The thalamic mGluR1-PLCβ4 pathway is critical in sleep architecture
- Joohyeon Hong†1,
- Jungryun Lee†2,
- Kiyeong Song1,
- Go Eun Ha1,
- Yong Ryoul Yang3,
- Ji Su Ma4,
- Masahiro Yamamoto4,
- Hee-Sup Shin2,
- Pann-Ghill Suh3Email author and
- Eunji Cheong1Email authorView ORCID ID profile
© The Author(s). 2016
Received: 14 September 2016
Accepted: 29 November 2016
Published: 21 December 2016
The transition from wakefulness to a nonrapid eye movement (NREM) sleep state at the onset of sleep involves a transition from low-voltage, high-frequency irregular electroencephalography (EEG) waveforms to large-amplitude, low-frequency EEG waveforms accompanying synchronized oscillatory activity in the thalamocortical circuit. The thalamocortical circuit consists of reciprocal connections between the thalamus and cortex. The cortex sends strong excitatory feedback to the thalamus, however the function of which is unclear. In this study, we investigated the role of the thalamic metabotropic glutamate receptor 1 (mGluR1)-phospholipase C β4 (PLCβ4) pathway in sleep control in PLCβ4-deficient (PLCβ4−/−) mice. The thalamic mGluR1-PLCβ4 pathway contains synapses that receive corticothalamic inputs. In PLCβ4−/− mice, the transition from wakefulness to the NREM sleep state was stimulated, and the NREM sleep state was stabilized, which resulted in increased NREM sleep. The power density of delta (δ) waves increased in parallel with the increased NREM sleep. These sleep phenotypes in PLCβ4−/− mice were consistent in TC-restricted PLCβ4 knockdown mice. Moreover, in vitro intrathalamic oscillations were greatly enhanced in the PLCβ4−/− slices. The results of our study showed that thalamic mGluR1-PLCβ4 pathway was critical in controlling sleep architecture.
KeywordsSleep Thalamus Phospholipase C β4 Knockout mice Delta wave Thalamocortical oscillation
Sleep-wake control has been attributed to many brain regions, including the brain stem [1–3], hypothalamus , basal forebrain , basal ganglia , and thalamus . Sleep is composed of the non-rapid eye movement (NREM) and rapid eye movement (REM) sleep states, which are categorized by characteristic brain rhythms in electroencephalography (EEG) recordings and distinctive eye movements [7, 8]. The NREM sleep state is characterized by large-amplitude, low-frequency EEG waveforms, and the REM sleep state is marked by distinctive regular theta (θ) waves . The EEG waveform components that are observed during NREM sleep are further subdivided according to frequency into very slow waves (<0.5 Hz), delta (δ) waves (0.5–4 Hz), and spindle (σ) waves (10–15 Hz) [7, 9].
The high-amplitude slow brain rhythms observed during NREM sleep accompany the synchronized oscillatory activity recorded in thalamocortical circuits . Thalamocortical circuits are composed of neurons in the cortex and thalamus. The thalamus is further dissected into thalamic reticular nuclei (TRN), which are composed of inhibitory neurons, and thalamocortical (TC) nuclei composed of excitatory neurons which reciprocally project each other . At the onset of NREM sleep, the membrane potential of thalamic neurons is hyperpolarized , which, then is followed by a shift in the firing pattern of thalamocortical (TC) neurons from tonic to burst firing . The firing of TC neurons has been implicated in the genesis of spindle and delta waves because they are both thought to originate from thalamic neurons [14–17], although cortically generated delta waves and spindles are observed in cats with extensive thalamic lesions . TC neurons send long axons to cortical neurons, and the cortical neurons in layer VI send strong excitatory feedback back to both TRN and TC neurons, which completes the loop of thalamocortical circuit.
Among the many inputs to TC neurons, including ascending inputs from the brainstem, the glutamatergic inputs from layer VI neurons of the cerebral cortex provide the largest amount of input . Among the ionotropic and metabotropic glutamate receptors (mGluRs), mGluR1 is highly expressed in TC neurons , where it is found exclusively in the postsynaptic membranes of the corticothalamic inputs from layer VI neurons . These observations suggest a major role of the mGluR1 in TC neuron modulation in response to corticothalamic inputs. Indeed, the activation of descending corticothalamic pathways increases the excitability of TC neurons through the mGluR1 pathway [19, 22]. mGluRs are often coupled to phospholipase C (PLC) activity in the brain , and mGluR1 is tightly linked to PLCβ4 in TC neurons [24, 25].
No study has investigated the role of corticothalamic inputs to TC neurons through mGluRs in sleep architecture. We studied the effects of corticothalamic input to TC neurons in sleep architecture and sleep rhythms through the mGluR1-PLCβ4 pathway in PLCβ4-deficient (PLCβ4−/−) mice.
Increased NREM sleep in PLCβ4−/− mice
Additional file 1: Movie S1 Simultaneous video recordings during natural sleep-wake cycles in the light phase. The behaviors of the PLCβ4−/− mice were recorded with video during the electroencephography (EEG)/electromyography (EMG) signal recordings during the natural sleep-wake cycles in the light phase. (MP4 10892 kb)
Additional file 2: Movie S2 Simultaneous video recordings during natural sleep-wake cycles in the dark phase. The behaviors of the PLCβ4−/− mice were recorded with video during the EEG/EMG signal recordings during the natural sleep-wake cycles in the dark phase. (MP4 5982 kb)
The PLCβ4−/− mice exhibited mild absence seizures, as has been previously reported , with occasional spike-wave discharges (SWDs) in the EEG recordings (green vertical bars in the extended hypnogram plot in the upper panel in Fig. 1d). The SWDs, which mainly occurred in the awake state, were accompanied by a substantial reduction in EMG tone (lower panel in Fig. 1d), which indicated behavioral arrest and which is typical during absence seizures. However, the percentage of SWD duration was less than 1.2% (light phase: 0.98 ± 0.23%; dark phase: 1.42 ± 0.34%; Fig. 1e). The incidences of SWDs were much lower in the NREM (light: 0.74 ± 0.18%; dark: 0.81 ± 0.18%) and REM (light: 0.17 ± 0.05%; dark: 0.12 ± 0.03%; Fig. 1e) sleep states. Furthermore, each SWD event had a duration of only 1–3 s, and these events did not interfere with the determination of the awake and sleep states.
Altered sleep architecture in the PLCβ4−/− mice
During the light phase, the number of long awake episodes (≥15 min) was significantly decreased (p < 0.005) in the PLCβ4−/− mice (n = 9; 1.4 ± 0.4) compared to the PLCβ4+/+ mice (n = 8; 4.1 ± 0.4), whereas the number of short awake episodes (<15 min) did not differ between the groups (Fig. 3d). It is noteworthy that the number of long NREM sleep episodes (≥10 min) was significantly increased (p < 0.005) in the PLCβ4−/− mice (20.7 ± 2.2) compared to the PLCβ4+/+ mice (11.5 ± 1.6), while the number of short NREM sleep episodes (<10 min) was significantly reduced (p < 0.05) in the PLCβ4−/− mice (63.6 ± 2.5) compared to the PLCβ4+/+ mice (81.8 ± 6.6; Fig. 3e). The number of short REM sleep episodes (<3 min) was also significantly decreased (p < 0.005) in the PLCβ4−/− mice (26.2 ± 1.5) compared to the PLCβ4+/+ mice (60.3 ± 5.7), and the long REM sleep episodes (≥3 min) occurred more frequently (p < 0.05) in the PLCβ4−/− mice (8.4 ± 1.2) compared to the PLCβ4+/+ mice (4.0 ± 1.1; Fig. 3f). The most remarkable change was that the overall number of REM sleep episodes was greatly decreased (p < 0.005) in the PLCβ4−/− mice (34.7 ± 2.0; PLCβ4+/+, 64.3 ± 5.9; Fig. 3f), while the overall number of NREM sleep episodes was similar between the groups (PLCβ4+/+, 93.3 ± 5.2; PLCβ4−/−, 84.2 ± 3.5; Fig. 3e), which suggested that the transition from NREM to REM sleep was hindered, resulting in long NREM sleep episodes and overall increases in total NREM sleep during the light phase.
During the dark phase, PLCβ4+/+ mice showed clear nocturnal activity with long awake episodes (Fig. 3g). However, the PLCβ4−/− mice exhibited a significant increase (p < 0.005) in the number of short awake episodes (PLCβ4−/−, 53.7 ± 3.7; PLCβ4+/+, 33.9 ± 3.3) and a significant decrease (p < 0.005) in the long awake episodes (PLCβ4−/−, 5.1 ± 0.7; PLCβ4+/+, 8.5 ± 0.4; Fig. 3g, j). In parallel, the number of short NREM sleep episodes was significantly increased (p < 0.05) in the PLCβ4−/− mice (77.2 ± 5.9) compared to the PLCβ4+/+ mice (55.3 ± 6.1; Fig. 3h, k). Furthermore, the overall number of episodes in the awake (PLCβ4+/+, 42.4 ± 3.4; PLCβ4−/−, 58.8 ± 3.4; p < 0.005; Fig. 3j) and NREM sleep (PLCβ4+/+, 61.4 ± 5.4; PLCβ4−/−, 80.7 ± 5.1; p < 0.05; Fig. 3k) states were significantly increased in the PLCβ4−/− mice, which indicated that the awake states could not be maintained due to the frequent transitions to the NREM sleep state. The number of short REM sleep episodes did not differ between the groups (PLCβ4+/+, 26.0 ± 4.9; PLCβ4−/−, 25.2 ± 3.9), whereas the number of long REM sleep episodes was significantly increased (p < 0.005) in the PLCβ4−/− mice (6.7 ± 0.9) compared to the PLCβ4+/+ mice (0.9 ± 0.3; Fig. 3i, l). The overall number of REM sleep episodes was not increased in the PLCβ4−/− mice (31.9 ± 4.5) compared to the PLCβ4+/+ mice (26.9 ± 4.9; Fig. 3l), although more frequent occurrence of the NREM sleep. These results suggested that a transition from the NREM to the REM sleep state was less likely to occur during both the dark and light phases in PLCβ4−/− mice.
These results indicated that PLCβ4−/− mice could not maintain the awake state during the dark phase and that their vigilance states were more frequently directed towards NREM sleep, which resulted in an increase in the amount of total NREM sleep. During the light phase when mice prefer to sleep, the NREM sleep states of the PLCβ4−/− mice were stabilized by decreased transitions from the NREM to the REM sleep state and significant increases in the long NREM sleep episodes.
Increased delta power during NREM sleep in the PLCβ4−/− mice
In order to exclude the possibility that the increased δ-band power in the PLCβ4−/− mice was due to the SWDs, despite their sparse occurrence, we analyzed the power spectral densities in SWD-free EEG traces. The PLCβ4−/− mice showed consistent increases in the δ-band power during NREM sleep irrespective of the appearance of SWDs in the EEG traces during both the light and dark phases (Additional file 3: Figure S1B and S1E). These findings indicated that the impairments in the mGluR1-PLCβ4 pathways resulted in increases in the δ-band power during NREM sleep regardless of SWD generation. Slow rhythms, such as δ waves, that appear during NREM sleep accompany synchronized oscillatory activity in the thalamocortical circuit . Therefore, we examined whether the thalamocortical oscillatory activity was affected by the mGluR1-PLCβ4 pathway impairments.
Increased in vitro thalamic network oscillations in PLCβ4−/− slices
Increased NREM sleep amount and delta band power in TC-restricted PLCβ4 knockdown mice
The results of our study demonstrated that slow intrathalamic oscillatory activity was significantly enhanced in brain slices from PLCβ4−/− mice (Fig. 5). Within the intrathalamic circuit consisting of reciprocal connections between the TC and TRN nuclei, PLCβ4 is almost exclusively expressed in TC neurons with tight linkage with the mGluR1, whereas no expression is found in the TRN [24, 25]. Therefore, these results suggested that the enhanced intrathalamic oscillations in the PLCβ4−/− slices were caused by the deletion of PLCβ4 in the TC neurons. The intrathalamic oscillations that were induced by a single electrical stimulus to the in vitro IC have often been examined in order to better understand the mechanisms underlying the sleep rhythms or spike wave discharges that are generated in the thalamocortical circuit [29–31]. Thus, the findings of enhanced intrathalamic oscillations in the PLCβ4−/− thalamic slices were in good agreement with the findings of significant increases in the power density of the δ waves during NREM sleep in PLCβ4−/− mice. The essential role of thalamic PLCβ4 in control of brain rhythms during NREM sleep was further supported by the TC-restricted PLCβ4 knockdown data (Fig. 6). The process underlying the generation of δ waves is unclear [4, 18, 34–37]. A previous study has shown that the generation of δ waves is thalamic-dependent when TC neurons reach a certain level of hyperpolarization , which has been questioned due to the view that cortical neurons pace the δ waves because the waves are still observed after large lesions in the thalamus . Our data from both PLCβ4−/− and TC-restricted PLCβ4 knockdown mice support the view that δ waves are regulated by the thalamic circuit . Taken together, these results suggested that impairments of the mGluR1-PLCβ4 pathway in TC neurons enhanced the slow-frequency thalamocortical oscillations and δ power during NREM sleep.
The δ wave appears mainly in the deep-sleep stages during NREM sleep, which is identical to sleep stages 3 and 4 in the human . Concomitant increases in δ power and the duration of NREM sleep have been observed in many studies under sleep debt conditions after sleep deprivation [38, 39]. In contrast, other studies have indicated that the EEG δ power is regulated independently of NREM sleep amount . In this study, we observed enhanced δ power during NREM sleep in parallel with increased NREM sleep amount in PLCβ4−/− mice. During the light phase, prolonged NREM sleep episodes in the PLCβ4−/− mice was observed with reductions in the NREM to REM sleep transition (Fig. 3). During the dark phase, PLCβ4−/− mice showed an increased transition from the awake state to the NREM sleep state resulting in the appearance of short NREM episodes accompanying concomitant disappearance of long lasting awake episodes. Nevertheless, the number of REM sleep episodes in the dark phase was not increased because the transition from NREM to REM sleep was decreased. These results together indicated that, once the mice entered NREM sleep, the transition to progress to the REM sleep state was attenuated in the PLCβ4−/− mice. The physiological significance of the reductions in the shift between the NREM-REM sleep states is not yet understood.
In this study, we also observed longer REM sleep episodes in the PLCβ4−/− mice compared to the PLCβ4+/+ mice (Fig. 3f, l). Some studies have suggested that, after REM sleep deprivation, the time spent in REM sleep is extended during recovery in order to produce a rebound effect . Therefore, the prolonged REM sleep episodes might be homeostatically regulated by abnormally maintained NREM sleep episodes and the reductions in the attempts to enter REM sleep. However, contributions to these results by other brain regions, including the mesopontine nuclei and hypothalamic nuclei , that regulate REM sleep cannot be completely excluded because these brain regions express low levels of PLCβ4 and send inputs to the thalamus . Therefore, further studies are needed to fully understand the observed changes in the REM sleep states.
Since the brain stem circuitry was first described as an ‘ascending reticular activating system’ that sends inputs to the thalamus and other brain regions [42, 44], many studies have reported that the brain stem circuitry regulates the transition between NREM and REM sleep by turning the REM sleep state on and off [2, 42]. Recently, many studies have focused on the role of these ascending pathways, including the brain stem [1, 45], basal forebrain that receives inputs from the brain stem , and hypothalamus , in the control of sleep state switching. To date, the function of L6 feedback is far from clear. However, it has been implicated in the shaping of receptive fields by the selective attention, initiation, and termination of thalamocortical oscillations , and it acts as a classical modulator [48, 49]. Much indirect in vitro evidence of this modulatory function of L6 inputs is in contrast with the driver functions of L5 inputs in higher-order thalamic nuclei [50, 51]. Definite proof would be changes that are elicited in the thalamocortical network state. Therefore, the current study is novel because it examined the role of the mGluR1- PLCβ4 pathway that is specific to L6 inputs in sleep architecture through modulations of thalamic oscillations. The loss of this major excitatory input pathway in a top-down control circuit to thalamus synapses dramatically changed the sleep architecture.
In summary, we found that the deletion of PLCβ4, which is specifically expressed postsynaptically to L6 corticothalamic inputs, attenuated the transition from NREM to REM sleep state in PLCβ4−/− mice, which subsequently increased the total amount of NREM sleep and enhanced the δ-frequency power in the EEGs. These results, combined with TC restricted-PLCβ4 knockdown data, demonstrated that the corticothalamic input to TC neurons through the mGluR1-PLCβ4 pathway was critical for sleep architecture and the generation of sleep rhythms.
All of the experiments used PLCβ4−/− mice and their wild-type littermates in the F1 hybrid that was generated by mating heterozygote mice (PLCβ4+/−) from two genetic backgrounds: 129/sv and C57BL/6 J. The mice were maintained with free access to food and water under a 12-h light and 12-h dark cycle, with the light cycle beginning at 6:00 am. The animal care and handling were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee at Yonsei University (Seoul, Korea).
Surgery & chronic EEG/EMG monitoring
Twelve- to 14-week-old male mice were used for the chronic monitoring of the EEG/EMG signals. For EEG/EMG electrode implantation, the mice were anesthetized with 0.2% tribromoethanol (20 mL/kg, intraperitoneal injection) and placed on a stereotaxic frame. An epidural electrode for EEG recording was implanted in the parietal lobe. For EMG signal recording, a Teflon-coated tungsten electrode was inserted into the nuchal musculature. A grounding electrode was implanted in the occipital region of the skull. After a 1-week recovery, the mice were placed in unrestrained chronic recording environments under 12-h light and 12-h dark conditions. They were allowed to adapt to the recording systems for 10–14 days. The EEG and EMG signals were amplified (F14-EET, Data Sciences International, St. Paul, MN, USA), low-pass-filtered at 100 Hz for EEG and high-pass-filtered at 10 Hz for EMG, and digitized at a sampling rate of 250 or 500 Hz. The data were continuously acquired for 48 h with a telemetry system (DATAQUEST A.R.T. 2.2, Data Sciences International).
Sleep scoring and analysis
The EEG/EMG records were scored semiautomatically with a SleepSign software sleep scoring system (Kissei Comtec America, Irvine, CA, USA) in 8-s epochs as wake (low-voltage, high-frequency EEG and high-amplitude EMG), NREM sleep (high-voltage, low-frequency EEG and low-amplitude EMG), or REM sleep (low-amplitude EEG constituted mainly by theta-wave activity and EMG atonia) according to the standard criteria of rodent sleep [46, 52]. The onset of the sleep and awake episodes was defined as three consecutive epochs. Epochs containing artifacts occurred during active wakefulness (with large movements), and epochs containing two vigilance states were visually identified. The percentage and amount of time spent in awake, NREM sleep, and REM sleep states, as well as the number of episodes, were calculated for each group. In order to categorize the episodes as long or short, we obtained all of the awake, NREM sleep, and REM sleep episodes that occurred during the light phase in the PLCβ4+/+ mice (n = 8). The episodes of each vigilance state were ranked according to their durations from minimum to maximum. Duration at the 90th percentile was the criterion for classifying episodes as long or short. The criterion was near 15, 10, or 3 min for the awake, NREM sleep, and REM sleep states, respectively, and this is indicated by the dotted line in Fig. 3a-c and g-i.
Power spectral density analysis
In order to analyze the power spectral densities in the entire traces for each vigilance state, the EEG spectral power was calculated in 0.5-Hz bins by fast Fourier transformation (Hamming window) of each 8-s epoch and normalized with the SleepSign software. In order to analyze the power spectral densities that excluded the power of the SWDs, 10 sets of 10 consecutive SWD-free epochs were chosen to represent the awake, NREM sleep, and REM sleep states in each animal. The EEG spectral power was calculated as described above and normalized with Clampfit 10.3 software (Molecular Devices, USA) and averaged in each animal. The power bins in the 0.5–20 Hz range were summed for the four frequency bands [δ (0.5–4 Hz), θ (4–9 Hz), σ (10–15 Hz), or α + β (9–20 Hz)] and then averaged in the groups across each arousal state.
For the histological analysis, the mice were anesthetized with 0.2% tribromoethanol (20 mL/kg, intraperitoneal injection) and transcardially perfused with 1 M phosphate-buffered saline (PBS) followed by a 4% paraformaldehyde solution. After the perfusions, the brains were removed and fixed in 4% paraformaldehyde overnight at 4 °C and then submerged in 30% sucrose solution for 3 days at 4 °C. The brains were then frozen in O.C.T compound, cut into serial 40-μm-thick coronal sections on a freezing microtome, and collected in 1 M PBS. The brain sections were permeabilized with 0.1% Tween-20 in 1 M PBS for 30 min and then incubated in blocking solution (5% normal goat serum in 1 M PBS) for 1 h. After washing 3 times with PBS, the tissues were incubated with a primary antibody against PLCβ4 (EMD Millipore Corporation, Billerica, MA, USA) for 24 h at 4 °C. The tissues were rinsed three times in PBS, incubated with a Cy3-conjugated secondary antibody (Amersham Biosciences Corporation, NJ, USA) for 2 h at room temperature, and then mounted on microscope slides with fluorescent mounting media (Dako Denmark A/S, Glostrup, Denmark). Fluorescence images were obtained with a LSM 700 confocal microscope (Carl Zeiss AG, Oberkochen, Germany).
Preparation of the brain slices
Thalamic slices were prepared from ~4 to 5-week-old PLCβ4−/− mice and their wild-type littermates. The brains were rapidly taken from the mice that were deeply anesthetized with halothane. The brains were blocked and sectioned in the horizontal plane. Blocks containing the VB and TRN nuclei were cut with a Leica VT1000 microtome (Leica Microsystems GmbH, Wetzlar, Germany) in ice-cold slicing solution containing the following (in mM): 234 sucrose, 2.5 KCl, 11 glucose, 26 NaHCO3, 1.25 NaH2PO4, 0.5 CaCl2, and 10 MgSO4. The slices were incubated for at least 1 h in artificial cerebrospinal fluid (ACSF) and then gradually brought to room temperature. The ACSF contained the following (in mM) : 124 NaCl, 26 NaHCO3, 1.25 NaH2PO4, 5 MgCl2, 1 CaCl2, 3 KCl, and 10 glucose. Both the slicing solution and ACSF were saturated with 95% O2 and 5% CO2 (pH 7.4).
In vitro thalamic oscillation recordings
For the oscillation recordings, the 400-μm-thick horizontal brain slices from the mice were placed in a humidified and oxygenated interface recording chamber and perfused with oxygenated ACSF (2 mL/min) at ~32 °C. The MgCl2 concentration in the perfusion solution was reduced to 0.65 mM, whereas normal ACSF contains 2 mM MgCl2. Intrathalamic oscillations were evoked by a 20- to 100-μV and 60- to 80-μs square pulse stimulus to the IC through a bipolar electrode (FHC, Bowdoin, ME, USA) that was positioned at the border of the IC and TRN. The stimulus interval was 30 s. The extracellular multiunit activity was recorded with a tungsten electrode (50–100 kΩ, FHC) that was placed in the VB. One experiment was performed per slice. The recordings were amplified 100,000 times, digitized at 20 kHz with a Digidata 1440A series, bandpass filtered at 5 Hz–5 kHz, and acquired with pClamp software (Molecular Devices LLC, Sunnyvale, CA, USA).
The differences between the vigilance-state data for the PLCβ4−/− and PLCβ4+/+ mice were analyzed by repeated measures analysis of variance. Student’s t-tests were used for the other data analyses. P values less than 0.05 were considered statistically significant.
metabotropic glutamate receptor 1
metabotropic glutamate receptors
Non-rapid eye movement
- PLCβ4−/− :
Phospholipase C β4 deficient
Rapid eye movement
Thalamic reticular nuclei
This work was supported by NRF-2014R1A2A2A01006940 and NRF-2014M3A7B4051596, which was funded by the government of the Republic of Korea (Ministry of Science, ICT & Future Planning); the International Collaborative R&D Program, which was funded by the Ministry of Trade, Industry and Energy, Korea; Yonsei University Future-Leading Research Initiative of 2015 (2015-22-0163) and the Brain Korea 21(BK21) PLUS program. Joohyeon Hong and Go Eun Ha are fellowship awardee by BK21 PLUS program.
This work was supported by NRF-2014R1A2A2A01006940 and NRF-2014M3A7B4051596, which were funded by the government of the Republic of Korea (Ministry of Science, ICT & Future Planning); the International Collaborative R&D Program, which was funded by the Ministry of Trade, Industry and Energy, Korea; Yonsei University Future-Leading Research Initiative of 2015 (2015-22-0163) and the Brain Korea 21(BK21) PLUS program.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its additional files.
EC conceived the project. JL, HS, and EC designed the study. JH, JL, GH, and KS performed the experiments and analyzed the results. YY, JM, MY and PS produced the mice for knock-down experiments. JH, JL, and EC interpreted the results and wrote the manuscript. All of the authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
All of the animal care and handling were performed according to the the guidelines of the Institutional Animal Care and Use Committee at Yonsei University (Seoul, Korea).
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