transferred to 20% sucrose in PBS until they sank to the bottom, followed by an incubation in 30% sucrose until they sank to the bottom. Then, the tissue was embedded in optimum cutting temperature (OCT) compound, frozen, and coronal sections were cut at 30 µm by a freezing microtome (Leica, Germany). Since dLight1.1 cannot be detected directly by native fluorescence, a further immunohistochemistry was required (Patriarchi et al., 2018). The brain slices were washed in PBS and incubated in chicken anti-GFP primary antibody (1:5000 dilution; GFP-1020, Aves Labs, United States) at 4◦C overnight. The next day, the sections were incubated in Alexa Fluor 488-conjugated donkey anti-chicken secondary antibody (1:1000 dilution, Cat. # 703-545-155, Jackson ImmunoResearch, United States) for 2 h at room temperature. Finally, slices were washed in PBS and mounted on glass slides using DAPI Fluoromount-G (Southern Biotech, Cat. # 0100–20). Images were captured by a fluorescence microscope (IX71, Olympus).

### Statistical Analyses

Data are presented as the means ± SEM. Paired or unpaired Student's t-tests were used for two-group comparisons and oneway analysis of variance (ANOVA) was used for multiple-group comparisons. A two-way ANOVA was used to analyze the experiments with modafinil and caffeine treatment. Following the ANOVA, Sidak or Bonferroni's post hoc tests were used to make pairwise comparisons. All the statistical tests were two-tailed and P-values less than 0.05 were considered significant. All the statistical analyses were performed using Prism 7.0 (GraphPad Software, United States) and MATLAB R2016a software.

# RESULTS

### Striatal Dopamine Levels Across Spontaneous Sleep–Wake Cycle

Patriarchi et al. (2018) showed that the dLight1.1 plasmid was silent in the absence of dopamine. When dopamine is released from presynaptic terminals, it binds to the dLight1.1 sensor and dramatically increases its fluorescence (**Figure 1A**). In order to ensure the efficient and precise expression of dLight1.1 in the dorsal striatum, an AAV encoding dLight1.1 under the control of a CAG promoter was unilaterally injected into the dorsal striatum of mice (**Figure 1B**). The mice were also chronically implanted with (1) a fiberoptic probe upon the virus-injection site for subsequent delivery of light excitation and collection of dLight1.1 fluorescence and (2) EEG/EMG electrodes for simultaneous polysomnographic recordings (**Figure 1C**). The success of the virus infection and the appropriate location of the fiberoptic implant were verified in each mouse after all experiments were completed. As shown in **Figure 1D** and **Supplementary Figure S1**), dLight1.1 fluorescence was robustly expressed at the injection site in the dorsal striatum.

To examine whether the striatal dopamine levels correlated with distinct vigilance states, we recorded striatal dLight1.1 fluorescent signals across spontaneous sleep–wake cycle. As shown in **Figures 1E,F**, the fluctuations of dLight1.1 fluorescence were correlated with the EEG/EMG signals. To compare the dLight1.1 signal amplitude during distinct vigilance states, the fluorescent signals were averaged in a state-dependent manner. We found that the mean striatal dLight1.1 signal was significantly higher during wakefulness (0.952% ± 0.128%) than during NREM sleep (−0.6% ± 0.114%) or REM sleep (−2.129% ± 0.179%), which exhibited the lowest fluorescence (**Figure 1G**; n = 7 mice; F2,18 = 116.1, P < 0.01; post hoc Tukey test: wake vs. NREM P < 0.01, wake vs. REM P < 0.01, NREM vs. REM P < 0.01). Although the peak value of REM sleep is higher and the trough value is lower (**Supplementary Figure S3**), the mean fluorescent signal is the lowest in REM sleep. Moreover, Dahan et al. (2007) found that the dopamine neuronal activity during REM sleep showed a pronounced bursting pattern with decreased amplitude. This firing pattern of dopaminergic

FIGURE 1 | Striatal dLight1.1 signals at distinct spontaneous sleep–wake states. (A) Schematic diagram of dLight1.1 with the dopamine D1 receptor and circularly permuted GFP (cpGFP) module (upper panel) and the working principle of dLight1.1. (B) Schematic showing the injection of AAV-CAG-dLight1.1 into the dorsal striatum. (C) Schematic showing the setup for fiber photometry used to assess dLight1.1 fluorescence with simultaneous polysomnographic recordings. (D) Expression of dLight1.1 in the dorsal striatum. The scale bar is 200 µm. (E,F) Representative EEG, EMG, and fluorescent photometry signal traces of striatal dLight1.1. during distinct sleep–wake states (green, wake; blue, NREM sleep; magenta, REM sleep). (G) Quantification of the average striatal dLight1.1 signal at distinct sleep–wake states. One-way ANOVA: F2,18 = 116.1, P < 0.0001; Tukey's post hoc test: wake vs. NREM sleep ∗∗P < 0.0001, wake vs. REM sleep ∗∗P < 0.0001, NREM vs. REM sleep ∗∗P < 0.0001; n = 7 mice. (H) Temporal dynamics of the striatal dLight1.1 signal during long-term wake (green), NREM sleep (blue), and REM sleep (magenta) episodes within normalized time. (I) Striatal dLight1.1 signal at the early wake period (first 20% of wake period) and the late wake period (last 20% of wake period) (t6 = 5.058, ∗∗P = 0.0023, n = 7 mice).

FIGURE 2 | Striatal dLight1.1 signal dynamics across spontaneous sleep-state transitions. (A) Striatal dLight1.1 signals across NREM sleep-to-wake transition. (Left) The time course of the dLight1.1 signal across the NREM sleep-to-wake transition. (Right) The average amplitude of the dLight1.1 signal 60 s pre- and post-transition (t6 = 5.441, ∗∗P < 0.01, n = 7 mice). (B) Striatal dLight1.1 signal at the wake-to-NREM sleep transition. (Left) The time course of the dLight1.1 signal across the wake-to-NREM sleep transition. (Right) The average amplitude of the dLight1.1 signal 60 s pre- and post-transition (t6 = 2.528, ∗P = 0.044). (C) Striatal dLight1.1 signal at the REM sleep-to-wake transition. (Left) The time course of the dLight1.1 signal across the REM sleep-to-wake transition. (Right) The average amplitude of the dLight1.1 signal 60 s pre- and post-transition the (t6 = 1.053, P = 0.333). (D) Striatal dLight1.1 signal at the NREM-to-REM sleep transition. (Left) The time course of the dLight1.1 signal across the NREM-to-REM sleep transition. (Right) The average amplitude of the dLight1.1 signal 60 s pre- and post-transition (t6 = 1.509, P = 0.182). (E) Time courses of the striatal dLight1.1 signals across NREM sleep-to-long wake or NREM sleep-to-short wake periods. (F) The change in the dLight1.1 signal after long or short wake periods compared with the 60 s pre-wake period (t12 = 2.388, ∗P = 0.034). Data are presented as the mean (black trace) ± SEM (gray shading) in (A–D) and as the mean (long wake period, green; short wake period, gray) ± SEM (shading) in (E).

neurons may be the reason that striatal dopamine level during REM sleep was more divergent with higher peak and lower trough. To examine the temporal dynamics of striatal dopamine during long-term sleep–wake states (duration longer than 30 s), we normalized the variable duration of sleep–wake states to a unit-less time window from 0 (state onset) to 1 (state offset). During the wakefulness period, dLight1.1 fluorescence peaked soon after the onset of wakefulness and gradually attenuated

(**Figure 1H**). We calculated the mean fluorescence during the first 20% and the last 20% of the wake episode. We found that dLight1.1 fluorescence in the early 20% of the wake episode was significantly higher than that in the late 20% (**Figure 1I**; n = 7 mice; t = 5.058, P = 0.0023). These findings demonstrated that striatal dopamine levels not only varied across spontaneous sleep–wake states but also showed dynamic changes within wakefulness episodes. Next, we assessed the striatal dopamine levels during state transitions. We found that the striatal dLight1.1 signal increased significantly during NREM sleep-towake transitions (**Figure 2A**; t = 5.441, P < 0.01), whereas it decreased during wake-to-NREM sleep transitions (**Figure 2B**; t = 2.528, P = 0.044). However, there was no significant dLight1.1 fluorescence change during REM sleep-to-wake transitions and NREM-to-REM sleep transitions (**Figure 2C**, P = 0.333 and **Figure 2D**, P = 0.182). Since the diversity of animal behaviors depend on the duration of the wakefulness episode, we further examined whether striatal dopamine levels fluctuated with the duration of wakefulness episodes; we calculated the dLight1.1 signal at longer wake episodes (duration > 30 s) and brief wake episodes (duration < 30 s). Interestingly, the net growth of dLight1.1 fluorescence was significantly higher when mice were awake for longer periods than for brief wake periods (**Figures 2E,F**; t = 2.388, P = 0.0343). These results indicated that striatal dopamine levels at wake onset were correlated with the duration of the following wake episode. Taken together, these findings demonstrated that striatal dopamine levels were highest during wakefulness and that they fluctuate dynamically across spontaneous state transitions.

### Striatal Dopamine Levels in Response to Acute External Stimuli

To investigate the dynamics of dopamine levels in the dorsal striatum in response to external stimuli, we recorded striatal dLight1.1 fluorescence while simultaneously conducting polysomnographic recordings in animals exposed to diverse salient stimuli and stimulants. The unexpected presentation of an auditory tone stimulus (70 dB, 2–4 kHz, 10 s duration) was employed as previously described (Cho et al., 2017). We exposed the mice to an auditory tone while asleep or awake (**Figures 3A,C**) and observed that the mice were immediately awakened when the tone was applied during the sleep period (**Figure 3A**). However, there were no detectable changes in striatal dLight1.1 fluorescence in response to the auditory stimulus either when mice were asleep or awake (**Figure 3B**: P = 0.260 and **Figure 3D**: P = 0.127). These results indicated that acute, short auditory tone stimuli did not elevate striatal dopamine release. Surprisingly, the striatal dLight1.1 signal ascended when the door of the mouse's home cage was suddenly opened at the end of the trial session. This observation prompted us to systematically investigate whether unexpected door-opening during sleep or wake states induced striatal dLight1.1 signal changes. We discovered that the striatal dLight1.1 signal rapidly increased whenever the home-cage door was opened suddenly during the sleep period (**Figures 3E,F**; t = 12.15, P < 0.01) and the wake period (**Figures 3G,H**; t = 11.18, P < 0.01). In addition, the amplitude of the striatal dLight1.1 signal induced by sudden door-opening was higher than the amplitude during spontaneous awake periods. Although both auditory tone stimulation and dooropening were able to wake up sleeping mice, the door-opening stimulus elevated dopamine release while auditory stimulation did not. The behavioral paradigm door-opening test maybe mix with visual and olfactory stimuli. In order to explore whether visual and olfactory stimuli enhance striatal dopamine tone, predator odor TMT or light flash were employed. We used light flash at 1 Hz for 10 s when mice were sleeping or awaking (**Supplementary Figure S2A**), and found that flash during awaking increased striatal dLight1.1 fluorescence (**Supplementary Figure S2C**, t = 4.486, P = 0.0463), but failed to enhance during sleep (**Supplementary Figure S2B**, t = 0.9181, P = 0.4555). These results indicated that awareness of visual stimuli enhanced the striatal dopamine tone. Application of air or predator odor TMT didn't enhance striatal dLight1.1 fluorescence (air, t = 0.4803, P = 0.6784; TMT, t = 0.2858, P = 0.8019. **Supplementary Figures S2D–F**). These results indicated that striatal dopamine levels responded only to specific acute stimuli.

### Striatal Dopamine Levels in Response to Cage Change

Our previous study showed that dopamine receptors were necessary for arousal when mice were exposed to new environments (Qu et al., 2010; Xu et al., 2014). However, whether exposure to a new environment augmented the striatal dopamine level was still unclear. To address this question, the cage-change model was employed to mimic a new environment (**Figure 4A**). We found that mice exhibited continuous wakefulness for almost 2 h after tail handled followed by moving to a new cage (**Figure 4B**), while mice kept awake for about 30 min after tail handled followed by returning to their home cages, coincident with our previous results (Qu et al., 2010; Xu et al., 2014). Striatal dLight1.1 fluorescence sharply increased when the mice were tail handled, then gradually attenuated to baseline about 2 h after moved to a new cage, whereas quickly decreased to baseline about 30 min after returned to home cages after tail handled (**Figures 4B,C**). We calculated the mean fluorescence 30 min before (serving as the baseline), 30 min and 30–120 min after returned home cage or moved to a new cage, and found that the mean striatal dLight1.1 fluorescence was significantly higher than baseline for post 30 min when mice were tail handled followed by returned to home cage, and but there was no statistical significance between post 30–120 min and baseline (**Figure 4D**. F2,4 = 7.284, P = 0.0464), However, when mice were tail handled followed by moved to new cage, the mean striatal dLight1.1 fluorescence was significantly higher for post 30 min and the following 90 min than baseline (**Figure 4D**. F2,12 = 10.1832, P = 0.0026). These findings revealed that moving the mice to home cage or new cage induced wakefulness and enhanced striatal dopamine release which sustained for 30 min for mice returned to home cage and for at least 2 h in mice exposed to a new cage.

FIGURE 3 | Striatal dLight1.1 fluorescence in response to acute stimuli. (A, Left) Schematic showing high-frequency auditory tones applied in the sleep state simultaneously with fiber photometry and EEG/EMG recording. (Right) Example traces of the fluorescence signal, EEG, and EMG before and after the onset of the auditory tone during sleep. (B, Left) The time course of the striatal dLight1.1 signal in response to auditory tones when mice were sleeping. (Right) Average fluorescence before and after onset of the auditory tone (t6 = 1.243, P = 0.260). (C, Left) Schematic showing auditory tones applied in the awake state simultaneously with fiber photometry and EEG/EMG recording. (Right) Example traces of the fluorescence signal, EEG, and EMG before and after the onset of the auditory tone during the awake state. (D, Left) The time course of the striatal dLight1.1 signal in response to auditory tones when mice were awake. (Right) Average fluorescence before and after onset of the auditory tone (t6 = 1.771, P = 0.127). (E, Left) Schematic showing the sudden opening of the recording-cage door during the sleep state. (Right) Typical traces of the fluorescence signal, EEG, and EMG before and after the onset of the door-opening stimulus during sleep. (F, Left) The time course of the striatal dLight1.1 signal in response to the door-opening stimulus while the mice were sleeping. (Right) Average fluorescence before and after onset of the door-opening stimulus (t6 = 12.15, ∗∗P < 0.0001). (G, Left) Schematic showing the sudden opening of the recording-cage door while mice were in the awake state. (Right) Typical traces of the fluorescence signal, EEG, and EMG before and after the onset of the door-opening stimulus during the wake period. (H, Left) The time course of the striatal dLight1.1 signal in response to the door-opening stimulus while the mice were awake. (Right) Average fluorescence before and after onset of the door-opening stimulus (t6 = 11.18, ∗∗P < 0.0001).

FIGURE 5 | Effects of caffeine (15 mg/kg) and modafinil (45 and 90 mg/kg) on the striatal dLight1.1 fluorescent signal. (A) Typical examples of the striatal dLight1.1 fluorescent signal, EEG, and EMG traces following vehicle (10% DMSO), caffeine, and modafinil administration. (B) Time courses of the striatal dLight1.1 fluorescent signal following vehicle, caffeine, and modafinil administration. (C) Average striatal dLight1.1 fluorescent signal in the 1 h before (gray circle and bar) and 2 h after each administration. Two-way ANOVA between time and stimuli: F1,20 (time) = 147.5142, P < 0.0001, pre–post comparisons followed by Bonferroni's test: ∗∗P < 0.0001; F3,20 (stimuli) = 29.5910, P < 0.0001. Stimuli comparisons followed by Sidak's test: ##P < 0.0001).

 Our previous studies have shown that caffeine promoted wakefulness via adenosine A2A receptors (A2ARs) (Huang et al., 2005; Lazarus et al., 2011), whereas modafinil induced wakefulness via D1Rs and D2Rs (Qu et al., 2008). A2ARs were reported to be densely co-expressed with D2Rs in the dorsal striatum (Svenningsson et al., 1999; Lazarus et al., 2012, 2013). however, it is still unknown whether modafinil and caffeine alter striatal dopamine levels. We found that caffeine (15 mg/kg) or modafinil (45, 90 mg/kg) promoted continuous wakefulness for about 2, 3, or 5 h, respectively (**Figure 5A**), consistent with our previous results (Huang et al., 2005; Qu et al., 2008). Administration of modafinil strongly increased striatal dLight1.1 fluorescence, with dLight1.1 signals rapidly reaching a peak and then gradually attenuating (**Figures 5A,B**). For better comparison of the effects of each drug on striatal dopamine tone, we calculated the average fluorescence 1 h before (serving as the baseline) and over the 2 h after each administration. We chose 2 h for comparisons because caffeine 15 mg/kg induced wakefulness for 2 h, although modafinil promoted longer effects. As shown in **Figure 5C**, administration of modafinil significantly enhanced the striatal dLight1.1 fluorescence, but vehicle and caffeine didn't, compared with their respective baseline (F1,20 = 147.5142, P < 0.0001, pre–post comparisons followed by Bonferroni's test: modafinil 90 mg/kg vs. baseline, P < 0.0001; modafinil 45 mg/kg vs. baseline, P < 0.0001; caffeine vs. baseline, P > 0.9999; vehicle vs. baseline, P > 0.9999). Moreover, the mean dLight1.1 signals for 2 h after administration of modafinil 90 mg/kg was 13.81% ± 1.55%, significantly higher than 6.83% ± 1.27% for modafinil 45 mg/kg, −0.86% ± 0.55% for caffeine 15 mg/kg, and −0.02% ± 0.40% for vehicle. The dLight signal for modafinil at 45 mg/kg was higher than that for caffeine 15 mg/kg or vehicle, but there was no statistical significance between caffeine 15 mg/kg and vehicle (F3,20 = 29.5910, P < 0.0001. Comparisons followed by Sidak's test: modafinil 90 mg/kg vs. modafinil 45 mg/kg, P < 0.0001; modafinil 45 mg/kg vs. caffeine, P < 0.0001; caffeine vs. vehicle, P = 0.4198). Taken together, these findings indicated that modafinil increased striatal dopamine levels but caffeine did not.

### DISCUSSION

Using a dopamine sensor and simultaneous polysomnographic recordings, we demonstrated that striatal dopamine levels were highest during wakefulness and dopamine fluctuations correlated with spontaneous sleep–wake transitions. Furthermore, we revealed that some external salient stimuli and certain wakepromoting stimulants elicited striatal dopamine release. These findings provide strong evidence that dopamine in the dorsal striatum is important for wakefulness under baseline conditions, induced by cage change or wake-promoting drug modafinil but not caffeine.

The dorsal striatum receives robust dopaminergic inputs from the SNc, as well as some input from the VTA and DRN (Beckstead et al., 1979; Stratford and Wirtshafter, 1990; Poulin et al., 2018). Although the single-unit firing rate of SNc and VTA dopaminergic neurons in cats and rats shows no changes across the stages of sleep or waking (Trulson et al., 1981; Miller et al., 1983; Steinfels et al., 1983), the specific enhancement of VTA and SNc dopaminergic neuron activity by optogenetic or chemogenetic approaches dramatically induces wakefulness (Eban-Rothschild et al., 2016; Oishi et al., 2017a; Yang et al., 2018). Furthermore, our recent work demonstrates that the inhibition of striatal D2R/A2AR-containing neurons, mimicking the action of dopamine on D2Rs, promotes wakefulness (Yuan et al., 2017). Axonal dopamine release not only depends on the firing rate and pattern of dopaminergic neurons but also on the concentration of calcium (Ca2+) (Kawagoe et al., 1992; Chen et al., 2011). Moreover, dopamine release is assumed to reflect a global response to the activity of midbrain dopaminergic neurons at a population level (Rice et al., 2011). Recent photometry data demonstrate that the population-level calcium signal of VTA and DRN dopaminergic neurons are correlated with the sleep– wake cycle (Eban-Rothschild et al., 2016; Cho et al., 2017). Using intracerebral microdialysis, Lena et al. (2005) elaborated that the dopamine concentrations in the NAc, downstream of the VTA, are higher during both wakefulness and REM sleep compared with NREM sleep in rats. Trulson (1985) used voltammetry to measure the release of dopamine in the dorsal striatum of cats across their sleep–wake cycle at a 5-min temporal resolution. During the 45-min recording consisting of consecutive 15-min periods of each sleep stage, the striatal dopamine voltammetric peak decrease from wake to NREM sleep, and from NREM to REM sleep in cats. Consistent with this finding, our present study showed that striatal dopamine levels were at their highest during wakefulness and their lowest during REM sleep. Taking advantage of the high temporal resolution of dLight1.1, we further analyzed the dynamic variation of striatal dopamine during short-term state transitions and found that striatal dopamine increased significantly during NREM sleepto-wake transitions and decreased during wake-to-NREM sleep transitions. Moreover, extracellular dopamine levels in mice striatal slices were reported to oscillate across the light/dark cycle (Ferris et al., 2014). These pieces of evidence suggest that recording the electrophysiological activity of single dopaminergic neurons does not accurately reflect the functional state of the central dopaminergic system. Collectively, the above findings solidly demonstrate that striatal dopamine release correlates with the sleep–wake cycle, despite the fact that dopaminergic neuron firing is uncorrelated with vigilance states.

The dopamine level in the dorsal striatum is a signal for prolonged time in wakefulness and crucial for the maintenance of wakefulness. Chemogenetic or optogenetic activation of SNc, VTA, or DRN dopaminergic neurons induce a long-lasting period of wakefulness (Eban-Rothschild et al., 2016; Cho et al., 2017; Oishi et al., 2017a; Yang et al., 2018). The fluctuation in DRN dopaminergic activity across sleep-to-wake transitions is significantly larger when mice are awake for a longer period than when they are briefly awake (Cho et al., 2017). Consistent

with this, our present study showed that striatal dopamine levels were higher when mice had longer periods of wakefulness (>30 s) than when they had brief periods of wakefulness (<30 s). These findings suggest that the striatal dopamine level can predict the length of the following wake episode. Chemogenetic inhibition of SNc or VTA dopaminergic neurons promotes sleep at the expense of wakefulness (Eban-Rothschild et al., 2016; Yang et al., 2018). Inhibition of dopaminergic neurons can, in theory, reduce striatal dopamine release and then lead to striatal D1R/D2R inactivation. Our previous study revealed that the genetic deletion of D2Rs destabilizes the wake stage and shortens the duration of wakefulness episodes (Qu et al., 2010). What's more, chemogenetic activation of D2Rs-containing neurons in the dorsal striatum promoted sleep (Yuan et al., 2017). Our present study showed that striatal dopamine levels peaked soon after wake onset and gradually reduced during the wake period. These pieces of evidence collectively suggest that decreasing dopamine levels can facilitate sleep initiation and lessens alertness. The multiplicity of arousal systems guarantees diverse behaviors in the normal individual. It has been reported that histaminergic tuberomammillary neurons are crucial for brief wakefulness (Huang et al., 2006). We can conclude from the literature that distinct arousal mechanisms govern different levels or types of alertness.

Our study showed that striatal dopamine levels not only correlated with the spontaneous sleep–wake cycle, but also responded to salient environmental stimuli. In addition to homeostatic and circadian drives as well as emotion, a good sleep also requires a quiet and safe environment (Saper et al., 2005). An unexpected sound or predator invasion can disrupt the quality of sleep (Saper et al., 2005; Eban-Rothschild et al., 2016; Cho et al., 2017). Consistently, the acute auditory tone and sudden dooropening of the home cage immediately awoke mice from NREM sleep. We found that door-opening and light flash induced striatal dopamine release, whereas the auditory tone failed to do so. An early study found that opening the door of a cat's housing chamber or the presence of the experimenter in the cat's field of vision is associated with the bursting activity of singleunit SNc dopaminergic neurons (Steinfels et al., 1983). While dopaminergic neurons fire in a slow, irregular fashion under baseline conditions, resulting in a tonic release of dopamine, they fire in bursts in response to salient environmental stimuli, which lead to phasic increases in dopamine release (Overton and Clark, 1997). Consistent with this, our data showed that more the striatal dopamine was released following exposure to salient stimuli than during spontaneous wakefulness. Mapping the inputs to midbrain dopaminergic neurons may help us understand their different responses to auditory and visual stimuli. Monosynaptic tracing studies demonstrate that SNc and VTA dopaminergic neurons receive dense input from the superior colliculus, a key structure processing visual information, but received hardly any input from the auditory system (Watabe-Uchida et al., 2012; Lerner et al., 2015). Moreover, previous studies have demonstrated that the superior colliculus is necessary to relay short-latency visual information to dopamine-containing regions of the ventral midbrain in rats (Comoli et al., 2003; Dommett et al., 2005). The door-opening stimulus combined both auditory and visual stimuli, making it sufficient to elicit striatal dopamine release. However, an auditory tone as a conditioned stimulus combined with a reward unconditioned stimulus induces a large dopamine release upon repeated cue-reward pairing but not in the first training session (Patriarchi et al., 2018). Collectively, the above findings suggest that striatal dopamine responds to specific stimuli.

Cage-change is a mouse model that mimics the human first-night effect, which can be observed in unfamiliar sleeping environments. We previously found that the genetic deletion or pharmacological blockade of D2Rs (densely expressed in the dorsal striatum) reduce the duration of wake episodes in mice following being moved to a new cage. The plasma corticosterone levels are elevated after cage change, suggesting that cage change or new environment induces an elevating arousal level (Qu et al., 2010; Xu et al., 2014). Eban-Rothschild et al. (2016) found that transferring the mice to a new environment or introducing novel objects to their home space enhance calcium activity in VTA dopaminergic neurons. Moreover, chemogenetic inhibition of VTA dopaminergic neurons prompts nest-building behavior and promotes sleep. Consistently, we found that cage change induced a significant increase in striatal dopaminergic tone. Therefore, we supposed that cage change induced an elevating arousal level with more increases in the striatal dopaminergic tone, suggesting that dopamine tone may relate to arousal level and the dopaminergic system may be a target for treating insomnia caused by environmental stimuli.

In the present study, we employed auditory tone, dooropening, light flash, predator odor, and cage change paradigm combined with striatal dopamine tone recording and found that striatal dopamine responds to specific stimuli. Striatal dopamine activities also are associated with lots of behaviors, such as locomotion, motivation, reward, and stress, all of which operate on the basis of wakefulness. The role of dopamine in motor behavior is extensively concerned. Rapid phasic signal in striatum-targeting dopaminergic axons is associated with triggering and locomotion in mice (Howe and Dombeck, 2016). Large proportion of SNc dopaminergic neurons transiently increased their activities before self-paced movement initiation in mice (da Silva et al., 2018). The activity of VTA dopaminergic neurons are increased during itch-induced scratching behavior in freely moving mice (Yuan et al., 2018). Dopamine is also involved in negative emotion. Intense exteroceptive stimuli, such as an electric shock on the tail or placing animals into an ice-water bath, provoke large and abrupt rises in the striatal dopamine signal (Keller et al., 1983). Dopamine neurons projecting to the anterior striatum display patterns of activity consistent with the reward value, while those projecting to the posterior tail of the striatum are activated by aversive and neutral stimuli, such as unexpected tone and air puff (Menegas et al., 2018). Taken together, striatal dopamine activities are associated with lots of behaviors, operating on the basis of wakefulness.

Modafinil is a wake-promoting drug used to treat daytime sleepiness. Numerous studies have suggested that modafinil promotes wakefulness by acting on the dopaminergic system Consistently, modafinil is found to bind dopamine uptake transporters (DATs) with low affinity (Mignot et al., 1994) and

the deletion of the DAT gene in mice blocks the wake-promoting effects of modafinil (Wisor et al., 2001). We previously found that the blockade of D1Rs and D2Rs abolishes the arousal effects of modafinil (Qu et al., 2008). In addition, modafinil has been reported to enhance extracellular levels of dopamine in the NAc, PFC, and medial hypothalamus of rats (de Saint Hilaire et al., 2001; Murillo-Rodriguez et al., 2007). Moreover, optogenetic stimulation of dopaminergic terminals in the NAc and dorsal striatum induce wakefulness, whereas the same conditions in the PFC fail to induce wakefulness. This result suggests that the NAc and dorsal striatum could be specific targets of modafinil. In line with this, our present study found that modafinil robustly raised striatal dopamine levels. Another widely used stimulant, caffeine, is a psychoactive compound that is found to promote wakefulness via A2ARs (Huang et al., 2005). A2ARs are densely co-expressed with D2Rs in the striatum (Schiffmann et al., 1991). Previous study revealed that the genetic deletion of striatal A2ARs abolishes arousal effect of caffeine (Huang et al., 2005; Lazarus et al., 2011). Chemogenetic inhibition of dorsal or ventral striatal A2AR positive neurons promote arousal, that mimic arousal effects of caffeine (Oishi et al., 2017b; Yuan et al., 2017). The external globus pallidus mediates the effect of dorsal striatal A2AR positive neurons on sleep, while ventral pallidum, but not VTA, mediates the effect of ventral striatal A2AR positive neurons on sleep. Our current study showed that caffeine did not enhance striatal dopamine levels. These results are consistent with previous studies that caffeine doesn't increase the c-fos expression in the SNc (Bennett and Semba, 1998). The differential effects of modafinil and caffeine on striatal dopamine levels suggest that despite them both being wake-promoting compounds that target the basal ganglia, their arousal effects have different underlying mechanisms, dopaminergic system for modafinil and adenosine system for caffeine. Patients with PD suffer from severe EDS and nigrostriatal dopamine deficiency has been proposed to be responsible for PD (Adler and Thorpy, 2005). In fact, most PD therapeutic agents act by increasing dopaminergic activity. In this study, we found that modafinil dramatically elicited striatal dopamine release. Hence, we propose that modafinil may be a potential agent to treat EDS in PD patients with motor symptoms. In addition, the adenosine system, especially the A2AR, has emerged as an attractive non-dopaminergic target in the pursuit of improved therapy for PD (Antonini and Poewe, 2014). Our study showed that caffeine, a non-specific antagonist of adenosine receptors, did not increase striatal dopamine, suggesting that caffeine promotes arousal but does not depend on dopaminergic systems. Moreover, large clinic studies showed that caffeine or coffee consumption has been associated with a reduced risk of PD (Ross et al., 2000; Ascherio et al., 2001). Hence, we propose that caffeine or A2AR antagonism could be a prospective agent for EDS therapy in PD.

Pharmacological, genetic, and clinical studies have demonstrated that striatal dopamine is involved in numerous behavioral and psychological processes that operate on the basis of wakefulness, including motor behaviors, attention, motivation, reward, and feeding. Dysregulation of nigrostriatal dopamine results in severe neurological disorders such as PD and Huntington's disease with diversified sleep disturbances. Our study demonstrated that striatal dopamine levels fluctuated across the spontaneous sleep–wake cycle and responded to external stimuli and wake-promoting stimulants. By understanding the dynamics of striatal dopamine under various conditions, our findings provide insight into the role of striatal dopamine in sleep regulation and suggest a potential treatment alternative for sleep disturbances in PD.

### DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the **Supplementary Files**.

# AUTHOR CONTRIBUTIONS

HD, JW, Y-FY, W-MQ, and Z-LH: conceptualization. HD and Y-FY: methodology, investigation, and formal analysis. HD, Y-FY, YS, W-MQ, and Z-LH: writing original draft. HD, JW, Y-FY, YS, W-MQ, and Z-LH: revised the manuscript. W-MQ and Z-LH: supervision and funding acquisition.

# FUNDING

This work was supported by the National Basic Research Program of China (Grant No. 2015CB856401 to Z-LH) and the National Natural Science Foundation of China (Grant Nos. 31530035 and<PHONE_NUMBER>5 to Z-LH, Grant Nos. 31871072, 31671099, and 31471064 to W-MQ).

# ACKNOWLEDGMENTS

We thank Dr. Ling Gong of Fudan University for technical assistance.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2019. 00242/full#supplementary-material

FIGURE S1 | Expression of dLight1.1 in the dorsal striatum in all mice except 2# which is in Figure 1D. The blue is DAPI, the green is dLight1.1. The scale bar is 200 µm.

FIGURE S2 | Striatal dLight1.1 fluorescence in response to light flash and predator odor. (A,D) Schematic showing light flash (A) or predator odor (D) was applied simultaneously with fiber photometry. (B) Up panel: Example traces of the fluorescence signal, EEG, and EMG before and after the onset of the light flash during sleeping. Down panel: (Left) The time course of the striatal dLight1.1 signal in response to light flash when mice were sleeping. (Right) Average fluorescence before and after onset of the light flash (t = 0.9181, P = 0.4555). (C) Up panel: Example traces of the fluorescence signal, EEG, and EMG before and after the onset of the light flash during awake. Down panel: (Left) The time course of the striatal dLight1.1 signal in response to light flash when mice were awake. (Right)

Average fluorescence before and after onset of the light flash (t = 4.486, P = 0.0463). (E) Up panel: example traces of the fluorescence signal, EEG, and EMG before and after the onset of air release during awake. Down panel: (Left) The time course of the striatal dLight1.1 signal in response to air delivery. (Right) Average fluorescence before and after onset of the air delivery (t = 0.4803, P = 0.6784). (F) Up panel: example traces of the fluorescence signal, EEG, and EMG before and after the onset of TMT delivery during awake. Down panel: (Left) The time course of the striatal dLight1.1 signal in response to TMT delivery. (Right) Average fluorescence before and after onset of the TMT delivery (t = 0.2858, P = 0.8019).

### REFERENCES

FIGURE S3 | The distributions of dLight fluorescence values in each state. The red presents REM sleep, the blue for NREM sleep, and the green for wake. The fluorescence in REM sleep was more divergent, but more convergent in NREM sleep. There was 3.14% dLight fluorescence values in REM sleep higher than 0.1, 10.26% values higher than 0.05, 71.48% values lower than 0, and 7.51% values lower than −0.1, whereas there was 0.57% dLight fluorescence values in NREM sleep higher than 0.1, 4.42% values higher than 0.05, 62.18% values lower than 0, 0.04% values lower −0.1. Although it looks more higher in REM sleep because there a few higher values, the mean of dLight fluorescence was the lowest in REM sleep.

extracellular dopamine tone. Proc. Natl. Acad. Sci. U.S.A. 111, E2751–E2759. doi: 10.1073/pnas.1407935111

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Dong, Wang, Yang, Shen, Qu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Acute Social Defeat Stress Increases Sleep in Mice

Shinya Fujii1 , Mahesh K. Kaushik1 , Xuzhao Zhou1 , Mustafa Korkutata1,2 and Michael Lazarus1 \*

1 International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan, 2 Ph.D. Program in Human Biology, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Japan

Social conflict is a major source of stress in humans. Animals also experience social conflicts and cope with them by stress responses that facilitate arousal and activate sympathetic and neuroendocrine systems. The effect of acute social defeat (SoD) stress on the sleep/wake behavior of mice has been reported in several models based on a resident-intruder paradigm. However, the post-SoD stress sleep/wake effects vary between the studies and the contribution of specific effects in response to SoD or nonspecific effects of the SoD procedure (e.g., sleep deprivation) is not well established. In this study, we established a mouse model of acute SoD stress based on strong aggressive mouse behavior toward unfamiliar intruders. In our model, we prevented severe attacks of resident mice on submissive intruder mice to minimize behavioral variations during SoD. In response to SoD, slow-wave sleep (SWS) strongly increased during 9 h. Although some sleep changes after SoD stress can be attributed to nonspecific effects of the SoD procedure, most of the SWS increase is likely a specific response to SoD. Slow-wave activity was only enhanced for a short period after SoD and dissipated long before the SWS returned to baseline. Moreover, SoD evoked a strong corticosterone response that may indicate a high stress level in the intruder mice after SoD. Our SoD model may be useful for studying the mechanisms and functions of sleep in response to social stress.

### Edited by:

Chiara Cirelli, University of Wisconsin–Madison, United States

### Reviewed by:

Janne Grønli, University of Bergen, Norway Peter Meerlo, University of Groningen, Netherlands

\*Correspondence:

Michael Lazarus<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Sleep and Circadian Rhythms, a section of the journal Frontiers in Neuroscience

Received: 26 October 2018 Accepted: 20 March 2019 Published: 03 April 2019

### Citation:

Fujii S, Kaushik MK, Zhou X, Korkutata M and Lazarus M (2019) Acute Social Defeat Stress Increases Sleep in Mice. Front. Neurosci. 13:322. doi: 10.3389/fnins.2019.00322 Keywords: social defeat stress, slow-wave sleep, homeostatic sleep need, mouse model, slow-wave activity

### INTRODUCTION

All living organisms respond to any external biological source of stress, i.e., perceived or actual threats, with a predictable biological pattern in an attempt to restore the internal homeostasis of the body (Chrousos, 2009). Acute stress typically involves transient responses of biological systems; however, prolonged activation of these initially beneficial reactions by excessive or chronically repeated stressors may lead to stress-related disorders. Social conflicts are a major source of stress for humans and play a major role in the pathogenesis of affective disorders like anxiety and depression (Charney and Manji, 2004; Huhman, 2006). Social conflicts also occur in animals (Toyoda, 2017). A social animal with the inability to dominate its opponent shows submissive behavior and accepts a lower social rank to avoid injury and death that could occur if the animal continues to act in an aggressive manner (Huhman, 2006). Rodent models of social defeat (SoD) stress are often based on a resident-intruder paradigm where the resident attempts to defend its home cage from the intruder who eventually subordinates itself to the unfamiliar territorial

resident conspecific. Acute SoD stress in rats has been shown to increase the heart rate and body temperature by sympathetic stimulation and blood corticosterone levels by activation of the hypothalamic–pituitary–adrenal axis (Koolhaas et al., 1997; Kataoka et al., 2014). These stress responses quickly dissipate and return to baseline levels within hours after the end of the stress period. SoD stress, however, also induces longer-lasting behavioral and physiologic changes. For example, several days may be required to normalize the circadian rhythmicity of body temperature, open-field behavior, and body mass growth after acute SoD (Meerlo et al., 1996a,b,c). Moreover, social animals are known to develop depression-like behaviors in response to chronically repeated SoD stress (Von Frijtag et al., 2000; van Kampen et al., 2002; Rygula et al., 2005; Huhman, 2006). For example, chronic SoD stress in mice induces anhedonia, anxiety, and social avoidance (Golden et al., 2011; Henriques-Alves and Queiroz, 2015). The anxiety and social avoidance behaviors in mice after chronic SoD stress can last several weeks (Berton et al., 2006; Tsankova et al., 2006; Krishnan et al., 2007). These longlasting behavioral changes in animals that experience repeated SoD stress are commonly associated with alterations in gene expression patterns in the brain (Tsankova et al., 2007).

The sleep/wake cycle is regulated by multiple factors, including homeostasis, circadian rhythm, and external environment, that are integrated by specific neuronal circuits controlling the sleep/wake behavior (Weber and Dan, 2016; Eban-Rothschild et al., 2017). Stress promotes wakefulness and inhibits sleep during the period when enhanced arousal is necessary to cope with external challenges. Alterations in the sleep architecture are often observed after an acute stress exposure (Sanford et al., 2015). A sleep rebound may be induced in animals to compensate for the sleep loss during stressful situations, but it is believed that sleep alterations are not only a homeostatic response to the sleep loss but also created by the stress. The type of sleep that is induced and the extent to which sleep stages are enhanced are highly variable between the types of stress or even the study design. For example, acute immobilization or restraint stress is followed by a selective increase in rapid eye movement sleep (REMS) (Rampin et al., 1991; Meerlo et al., 2001). Moreover, mice experiencing escapable foot shock stress showed an increase in REMS, whereas mice treated with inescapable foot shock stress showed a decrease in REMS (Sanford et al., 2010). Sleep is considered to have an important role in coping with stressful situations (Goldstein and Walker, 2014).

The acute effect of SoD stress on sleep has been reported in several rodent models. Although slow-wave activity (SWA) is consistently enhanced during sleep after SoD stress in mice and rats (Meerlo et al., 1997; Meerlo and Turek, 2001; Kamphuis et al., 2015; Henderson et al., 2017), the extent to which sleep stages are promoted varies between SoD studies and protocols. Male C57BL/6j mice when defeated by aggressive mice of the same strain during a 1-h interaction period showed a strong increase of slow-wave sleep (SWS) (Meerlo and Turek, 2001), whereas only a small increase of the SWS amount was observed in another study using the same SoD procedure (Vaanholt et al., 2003). This difference may be explained by variations in the aggressive behavior of the resident mice or prior stress experiences of the intruder mice in the laboratory environment. When highly aggressive male CD-1 mice were used in another study for the SoD of C57BL/6j mice during a 5-min interaction period followed by a 20-min period of olfactory, visual and auditory contact between the resident and intruder mice, a SWS increase in the intruder mice was preceded by an increase of wakefulness (Henderson et al., 2017). A fixed interaction period likely leads to painful attacks of the CD-1 mouse on the intruder mouse and thus, the sleep-wake behavior of the intruder mice may also be affected by pain. Overall, the extent to which the SoD procedure (e.g., pain or sleep deprivation) contributes to the post-SoD stress sleep/wake effects is not well established.

In the present study, we developed a mouse model of SoD stress based on a resident-intruder paradigm to evoke sleep alterations in the intruder C57BL/6j mouse after acute SoD stress by the resident CD-1 mouse trained to display persistent aggression against the intruder mouse. To prevent painful attacks to the submissive intruder mouse, the intruder and resident mice were separated by a partition when the intruder mouse showed clear submissive behavior and was attacked by the resident mouse. We found that SWS strongly increased in response to SoD, whereas REMS was only moderately increased for a limited period several hours after the SoD session.

### MATERIALS AND METHODS

### Animals

Male C57BL/6j mice (13–20 weeks old, and weighing 26– 33 g), maintained at the International Institute of Integrative Sleep Medicine of the University of Tsukuba, were used in the experiments. Male CD-1 (retired breeders) mice were obtained from Japan SLC (Hamamatsu, Japan). The animals were housed in an insulated and soundproof recording chamber maintained at an ambient temperature of 23 ± 0.5◦C with a relative humidity of 50 ± 5% and an automatically controlled 12 h light/12 h dark cycle (illumination intensity ≈ 100 lux). All animals had free access to food and water. This study was performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the US National Institutes of Health (2011). Experimental protocols were in compliance with relevant Japanese and institutional laws and guidelines, and approved by the University of Tsukuba animal ethics committee (protocol #14-322). Every effort was made to minimize the number of animals used as well as any pain and discomfort experienced by the animals.

### Stereotaxic Surgery for Placement of the EEG/EMG Electrodes

Mice were anesthetized with pentobarbital (50 mg/kg, intraperitoneal [i.p.]) and then placed in a stereotaxic apparatus. Electroencephalogram (EEG) and electromyogram (EMG) electrodes for polysomnographic recordings were chronically implanted in the mice (Oishi et al., 2016). The implant comprised two stainless steel screws (1 mm in diameter) that served as the EEG electrodes inserted through the skull above the cortex (anteroposterior, +1.0 mm; left-right, −1.5 mm from bregma or lambda) according to the atlas of Paxinos and Franklin (2004). Two insulated, stainless steel Teflon-coated wires, serving as the EMG electrodes, were placed bilaterally into both trapezius muscles. All electrodes were attached to a micro connector and fixed to the skull with dental cement.

### Stress Procedures

fnins-13-00322 April 1, 2019 Time: 18:4 # 3

The SoD stress protocol was designed based on the residentintruder paradigm. To prepare aggressive resident mice, male CD-1 mice (body weight > 40 g) were singly housed and trained to display persistent aggression against male C57BL/6j mice by placing a male C57BL/6j mouse in the cage of a CD-1 mouse one or two times a week. During the training, the C57BL/6j mouse was immediately removed from the cage when attacked and defeated by the CD-1 mouse. Unsuitable CD-1 mice that showed little aggression or extreme violent behavior (i.e., mice that inflict serious injuries to their opponent) were excluded from the study. Only CD-1 mice showing aggression against C57BL/6j mice within 1 min were used in the experiments to ensure successful SoD. The C57BL/6j mice used for aggression training were not used for any SoD experiments.

For SoD stress, we used a transparent acrylic partition placed diagonally in a rectangular cage to separate the intruder and resident mice. The partition has a wire-mesh opening in the lower part for olfactory, visual, and auditory contact (**Figures 1B,C**). A SoD session lasted 60 min starting at zeitgeber time 11. The C57BL/6j mouse (intruder mouse) was placed behind the partition in the resident mouse's cage. After 5 min, the partition was removed and re-inserted when the intruder mouse was attacked by the resident mouse and showed clear submissive behavior, including submissive posture, escaping, or freezing behavior. During one SoD session, the removal of the partition was repeated two, four, or eight times at 25-, 15-, or 7-min intervals, respectively. After the SoD session, the intruder mouse was returned to its home cage (**Figure 1D**).

The following experiments were also conducted to differentiate the specific effects of SoD and non-specific effects of the SoD procedure (**Figure 1E**): (1) C57BL/6j mice were placed in an unused cage with bedding, food pellets, and a partition for 60 min and sleep deprived by cage tapping ("Sleep deprivation" session). (2) C57BL/6j mice were placed in a cage with a partition previously used by a CD-1 mouse for more than 5 days ("No resident" session). (3) The intruder mouse was placed in the resident cage while the intruder and resident mice were separated by the partition for 60 min ("No contact" session). Sleep deprivation by cage tapping was not conducted during the "No resident" and "No contact" sessions.

### Vigilance State Assessment Based on EEG/EMG Polygraphic Recordings

One week after surgery, the mice were individually housed in cages in an insulated soundproof recording chamber and connected to the EEG-EMG recording cables for 2–3 days of habituation before starting the polygraphic recordings. Before the SoD session, some mice were subjected to a "Sleep deprivation" or "No resident" session, followed by a "No contact" session. Sessions were run every 3 or 4 days. Baseline was recorded on the day prior to each session (**Figure 1A**). The EEG/EMG signals were amplified, filtered (EEG 0.5–30 Hz; EMG 20–200 Hz), digitized at a sampling rate of 128 Hz, and recorded using the data acquisition software SleepSign R (Kissei Comtec, Matsumoto, Japan). The vigilance states were classified offline in 10-s epochs of three stages, i.e., wakefulness, REMS, and SWS by SleepSign R (ver 3.4) according to standard criteria (Oishi et al., 2016). As a final step, the software-defined vigilance stage of each 10 s epoch was visually examined, and manually corrected when necessary. Spectral analysis of EEG by fast Fourier transform was performed, and the EEG power densities were calculated in the range of 0.5–25 Hz in 0.5-Hz bins. SWA during SWS was calculated based on EEG power in the range of 0.5– 4 Hz. In a 3-hourly plot of SWA, the data were presented as percentages of the mean of the 12-h baseline SWS during the dark period.

### Behavioral Analysis

Animal behaviors during "No resident" and "No contact" sessions were analyzed using video recordings. Vigilance states and behaviors during wakefulness were scored in 4-s epochs. Behaviors were scored as grooming, exploration (including ambulation, rearing, digging, and sniffing), consumption (eating and drinking), or quiet waking, when the behavior accounted for more than 50% of the epoch.

### Blood Sampling and Corticosterone Measurement

Blood sampling was performed by cardiac puncture with the mice under deep isoflurane anesthesia immediately after the SoD sessions with 2, 4, or 8 defeats. Blood samples were obtained within 2 min, which is rapid enough to ensure that the stress imposed by the blood-sampling procedure did not affect the corticosterone levels in the plasma (Riley, 1960). For the control group, mouse blood was collected immediately after the "No resident" session. For basal corticosterone levels, blood was collected from undisturbed mice in their home cages at zeitgeber time 12. All the mice were used only once and not subjected to any behavioral tests. Blood samples were collected into EDTAcoated syringes and immediately centrifuged at 10,000 rpm for 15 min at 4◦C. Plasma samples were collected and stored at −80◦C until the assay was performed. Plasma corticosterone was measured in duplicate wells using the DetectX R ELISA kit (Arbor Assays, Ann Arbor, MI, United States). The mean of intraassay coefficients of variations (n = 31) calculated from duplicate wells was 2.82%.

### Statistical Analysis

Statistical analyses were carried out using GraphPad Prism software (GraphPad Software, La Jolla, CA, United States). All results are presented as mean ± standard error of the mean (SEM). A paired two-tailed Student's t-test was used for statistical comparisons between paired groups (**Figures 2B,C**, **3A**, **4A–C,E** and **Tables 2**, **4**). Two-way repeated-measures analysis of variance

(ANOVA) followed by Bonferroni's post hoc comparisons was used to analyze the sleep/wake profile (**Figure 2A**) and EEG power (**Figures 4D,F**). One-way ANOVA followed by Bonferroni's (**Figures 2D–F**) or Dunnett's (**Figures 3B**, **4E**) post hoc comparisons were performed to compare groups of three or more. The Kruskal–Wallis test was used to analyze the data with significant variance heterogeneity assessed by Levene's test (**Figure 2D**). In all cases, P < 0.05 was considered significant.

### RESULTS

### SoD Stress Strongly Increases Sleep

We established a mouse model of sleep alterations after acute SoD stress based on a resident-intruder paradigm, whereby a C57BL/6j male mouse was introduced as an intruder to a resident CD-1 male mouse selected for its high level of aggression (**Figure 1**). To prevent injury to the intruder mouse, the intruder and resident mice were separated by a partition. During a 1-h SoD session, the partition was removed multiple times and reinserted every time when the intruder mouse was attacked and showed submissive behavior. After the SoD session, the intruder mouse was returned to the home cage and EEG and EMG of the intruder mice were recorded.

First, we investigated the extent to which sleep was affected by different numbers of defeats during a 1-h session. Intruder mice were exposed to two, four, or eight successive defeats. In each successive defeat, mice were in contact with the resident mice for 15.5 ± 1.5 s. We analyzed EEG and EMG recordings made after the SoD session or on the previous day (baseline). SWS strongly increased from the first 3 h after the sessions with two, four, and eight defeats. The strong increase in SWS lasted until 6 or 9 h after the sessions with two or four defeats, respectively. Accordingly, wakefulness was decreased during the corresponding periods. A REMS increase was observed only between 6 and 9 h after the SoD session with four successive defeats (**Figure 2A** and **Table 1**). When the first 3 h following the SoD sessions were analyzed in more detail, an increase in wakefulness only occurred during the first 10 min after the SoD sessions and SWS started to increase afterward (**Figure 2B**). Because most of the effects on sleep stages were observed within 9 h after the sessions, we compared the total amounts of 9 h following the SoD sessions (**Figure 2C** and **Table 2**). The total baseline amounts of the vigilance states during the same time period were not significantly different among groups [SWS: P = 0.4914, F(2,20) = 0.7364, REMS: P = 0.0761, F(2,20) = 2.938, Wake: P = 0.3578, F(2,20) = 1.083]. SWS significantly increased after SoD sessions, whereas wakefulness decreased accordingly. By contrast, REMS was only increased significantly after four defeats.

FIGURE 2 | Slow-wave sleep (SWS) increased after social defeat (SoD) stress. (A) Time-course of SWS, rapid eye movement sleep (REMS), and wakefulness for 21 h after SoD sessions with two (n = 8), four (n = 8), or eight (n = 7) defeats. ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001 compared with baseline, assessed by two-way repeated measures ANOVA followed by Bonferroni's post hoc comparisons. (B) Time-course of SWS, REMS, and wakefulness for 3 h after SoD sessions. ∗P < 0.05 compared with baseline, assessed by paired two-tailed Student's t-test. (C) Total amounts of SWS, REMS, and wakefulness for 9 h after SoD sessions. ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001 compared with baseline, assessed by paired two-tailed Student's t-test. (D) Changes in total amounts of SWS, REMS, and wakefulness for 9 h between baseline and after SoD sessions. ∗P < 0.05, compared between groups, assessed by the Kruskal–Wallis test (SWS and wakefulness) or one-way ANOVA (REMS) followed by Bonferroni's post hoc comparisons. (E) Deviation of changes in total amounts of SWS and wakefulness. The deviation was calculated by subtracting the mean from the experimental values of each animal for changes in the total amounts of SWS and wakefulness for 9 h in each condition. ∗P < 0.05 and ∗∗P < 0.01, compared between groups, assessed by one-way ANOVA followed by Bonferroni's post hoc comparisons. (F) Blood plasma corticosterone levels from undisturbed mice (n = 6) at zeitgeber time 12 of the baseline day or mice after a "No resident" (n = 7) or SoD sessions (n = 6). ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001 compared between groups, assessed by one-way ANOVA followed by Bonferroni's post hoc comparisons. Data are presented as means ± SEM. NS, not significant.

Moreover, the calculated changes in the total amounts of SWS, REMS, and wakefulness between baseline and the post-SoD 9-h periods revealed a significant difference in REMS only between four and eight times (**Figure 2D** and **Table 1**). Because variance in SWS or wakefulness was highly significantly different among groups, we performed Bonferroni's post hoc comparisons on the absolute deviations from the mean (**Figure 2E**). The deviations were significantly lower only in the intruder group with four defeats. Finally, we studied corticosterone responses in the intruder mice after SoD as an indicator of stress due to activation of the hypothalamic-pituitary-adrenal axis, the central stress response system (Mormède et al., 2007). Blood samples for measuring corticosterone levels were collected from undisturbed mice (to establish the baseline) and intruder mice immediately after the SoD sessions. A control session in a cage previously used by the resident mouse ("No resident") was also conducted. SoD sessions significantly increased corticosterone levels and it was significantly stronger compared with "No resident" mice [P < 0.0001, F(4,26) = 24.5, one-way ANOVA followed by Bonferroni's post hoc comparisons, **Figure 2F**]. These results may indicate that SoD causes stress in mice. However, there was no significant difference between SoD sessions with different

FIGURE 4 | Sleep architecture after social defeat (SoD) stress. (A–C) Episode numbers (A), mean episode durations (B), and numbers of stage transitions (C) for 9 h after SoD session with four defeats. ∗P < 0.05 and ∗∗P < 0.01 compared with baseline, assessed by paired two-tailed Student's t-test. (D) Power density of EEG during SWS for 30 min after SoD session with four defeats or onset of the dark phase on the baseline day. ∗P < 0.05 compared with baseline, assessed by two-way repeated measures ANOVA followed by Bonferroni's post hoc comparisons. (E) Changes in the SWA (0.5–4 Hz) relative to the baseline for 30 min after a "Sleep deprivation," "No resident," "No contact," or SoD session. ∗P < 0.05 compared with baseline, assessed by paired two-tailed Student's t-test, #P < 0.05 compared with SoD, assessed by one-way ANOVA followed by Dunnett's post hoc comparisons. (F) Time-course of SWA changes after SoD session. ∗∗∗P < 0.001 compared with baseline, assessed by two-way repeated measures ANOVA followed by Bonferroni's post hoc comparisons.

numbers of successive defeats. These results suggested that the SWS increase observed after SoD stress is mostly independent of the numbers of defeats.

### Specific Effects of SoD Versus Non-specific Effects of the SoD Procedure

To differentiate between the specific effects of SoD and non-specific effects of the SoD procedure (e.g., novelty or sleep deprivation), the sleep/wake behavior of mice after SoD stress was compared with that following other conditions, including sleep deprivation in a clean unused cage ("Sleep deprivation"), a cage previously used by the resident mouse ("No resident"), or the presence of the resident mouse separated by a partition ("No contact"). Data of the control conditions were compared with those of four successive defeats, because SWS consolidation occurred with significantly less variation after this SoD procedure than the ones with two or eight defeats.

During the "No resident" or "No contact" session, the animals were spontaneously active and awake during most of the 1-h session (**Table 3**). "Sleep deprivation" and "No contact" sessions significantly increased SWS and decreased wakefulness during a 9-h period after the sessions (**Figure 3A** and **Table 4**); however, the SWS increase was smaller than that after SoD stress (**Figure 3B**). SWS was not changed after the "No resident" session (**Figure 3A**). REMS also increased after the SoD and control procedures; however, there was only a significant difference between the baseline and experimental

### TABLE 1 | Statistics of data shown in Figures 2A,B, 3B.

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periods after SoD stress (**Figure 3A**). These results suggest that a significant part of the sleep changes after SoD can be attributed to non-specific sleep deprivation during the SoD session, i.e., about half of the increase of SWS and all of the increase of REM sleep may be a homeostatic sleep response to non-specific sleep deprivation.

### SoD Stress Increases the Number of SWS Episodes and Slow-Wave Activity

The sleep architecture and SWA were then analyzed after SoD session with four successive defeats. SoD stress significantly affected the episode number of SWS and wakefulness for 9 h after the SoD session (SWS: P = 0.0079, Wake: P = 0.0077, paired Student's t-test, **Figure 4A**). The mean duration of wake episodes decreased by 48.4 ± 6.9% (P = 0.0035, paired Student's t-test) compared with the baseline, whereas the duration of the SWS or REMS episodes was not significantly different or slightly increased, respectively (**Figure 4B**). The number of stage transitions from SWS to wake and wake to SWS was significantly increased (SWS to Wake: P = 0.0131, Wake to SWS: P = 0.0076, paired Student's t-test), whereas other stage transitions were not affected by SoD stress (**Figure 4C**).

To assess whether EEG activity was altered by SoD stress, we compared the normalized EEG power spectrum of SWS for 30 min following the SoD session with baseline SWS. EEG activity was significantly increased in the frequency range of 1–4 Hz and decreased in the frequency range of 4.5–10 Hz during SWS [interaction P < 0.0001, F(49,350) = 25.47, two-way repeated-measures ANOVA followed by Bonferroni's post hoc comparisons, **Figure 4D**]. SWA in the frequency range of 0.5–4 Hz was then calculated during the same period and compared with the "Sleep deprivation," "No resident," and "No contact" conditions. SWA was significantly increased in "No resident," "No contact," and SoD mice, although the increase of SWA in the SoD mice was significantly stronger than that in the "No resident" mice, but not in the "No contact" mice [P = 0.0176, F(3,20) = 4.258, oneway ANOVA followed by Dunnett's post hoc comparisons, **Figure 4E**]. Finally, we calculated the 3-h SWAs in intruder mice for 12 h following SoD stress and found that the first 3-h SWA was significantly higher than the baseline SWA on the previous day [main effect P = 0.0005, F(1,28) = 15.66, interaction P = 0.0002, F(3,28) = 8.991, two-way repeatedmeasures ANOVA followed by Bonferroni's post hoc comparisons, **Figure 4F**].

### DISCUSSION

The present study explored the effect of acute SoD stress on the sleep/wake cycle in mice. We observed that SoD stress strongly promoted SWS over 9 h. Corticosterone, as an indicator of stress, was significantly increased in the blood plasma by SoD, confirming that SoD induced stress in the mice. The increase in SWS was associated with more transitions from wakefulness to SWS without changing the mean episode duration of SWS.

The effect of acute SoD stress on subsequent sleep amount was reported in several studies. One study showed strong SWS increase in mice after acute SoD stress by interaction for 1 h with mice from the same strain (Meerlo and Turek, 2001). By contrast, another study using the same procedure showed little

∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001 compared with baseline, assessed by paired two-tailed Student's t-test.

TABLE 3 | Percentage of animal behaviors (during wakefulness), slow-wave sleep (SWS), and rapid eye movement sleep (REMS) during the "No resident" and "No contact" sessions.

effect of SoD on the SWS amount (Vaanholt et al., 2003). Due to the lack of information about the number and duration of attacks of the resident mice against the intruder mice, it is hard to explain the difference between the studies. Moreover, it was also reported that wakefulness is increased in the first 3 h after SoD followed by a sleep increase during later hours (Henderson et al., 2017). Highly aggressive male CD-1 mice were used in this study for the SoD of C57BL/6 mice during a 5-min interaction period followed by a 20-min period of olfactory, visual, and auditory contact between the resident and intruder mice. The initial increase of wakefulness in this study may be attributed to repeated painful attacks of the CD-1 mice against smaller C57BL/6 mice. Alternatively, the durations of physical and sensory stress (25 min) may be too short to induce sleep. Although variations in the aggressive behavior of the resident mice may account to some extent for the differences in the sleep/wake responses between the studies, sleep architecture, SWA, and corticosterone responses in socially defeated mice may also be influenced by other factors. For example, laboratory environment and time-of-theday when the conflict was introduced may affect sleep and corticosterone parameters. Moreover, prior stress experiences of the intruder mice such as early life stress during maternal care, fighting between littermates, or low social ranking in the litter can also be important factors for outcomes after SoD stress (Wang et al., 2014; Peña et al., 2017). We designed a SoD stress protocol to minimize behavioral variations during SoD. In our protocol, intruder mice were separated from aggressive resident mice (CD-1 mice) when they exhibited submissive behaviors rather than by setting a fixed duration of interaction with the resident mice, preventing repeated and painful attacks against the submissive mouse. Moreover, physical interaction was conducted two, four or eight times to maintain a high level of SoD stress in the intruders during the session. We found that acute SoD stress, introduced at the end of the light period, increases SWS shortly after the SoD session. Moreover, the comparison of the specific effects of SoD and non-specific effects of the SoD procedure showed that a large portion of the sleep changes after SoD stress can be attributed to a homeostatic response to sleep deprivation. On the other hand, about half of the SWS increase is likely a specific response to SoD.

We also observed a strong increase in SWA after the SoD session (**Figures 4E,F**), consistent with previous reports in mice and rats (Meerlo et al., 1997; Meerlo and Turek, 2001; Kamphuis et al., 2015; Henderson et al., 2017). An increase in SWA is considered the hallmark of homeostatic sleep need in response to sleep deprivation (Suzuki et al., 2013; Dispersyn et al., 2017; Wang et al., 2018). The SWA increase, however, was stronger after the SoD condition than under sleep-disrupting control conditions without a CD-1 mouse. Moreover, SWS was still enhanced long after the SWA returned to the baseline level. These observations suggest that SWA is also affected by social stress and at least part of the SWS in response to SoD is independent of SWA.

Moreover, we observed large variations in the SWS increase after the SoD sessions, especially the ones with two and eight successive defeats (**Figures 2D,E**). It is well known that grouphoused male mice establish a social hierarchy (Wang et al., 2014). As we did not control the social ranking of the intruder mice before the SoD session, it is possible that the SWS response of the intruder mice is affected by their SoD resilience.

The neuronal mechanisms of the sleep response to SoD stress are unknown. The mesolimbic dopamine system plays an important role in the development of depression-related

TABLE 4 | Total amounts (minutes, mean ± SEM) of slow-wave sleep (SWS), rapid eye movement sleep (REMS), and wakefulness for 6, 9, 12, and 24 h after "Sleep deprivation," "No resident," and "No contact" sessions.

∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001 compared with baseline, assessed by paired two-tailed Student's t-test.

behaviors after repeated SoD stress (Russo and Nestler, 2013). The mesolimbic system comprises dopaminergic projections from the ventral tegmental area to the nucleus accumbens, an area critical for reward and motivation (Graybiel, 2008; Russo and Nestler, 2013), and is also connected to the medial prefrontal cortex, amygdala, and hippocampus, allowing the system to integrate cognition, emotion, and action (Floresco, 2015). We and others recently described the role of the mesolimbic system in the gating of sleep by motivated behavior (Eban-Rothschild et al., 2016; Oishi et al., 2017a,b; Luo et al., 2018). Therefore, it is plausible that the mesolimbic system also mediates sleep responses to SoD stress.

Acute stress responses induce behavioral, sympathetic and neuroendocrine changes in animals to facilitate a fight or flight response (Chrousos, 2009). Sympathetic and neuroendocrine responses, including the increase of body temperature and heart rate and secretion of noradrenaline, adrenaline, and corticosteroids, usually dissipate within hours after acute SoD in rodents (Koolhaas et al., 1997, 2017). Sleep is known to inhibit glucocorticoid secretion in healthy men (Weitzman et al., 1983), whereas REMS disinhibition and dysregulation of hypothalamic-pituitary-adrenal axis are hallmarks of depression and chronic stress (Nollet et al., 2019; Steiger and Pawlowski, 2019). The increased SWS after SoD stress in our study may facilitate the termination of stress responses. Moreover, SWS induction may also be beneficial for restoration of brain homeostasis after the stress response (Xie et al., 2013; Ding et al., 2016). By contrast, REMS is considered to have an important function in the processing of emotional memory of adverse experiences to promote emotional and mental recovery (Suchecki et al., 2012; Goldstein and Walker, 2014). Only a moderate REMS increase was observed during a period 6–9 h after SoD sessions with four defeats (**Figure 2A**). Most, if not all, of the REMS increase, however, may be attributed to non-specific sleep deprivation during the SoD session and thus, a beneficial effect of REMS after SoD remains unclear.

In summary, our study revealed strong SWS responses to acute SoD stress in submissive male mice attempting to appease their aggressive counterpart, when painful attacks on the submissive mice were prevented. Our SoD stress model may be useful for studying the mechanisms and functions of sleep in response to social stress.

### DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the supplementary files.

### AUTHOR CONTRIBUTIONS

SF and ML designed the experiments. SF, MKK, XZ, and MK collected and analyzed the data. SF and ML wrote the manuscript.

# FUNDING

This work was supported by the Japan Society for the Promotion of Science [Grant in-Aid for Scientific Research B (Grant No. 17H02215) to ML]; the Japan Science and Technology Agency [CREST grant (Grant No. JPMJCR1655) to ML]; the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan [Grants-in-Aid for Scientific Research on Innovative Areas "Living in Space" (Grant Nos. 15H05935, 15K21745, and 18H04966) and "WillDynamics" (Grant No. 17H06047) to ML]; the World Premier International Research Center Initiative (WPI) from MEXT (to ML); and a research grant of the Astellas Foundation for Research on Metabolic Disorders (to ML).

### ACKNOWLEDGMENTS

We thank all of the laboratory members for technical assistance, discussions, and comments.

# REFERENCES

fnins-13-00322 April 1, 2019 Time: 18:4 # 11

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Fujii, Kaushik, Zhou, Korkutata and Lazarus. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Whole-Brain Neural Connectivity to Lateral Pontine Tegmentum GABAergic Neurons in Mice

Ze-Ka Chen1† , Xiang-Shan Yuan1† , Hui Dong1† , Yong-Fang Wu2 , Gui-Hai Chen2 , Miao He1 , Wei-Min Qu1 and Zhi-Li Huang1 \*

1 State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, China, 2 Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, China

The GABAergic neurons in the lateral pontine tegmentum (LPT) play key roles in the regulation of sleep and locomotion. The dysfunction of the LPT is related to neurological disorders such as rapid eye movement sleep behavior disorder and ocular flutter. However, the whole-brain neural connectivity to LPT GABAergic neurons remains poorly understood. Using virus-based, cell-type-specific, retrograde and anterograde tracing systems, we mapped the monosynaptic inputs and axonal projections of LPT GABAergic neurons in mice. We found that LPT GABAergic neurons received inputs mainly from the superior colliculus, substantia nigra pars reticulata, dorsal raphe nucleus (DR), lateral hypothalamic area (LHA), parasubthalamic nucleus, and periaqueductal gray (PAG), as well as the limbic system (e.g., central nucleus of the amygdala). Further immunofluorescence assays revealed that the inputs to LPT GABAergic neurons were colocalized with several markers associated with important neural functions, especially the sleep-wake cycle. Moreover, numerous LPT GABAergic neuronal varicosities were observed in the medial and midline part of the thalamus, the LHA, PAG, DR, and parabrachial nuclei. Interestingly, LPT GABAergic neurons formed reciprocal connections with areas related to sleep-wake and motor control, including the LHA, PAG, DR, parabrachial nuclei, and superior colliculus, only the LPT-DR connections were in an equally bidirectional manner. These results provide a structural framework to understand the underlying neural mechanisms of rapid eye movement sleep behavior disorder and disorders of saccades.

Keywords: GABA, lateral pontine tegmentum, viral neuronal tracing, reciprocal connections, sleep-wake cycle, locomotion

# INTRODUCTION

The pontine tegmentum, a brainstem structure, are all the regions from the basilar pons to the fourth ventricle. It plays important roles in perception, movement, vigilance, respiration, and the sleep-wake regulation (Plazzi et al., 1996; Alheid et al., 2004; Tziridis et al., 2012). The lateral pontine tegmentum (LPT) is the dorsolateral part of the pontine tegmentum, which is included in the deep mesencephalic nucleus (DpMe). The LPT is identified as the site between the ventral lateral periaqueductal gray (vlPAG) and the pedunculopontine tegmental nucleus (PPT) on the

### Edited by:

Takeshi Sakurai, University of Tsukuba, Japan

### Reviewed by:

Christelle Peyron, INSERM U1028 Centre de Recherche en Neurosciences de Lyon, France Michihiro Mieda, Kanazawa University, Japan

> \*Correspondence: Zhi-Li Huang<EMAIL_ADDRESS>†These authors have contributed

equally to this work

### Specialty section:

This article was submitted to Sleep and Circadian Rhythms, a section of the journal Frontiers in Neuroscience

Received: 08 January 2019 Accepted: 01 April 2019 Published: 24 April 2019

### Citation:

Chen Z-K, Yuan X-S, Dong H, Wu Y-F, Chen G-H, He M, Qu W-M and Huang Z-L (2019) Whole-Brain Neural Connectivity to Lateral Pontine Tegmentum GABAergic Neurons in Mice. Front. Neurosci. 13:375. doi: 10.3389/fnins.2019.00375

ventro-dorsal localization, and is a trip lying the region medial to the PPT on the rostro-caudal extent in rodents (approximately −4.04 to −5.02 mm corresponding to Bregma in the mouse brain atlas) (Franklin and Paxinos, 2001; Lu et al., 2006; Sherman et al., 2015). The LPT mainly contains glutamatergic and GABAergic neurons (Liang et al., 2014).

The LPT is involved in voluntary locomotion, general anesthesia, and even incentive learning (Sukhotinsky et al., 2005; Fogel et al., 2010; Le Ray et al., 2011). Dysfunction of the LPT is associated with neurological disorders such as rapid eye movement (REM) sleep behavior disorder (RBD), saccade disorders and temporal lobe epilepsy (TLE). Using magnetic resonance imaging, diffusion changes of visualized structural abnormalities were found in the areas including the LPT in patients with idiopathic RBD (Scherfler et al., 2011). Ocular flutter was found to be related to the right upper pontine tegmentum damage, which contained LPT neurons (Tsutsumi et al., 2009). LPT dysfunction in patients with TLE was found to contribute to neurocognitive disturbances (Englot et al., 2018). In addition to the above, LPT neurons are especially known to have a vital function in REM sleep regulation in rodents (Sapin et al., 2009) and demyelination of LPT neurons led to dream-like hallucinations during wake, relating to the impairment of REM sleep inhibitory mechanisms (Vita et al., 2008). Furthermore, c-fos, a marker of activated neurons, was strongly expressed in the LPT after REM sleep deprivation and colocalized with glutamate decarboxylase, a key enzyme for the production of GABA, suggesting that LPT GABAergic neurons are involved in REM sleep regulation (Lu et al., 2006; Sapin et al., 2009).

The neuronal connectivity of LPT neurons has previously been investigated using classic anterograde tracers, such as biotinylated dextran amine (BDA), and retrograde tracers, such as horseradish peroxidase or cholera toxin b-subunit (CTB). Consequently, the LPT was shown to receive inputs from the lateral hypothalamic area (LHA) (Clement et al., 2012), the pallidum (Gorbachevskaia, 2011), and the sublaterodorsal nucleus (SLD) (Boissard et al., 2003), while projecting to the LHA (Ford et al., 1995), superior colliculus (SC) (Appell and Behan, 1990), and SLD (Liang and Marks, 2014). However, specific afferent inputs and efferent outputs of LPT GABAergic neurons remain unknown.

Recent genetic tracing methods using the retrograde trans-synaptic rabies virus (RV) allow us to identify the presynaptic connections of a genetically well-defined neuronal population (Wickersham et al., 2007; Yuan et al., 2018). Moreover, the use of adeno-associated virus (AAV) vectors allows cell-type-specific, non-toxic, long-term expression of transgenes for anterograde tracing of neuronal pathways (Kuhlman and Huang, 2008; Holloway et al., 2013; Zhang et al., 2013). Here, we employed a modified RV and AAV in combination with the Cre/LoxP system to map presynaptic inputs and output projections of LPT GABAergic neurons. We identified several monosynaptic inputs of LPT GABAergic neurons by double labeling with antibodies for molecular markers related to sleep-wake regulation. Our results revealed the wide-ranging connectivity of LPT GABAergic neurons and provided a structural framework to understand the underlying neural circuits of important physiological functions.

# MATERIALS AND METHODS

### Animals

The viral-based tracing experiments were all performed in male GAD2-IRES-Cre mice obtained from Miao He and Z. Josh Huang's laboratory, which generated the strain on a mixed genetic background (129SVj/B6; with black coat color) to genetically target almost whole-brain GABAergic neurons with a high level of specificity (Taniguchi et al., 2011). The mice were bred and housed under an automatically controlled 12-h light/12-h dark cycle (lights on at 7 a.m.; 100 lux intensity) (Zhang et al., 2017). After surgery, the mice were allowed to recover for at least 2 weeks before further experiments. This study was carried out in accordance with the recommendations of the China Regulations on the Administration of Laboratory Animals (the Decree No. 2 of National Science and Technology Commission of the People's Republic of China) and all animal procedures were approved by the Medical Experimental Animal Administrative Committee of the School of Basic Medical Sciences (Permit No. 20140226-024), Fudan University (Shanghai, China).

# Virus

All the viral vectors were packaged by BrainVTA (BrainVTA, Co., Ltd., Wuhan, China). The titer of the EnvA-pseudotyped, glycoprotein (RG)-deleted, DsRed-expressing rabies virus (RV-EnvA-1RG-DsRed) was about 2 × 108 infecting units per mL. The three AAV vectors, namely AAV-EF1α-DIO-ChR2-mCherry, AAV-EF1α-DIO-TVA-GFP, and AAV-EF1α-DIO-RV-G, were all packaged into the 2/9 AAV serotype and titred at about 3 × 1012 genome copies per mL (Yuan et al., 2018).

# Surgery and Viral Injections

Totally, 28 mice were injected with viruses. Only eight animals were correctly injected after histological confirmation and were included in the results. For virus injection, adult GAD2-IRES-Cre mice were anesthetized with ∼1.5% isoflurane in oxygen (flow rate of 1 L/min). Using the Nanoject II (Drummond Scientific, Broomall, PA), 23 nL AAV9-EF1α-DIO-TVA-GFP and AAV9-EF1α-DIO-RvG was stereotaxically injected (4.6 nL/s) via a micropipette into the unilateral LPT (anterior–posterior: −4.2 mm, medial-lateral: +1.0 mm, dorsal-ventral: −3.5 mm) (Krenzer et al., 2011). Following the injection, the pipette was held in place for an additional 10 min to allow diffusion of viral particles away from the injection site before being slowly withdrawn. Two weeks later, 46 nL volume of RV-EnvA-1RG-DsRed was prepared to inject into the previous site of the LPT (n = 4 mice). For anterograde tracing, AAV-EF1α-FLEX-ChR2-mCherry was injected into the LPT following the same procedures described above. After 3 weeks, these mice were perfused (n = 4 mice).

### Histology and Immunostaining

fnins-13-00375 April 17, 2019 Time: 16:25 # 3

After being deeply anesthetized, adult mice were transcardially perfused with cold normal saline followed by 4% paraformaldehyde in 0.1 M phosphate buffer (PB). The brains were then extracted, post-fixed for 6 h, and then incubated in 20% sucrose in PB at 4◦C until they sank. Coronal sections (30 µm) were cut from a fixed brain on a freezing microtome (CM1950, Leica, Germany) into four series to obtain groups of tissue for multiple manipulations, and the distance between sections in each group was 90 µm. Tissue groups were restored in a cryopreservative solution.

For immunofluorescence, sections were rinsed with 0.3% Triton X-100 in 0.01 M PBS, and then they were incubated by primary antibodies [goat anti-choline acetyltransferase (ChAT), 1: 1000, Millipore AB144P; polyclonal rabbit anti-GABA, 1:1000, Acris Antibodies 20094; polyclonal rabbit anti-5-hydroxytryptamine (5-HT), 1:3000, Sigma S5545; polyclonal rabbit anti-calretinin, 1:2000, Invitrogen MA5-14540; polyclonal goat anti-parvalbumin, 1:3000, Swant PVG213; polyclonal goat antibody against orexin A, SCB sc8070, 1:2000; polyclonal goat antibody against melanin-concentrating hormone (MCH) sc-14509, SCB,1:1000] in PBS containing 0.3% Triton X-100 (PBST) overnight at 4◦C.  Next, the sections were incubated for 10 min to stain nuclei by 40 ,6-diamidino-2 phenylindole (DAPI, 1: 10000, Sigma-Aldrich D9542) and rinse washed three times. Finally, the sections were mounted on glass slides and cover-slipped. For the immunostaining of mCherry, free-floating sections were rinsed in PBS and incubated by primary antibodies (polyclonal rabbit anti-mCherry 1:3000, Clontech 632496) in PBST and waggled slowly overnight at 4 ◦C. After washing, sections were incubated with secondary antibodies (biotinylated goat anti-rabbit IgG, 1:1000, Vector Laboratories BA-1000) and then incubated with an avidin-biotin peroxidase complex (ABC) solution (1:1000, Vector Laboratories PK-6100) for 1 h. After washing, the sections were immersed in a 3,3-diaminobenzidine-4 HCl (DAB, Vector Laboratories SK-4100) for 5–10 min at room temperature, after which mCherry-immunoreactive neurons could be identified by the presence of brown reaction product. Finally, the sections were mounted on glass slides, dried, dehydrated, and cover-slipped (Chen et al., 2016; Yuan et al., 2017; Luo et al., 2018).

### Imaging and Analysis

Whole-brain sections were imaged by a 10 or 20× objective on the VS120 virtual microscopy slide scanning system (Olympus) and magnified images of brain sections were captured using a 20 or 40× objective by a confocal microscope, to obtain more detail (Olympus Fluoview 1000, Tokyo, Japan).

For cell mapping of neurons, neural bodies were quantified semi-automatically using ImageJ software. Based on mouse brain map, ImageJ was used to manually depict the boundaries of specific brain regions (Franklin and Paxinos, 2001). For starter cell mapping, we first applied ImageJ to distinguish the cells coexpressing DsRed and GFP as starter cells, then generated cell representation by applying the automatic wand (tracing) tool and bicubic interpolation to maximize neuronal fidelity. Next, we inverted the colorless areas to white and matched the cellular outlines to the appropriate brain regions based on the mouse brain atlas (**Figure 2A**). Starter cells were binned at 0.12 mm along the anterior–posterior axis, centered at the brain slice coordinate for each coronal section image.

For axonal varicosity counting, the images were captured by a 20× objective on the Olympus VS120 system. The axonal varicosity values of overall brain were calculated semi-automatically by particle analyzing plugin in ImageJ. If the axons had a transverse diameter greater than 0.5 µm, then varicosities were defined (Mechawar et al., 2000; Li et al., 2002). The outlines of brain regions were also depicted by ImageJ based on the reference brain atlas.

All data values were presented as the mean ± standard error of the mean (SEM). The strength and direction of the linear relationship between subregions and cells or varicosity proportion were measured by the Pearson product-moment correlation coefficient (CC). Two-tailed Student's t-tests were used to compare the monosynaptic inputs and axonal projections of LPT GABAergic neurons.

### RESULTS

### Applying the Rabies Virus System to Identify Monosynaptic Inputs to LPT GABAergic Neurons

In order to identify monosynaptic inputs to LPT GABAergic neurons, we used a cell-type-specific, RG-deleted RV strategy in GAD2-IRES-Cre mice. The approach has been reported to mark monosynaptic inputs to specifically selected starter cells and has been successfully used in previous studies by our group (Yuan et al., 2018). GAD2-IRES-Cre mice were injected in the LPT with AAV helper viruses fused to green fluorescent protein (GFP). Two weeks later, the modified RV was injected in the same area. One week after this second injection, the mice were perfused, and the brains were processed (**Figures 1A,B**).

Rabies virus infection was clearly detectable at the injection site in GAD2-IRES-Cre mice compared with wild-type littermates, indicating no leakage of viral infection (**Figure 1C**). The location of the injection site was verified by identifying virus-infected neurons in the dorsomedial corner of the PPT and the ventrolateral corner of the PAG. The yellow staining in neurons indicated that the starter cells were co-infected by AAV helper viruses and RV, and the purple staining in neurons presented ChAT immunoreactive signals for the PPT, which was the border of the LPT (**Figures 1D,E**). There were also some DsRed-positive neurons not expressing GFP in the LPT, revealing the existence of local input directly to LPT GABAergic neurons (**Figures 1D,E**, **2A**).

In addition, we mapped the GABAergic starter cells as described in Section "Materials and Methods" (**Figure 2A**) and revealed that they were mostly restricted to the LPT. Most of the starter neurons were found to be located in the coronal planes between −4.16 and −4.84 mm from bregma, especially between −4.36 and −4.60 mm from bregma (**Figure 2B**).

### Input Patterns of LPT GABAergic Neurons

To determine the presynaptic connections of LPT GABAergic neurons, we analyzed serial coronal sections from mice injected with TVA-based trans-synaptic tracer. Sections from a representative GAD2-IRES-Cre brain revealed RV-DsRed-labeled presynaptic input neurons located only in specific brain nuclei in a unilateral manner (**Figure 3**). **Figure 4** depicts input tracing to LPT GABA neurons in greater detail, with enlarged images of inputs from typical subregions such as the bed nucleus of the stria terminalis (BNST), central nucleus of the amygdala (CeA), parasubthalamic nucleus (PSTN), substantia nigra pars reticulata (SNr), SC, dorsal raphe nucleus (DR), PAG, and tuberomammillary nucleus (TMN). Further immunofluorescence assays showed that the inputs to LPT GABAergic neurons were colocalized with several markers associated with important neural functions, especially the sleep-wake cycle. In the hypothalamus, the inputs to LPT GABAergic neurons were found to be partly colocalized with orexin (0.14 ± 0.04 in **Figure 5A**) or MCH (0.11 ± 0.02 in **Figure 5B**) in the LHA, which was shown

to inhibit or promote REM sleep (Adamantidis et al., 2007; Sasaki et al., 2011; Vetrivelan et al., 2016). The input neurons from the PSTN to LPT GABAergic neurons were mostly colocalized with calretinin (CR, 0.44 ± 0.04 in **Figure 5C**), where the inputs might to be related to the sleep-wake cycle (Steininger et al., 1999). In addition, the inputs from the limbic system to the LPT were partly colocalized with GABA neurons, such as the BNST (0.32 ± 0.03 in **Figure 5D**) and CeA (0.41 ± 0.03 in **Figure 5E**), which may be associated with sleep-wake regulation (Kodani et al., 2017; Mahoney et al., 2017) The inputs from the DR to the LPT were found partly colocalized with serotonin (0.20 ± 0.04 in **Figure 5F**), which was proved to promote wakefulness (Ito et al., 2013). Beyond our expectation, numerous inputs from the LPT were colocalized with parvalbumin in the SNr (0.65 ± 0.03 in **Figure 5G**), a region that was reported to mostly contain GABAergic neurons and to be possibly involved in REM sleep (Pal and Mallick, 2009).

injection site is marked by red solid circles. Only the hemisphere ipsilateral to the injection area is shown. Scale bar: 500 µm. MO, momatomotor cortex; SS, somatosensory cortex; STRv, stratum ventral region; PALv, pallidum, ventral region; PALd, pallidum, dorsal region; BMA, basomedial amygdalar nucleus;CeA, central amygdaloid nucleus; MEA, medial amygdalar nucleus; BNST, bed nucleus of the stria terminalis; PF, parafascicular thalamic nucleus; POA, preoptic area; LHA, lateral hypothalamic area; MHA, medial hypothalamic area; PSTN, parasubthalamic nucleus; TMN, tuberomammillary nucleus; ZI, zona incerta; SNc, substantia nigra pars compacta; SNr, substantia nigra pars reticulata; DpMe, deep mesencephalic nucleus; VTA, ventral tegmental area; SC, superior colliculus; RRF, retrorubral field; DR, dorsal raphe nucleus; PAG, periaqueductal gray; PnR, pontine reticular nucleus; NLL, the nuclei of the lateral lemniscus; PBN, parabrachial nucleus; SLD, sublateral dorsal nucleus; LDT, laterodorsal tegmental nucleus; LC, locus coeruleus; Gi, gigantocellular reticular nucleus; CGPn, central gray of the pons; RMg, raphe magnus nucleus; Ve, vestibular nucleus; DCN, deep cerebellar nucleus.

After identifying the brain regions with monosynaptic input to LPT GABAergic neurons, the distribution of these DsRed-labeled afferent neurons was analyzed based on detailed statistics. We divided each brain into six general structures, namely the midbrain, diencephalon, telencephalon, cerebellum, medulla, and pons, which together encompassed 48 specific brain regions containing the DsRed-labeled neurons in the whole brain (n = 4 mice). Then we calculated the proportion of input from each region against the total number of input neurons (**Figure 6**, left). We found that the midbrain provided the highest numbers

retroflexus; ns, nigrostriatal bundle; cp, cerebral peduncle, basal part; mt, mammillothalamic tract; ml, medial lemniscus; InG, intermediate gray layer of the superior colliculus; InWh, intermediate white layer of the superior colliculus; DpG,; mlf, medial longitudinal fasciculus; Aq, aqueduct; scp, superior cerebellar peduncle.

of inputs to the LPT, while few labeled neurons were found in the medulla or cerebellum. The largest number of inputs to LPT GABAergic neurons was found to arrive from the SC (15.10 ± 3.44%). The brain regions sending the second and third largest number of inputs to LPT GABAergic neurons were the SNr (10.00 ± 1.62%) and the DR (6.13 ± 1.07%) in the midbrain. In addition, the DpMe (5.17 ± 0.67%), PAG (3.64 ± 0.40%), and the substantia nigra pars compacta (SNc, 2.83 ± 0.68%) in the midbrain, the LHA (4.41 ± 1.05%) and PSTN (3.12 ± 1.08%) in the diencephalon, and the CeA (4.62 ± 1.71%) and the BNST (3.29 ± 0.48%) in the telencephalon also had strong projections to LPT GABAergic neurons.

### Output Patterns of LPT GABAergic Neurons

We next mapped the output of LPT GABAergic neurons using an AAV-mediated virus, similar to our previous work with viral anterograde tracing (Zhang et al., 2013; Chen et al., 2016; Oishi et al., 2017; Wang et al., 2017). To label the

axonal projections, we injected AAV expressing Cre-dependent ChR2-mCherry (**Figure 7A**) into the LPT of GAD2-IRES-Cre mice. After 3 weeks, mCherry expression was observed to be restricted to the LPT, between the PPT and vlPAG (**Figure 7B**).

SNr, substantia nigra pars reticulata; MCH, melanin-concentrating-hormone; 5-HT, 5-hydroxytryptamine.

In order to more precisely detect axonal varicosities, we combined viral tracing with DAB immunostaining to label mCherry (Campos et al., 2014). Images were compared with the reference brain atlas, and mCherry labeled axons were

parvalbumin (PV)-positive neurons in the SNr (G). Rightmost columns, quantification of DsRed+ cells that are positive for special biomarkers. n = 4, each data point represents one experimental animal. LHA, lateral hypothalamic area; PSTN, parasubthalamic nucleus; CeA, central amygdaloid nucleus; DR, dorsal raphe nucleus;

area; ZI, zona incerta; STN, subthalamic nucleus; SNc, substantia nigra pars compacta; VTA, ventral tegmental area; SNr, substantia nigra pars reticulata; DpMe, deep mesencephalic nucleus; RRF, retrorubral field; RMC, red nucleus; CnF, cuneiform nucleus; PPT, pedunculopontine tegmental nucleus; IC, inferior colliculus; PRT, pretectal region; SC, superior colliculus; DR, dorsal raphe nucleus; PAG, periaqueductal gray; PnR, pontine reticular nuclues; NLL, the nuclei of the lateral lemniscus; LDT, laterodorsal tegmental nucleus; PBN, parabrachial nucleus; SLD, sublateral dorsal nucleus; LC, locus coeruleus; Mo5, motor trigeminal nucleus; MnR, median raphe nucleus; Gi, gigantocellular reticular nucleus; Ve, vestibular nucleus; PCRt, parvicellular reticular nucleus, alpha part; Pr5, principal sensory trigeminal nucleus; CGPn, central gray of the pons; RMg, raphe magnus nucleus; IRT, intermediate reticular nucleus; DCN, deep cerebellar nuclues.

detected (**Figure 7C**). After the neurons at the injection site (identified by the existence of mCherry labeled cell bodies) were excluded, the projection to each brain area was quantified by the pixels of the axonal varicosities (**Figure 8**). Among the five major brain subdivisions that received LPT GABAergic projections (the cerebellum received almost no projection), the midbrain, diencephalon, and pons received the most LPT GABAergic projections, while few axons were detected in the telencephalon or medulla (**Figure 9**, left). The largest proportion of projections from LPT GABAergic neurons was found in the vlPAG (11.70 ± 0.86%), whereas the proportion of the LPT GABAergic varicosities in other subregions of the PAG were 1.42 ± 0.20% (lPAG) and 0.49 ± 0.11% (dPAG). The DR (6.39 ± 0.50%) and SC (4.60 ± 1.16%) in the midbrain, the parabrachial nuclei (PBN, 8.79 ± 1.04%) in the pons, and the LHA (8.08 ± 0.94%), medial hypothalamic area (5.09 ± 1.20%), and mediodorsal/central medial thalamic nucleus (MD/CM, 3.94 ± 0.55%) in the diencephalon also received strong projections from LPT GABAergic neurons.

### Reciprocal Connections Between the LPT and Other Nuclei

Comparing the broad distribution of the inputs and outputs of the LPT, we found that there was a high correlation between LPT afferents and efferents on a large scale of the whole brain. We found the CC was high between the afferent and efferent spatial distributions in all six major brain subdivisions (R = 0.867). Among the six major brain subdivisions, the diencephalon, midbrain, and pons revealed strong reciprocal connections with the LPT; they heavily received projections from and sent axonal fibers to LPT GABAergic neurons. Specifically, the midbrain provided a significantly higher proportion of inputs to the LPT, compared with the proportion of projections from the LPT to the midbrain (53.31 ± 6.33 versus 34.87 ± 2.84%, P = 0.038; Individual P-values indicate two-tailed Student's t-test comparisons of inputs and outputs for each brain region. In contrast, the pons and diencephalon had higher proportions of fibers arriving from the LPT than inputs going into the LPT (**Figure 10A**, pons: 19.94 ± 1.36 versus 12.62 ± 1.17%, P = 0.006; diencephalon: 24.03 ± 2.59 versus 14.22 ± 2.13%, P = 0.027 by two-tailed Student's t-test).

Our analysis of the proportion of inputs versus outputs in each region revealed that LPT GABAergic neurons had equally interactive connections to structures within the DR in the midbrain (**Figure 10B**; the proportion of the inputs to the LPT versus the outputs from the LPT, 6.04 ± 1.07 versus 6.39 ± 0.50%). However, there were many brain regions where connections with the LPT dominated in a unidirectional manner, such as the PAG and SC (**Figure 10B**). **Figures 10C,D** summarizes the afferents and efferents of LPT GABAergic

neurons in schematic form, reflecting the anatomical distribution of these innervations.

# DISCUSSION

By applying advanced viral tracing methods, we identified for the first time the whole-brain distribution of input and output projections of LPT GABAergic neurons. The afferents and efferents of LPT GABAergic neurons were mostly asymmetric in major nuclei such as the LHA, SC, PAG, PBN, and SNr. Only the DR had equally reciprocal connections with the LPT. In addition, LPT GABAergic neurons received projections from several brain areas related to sleep-wake regulation and motor activity. Finally, we found a strong connection between LPT GABAergic neurons and the limbic system, suggesting that emotional processing may be associated with LPT-mediated REM sleep and motor regulation.

### Technical Advances and Considerations

Genetically engineered RV has been widely used in neuroscience for its cell type-specific infection of neurons that does not affect passing neuronal tracts and its effective passage across known synaptic connections (Wickersham et al., 2007; Weissbourd et al., 2014). Accordingly, our data obtained using RV-mediated retrograde tracing in GAD2-IRES-Cre mice demonstrated that there are, in fact, direct projections from the SLD and LHA to LPT GABAergic neurons. This result not only corroborated a similar finding using CTB retrograde tracing (Lu et al., 2006; Clement et al., 2012), but also firstly identified GABAergic neurons in the LPT that received the inputs from the SLD and LHA. In addition, there are some differences between the distributions of the inputs to LPT GABAergic neurons compared with the inputs to LPT non-specific neurons. Clement and colleagues report that the ZI sends numerus projections to the LPT by CTB tracing (Clement et al., 2012). However, we found that the specific connection from ZI to LPT GABAergic neurons by RV-based tracing was weak, suggesting that the ZI may send most projections to glutamatergic neurons in the LPT.

On the other side, we built a detailed and quantitative map of LPT GABAergic neuron projections to the entire brain by AAV-mediated anterograde tracing, which was more specific and more restricted than the traditional methods. For example, although the non-specific projections from the LPT to the subthalamic nucleus (STN) were found by biotinylated dextran amine (BDA) (Bevan and Bolam, 1995), they didn't give the statistics of GABAergic projections from the LPT or full view of any other projecting nuclei. In our results, we quantitatively showed that LPT GABAergic neurons sent GABAergic varicosities in the STN (<0.1% of whole-brain varicosities). By visualizing the AAV reporter mCherry with DAB immunostaining, we avoided false positive

anterior commissure, posterior; scp, superior cerebellar peduncle.

signals, which are commonly observed after fluorescence immunohistochemistry (Oh et al., 2014). We chose to quantify projections by counting axonal varicosities instead of measuring fluorescence intensity, in order to avoid differences between thickness of brain sections or light exposure by the microscope.

### Morphological Findings

Due to the technical shortcomings of classical tracing methods, the complete neuronal connectivity of GABAergic neurons in the LPT was previously unknown. We used cell-type-specific anterograde and retrograde viral tracing to map the connectivity of LPT GABAergic neurons and found that their main inputs (≥20%) originated from the midbrain, whereas the brain regions that received projections from LPT GABAergic neurons were mainly distributed (≥20%) between the telencephalon, midbrain, and pons.

Among major nuclei connected to LPT GABAergic neurons, the DR had equally interactive connections to LPT GABAergic neurons (**Figure 10B**). Weissbourd and his colleagues found that the LPT projects to DR 5-HT neurons (Weissbourd et al., 2014). Here, we identified that the neuronal type in the LPT projected to the DR was GABAergic (**Figure 8**). The DR 5-HT neurons innervated LPT GABAergic neurons to form bidirectional connections (**Figure 5F**). This equally reciprocal LPT-DR connection may reveal a pathway for bidirectional feedback regulation of behavioral functions such as the antidepressant effect (Briley and Moret, 1993).

tegmental area; RRF, retrorubral field; RMC, red nucleus; CnF, cuneiform nucleus; PPT, pedunculopontine tegmental nucleus; DR, dorsal raphe nucleus; dPAG, dorsal periaqueductal gray; lPAG, lateral periaqueductal gray; vlPAG, ventrolateral periaqueductal gray; PnR, pontine reticular nuclues; LDT, laterodorsal tegmental nucleus; NLL, the nuclei of the lateral lemniscus; PBN, parabrachial nucleus; SLD, sublateral dorsal nucleus; LC, locus coeruleus; Mo5, motor trigeminal nucleus; MnR, median raphe nucleus; Gi, gigantocellular reticular nucleus; Ve, vestibular nucleus; PCRt, parvicellular reticular nucleus, alpha part; Pr5, principal sensory trigeminal nucleus; CGPn, central gray of the pons; RMg, raphe magnus nucleus; IRT, intermediate reticular nucleus.

From comparison of distribution among the major nuclei more unidirectionally connecting to LPT GABAergic neurons, we found that afferents to LPT GABAergic neurons concentrated on the SC and SNr, whereas GABAergic efferents from the LPT were mostly distributed in the PAG, PBN, and LHA. Most afferents to LPT GABAergic neurons originated from the SC, particularly the intermediate and deep layers of the SC (**Figures 3**, **6**). Although the projections from the SC to the LPT have been demonstrated in monkeys by anterograde tracing with BDA (Wang et al., 2013), the fact that these projections are predominantly GABAergic was unknown. Although previous studies indicated that the LPT received inhibitory projections from the SNr (Sherman et al., 2015), we identified the SNr as the second largest input to LPT GABAergic neurons. In addition, we showed that the main outputs of LPT GABAergic neurons project to the PAG, especially the vlPAG (**Figures 8**, **9**), while LPT-projecting PAG neurons were already known (Weissbourd et al., 2014). It was previously suggested that the majority of LPT cells provide GABAergic inhibition of PPT cholinergic neurons, including those cholinergic neurons that provide a major ascending pathway into the posterior LHA (Ford et al., 1995). However, our data revealed that the LHA directly received strong projections from LPT GABAergic neurons.

The proportion of inputs to the LPT among whole-brain inputs is different from that of the LPT. A pathway from the PSTN to the LPT was previously reported using phaseolus vulgaris leucoagglutinin anterograde tracing (Goto and Swanson, 2004), and by this method it is not possible to determine whether a majority or a minority of PSTN neurons project to LPT. Here, we reported the proportion of the inputs from the PSTN projecting to LPT GABAergic neurons among whole-brain inputs was 3.02 ± 1.09%, which was about 44% colocalized with CR neurons (**Figure 5C**). Combined with dense inputs from the CeA in the forebrain, our results suggested a strong ability of the LPT to integrate behavioral information from the PSTN and CeA such as feeding and fear behaviors (Zimmerman et al., 2007; Chometton et al., 2016). Although the LPT has been shown to provide strong GABAergic projections to the dorsocaudal region of the pontine tegmentum, including the PBN, LC, and SLD (Hayashi et al., 2015), our finding of dense, LPT-originating, GABAergic axonal varicosities in the SNc of the midbrain suggested that the LPT also transmitted strong information to the SNc.

# Functional Implications

LPT GABAergic neurons have strong connections with brain regions related to sleep-wake regulation. Firstly, the output to the vlPAG is known to gate REM sleep and the ultradian rhythm of REM/NREM alternation (Weber et al., 2018). In addition, vlPAG/DpMe GABAergic neurons were shown to be important in sleep regulation (Hayashi et al., 2015; Weber and Dan, 2016) and thus, bidirectional projections between the LPT and

subdivisions of the whole brain. LPT GABAergic inputs are colored red and the outputs are shaded in orange. The data represent the mean ± SEM, with n = 4 for each group. Two-tailed Student's t-tests indicated significant (∗P < 0.05, ∗∗P < 0.01) differences between the inputs and outputs of LPT GABAergic neurons in the same subdivision. (B) The percentage of input versus percentage of output in each region of typical nuclei that had strong connections with the LPT. The proportion of inputs or outputs ≥ 5% are indicated with open circles. (C,D) Schematic diagrams presenting the afferents and efferents of LPT GABAergic neurons. BNST, bed nucleus of the stria terminalis; STRv, stratum ventral region; PALd, pallidum, dorsal region; PALv, pallidum, ventral region; CeA, central amygdaloid nucleus; BMA, basomedial amygdalar nucleus; POA, preoptic area; LHA, lateral hypothalamic area; MHA, medial hypothalamic area; PSTN, parasubthalamic nucleus; MD/CM, mediodorsal/central media thalamic nucleus; PVT, paraventricular thalamic nucleus; PF/Po, parafascicular/ posterior thalamic nucleus; SNc, substantia nigra pars compacta; ZI, zona incerta; SNr, substantia nigra pars reticulata; VTA, ventral tegmental area; DpMe, deep mesencephalic nucleus; SC, superior colliculus; RRF, retrorubral field; CnF, cuneiform nucleus; PPT, pedunculopontine tegmental nucleus; DR, dorsal raphe nucleus; PAG, periaqueductal gray; IC, inferior colliculus; PnR, pontine reticular nuclues; NLL, the nuclei of the lateral lemniscus; PBN, parabrachial nucleus; SLD, sublateral dorsal nucleus; LC, locus coeruleus; LDT, laterodorsal tegmental nucleus; IRT, intermediate reticular nucleus; Gi, gigantocellular reticular nucleus; PCRt, parvicellular reticular nucleus, alpha part.

vlPAG may be important for sleep regulation. Strong inhibitory projections from the LPT to the arousal-promoting PBN may also indicate a role of the LPT in sleep/wake control and thermoregulation (Fuller et al., 2011; Kaur et al., 2013; Nakamura, 2018; Wang et al., 2018). Furthermore, we and others identified SLD-projecting LPT GABAergic neurons (Boissard et al., 2003), which may play important roles in REM sleep regulation and related sleep disorders, such as narcolepsy or REM sleep behavior disorder. The direct connections we observed between the LPT and the hypothalamus suggested that LPT GABAergic neurons

might be involved in regulation of hypothalamus functions, such as REM sleep, arousal, feeding behaviors, body temperature, and sense of pain (Clement et al., 2012; Li et al., 2014). Finally, another study showed that the neuronal activity of the posterior lateral hypothalamus containing the PSTN was related to the wake and REM sleep (Steininger et al., 1999). In the present study, the PSTN sent abundant projections to the LPT GABAergic neurons, suggesting that the pathway of the PSTN to LPT GABA neurons might be involved in sleep-wake regulation. Besides that, chemical activation of PSTN neurons elicited depressor as well as bradycardia (Ciriello et al., 2008), and hedonic tastes increased c-Fos expression of the PSTN in rats (Chometton et al., 2016), suggesting that cardiovascular regulation and food intake were probably mediated by the PSTN innervated LPT GABAergic neurons.