Article

Involvement of Ca2+-Dependent Hyperpolarization in
Sleep Duration in Mammals
Highlights
d

d

d

d

Authors2+

A simple model predicts Ca -dependent hyperpolarization
regulates sleep duration
Impaired/enhanced Ca2+-dependent hyperpolarization
decreases/increases sleep duration

Fumiya Tatsuki,
Genshiro A. Sunagawa, Shoi Shi, ...,
Koji L. Ode, Katsuhiko Matsumoto,
Hiroki R. Ueda

Impaired Ca2+-dependent hyperpolarization increases neuralexcitability

Correspondence

Impaired Ca2+/calmodulin-dependent kinases (Camk2a/
Camk2b) decreases sleep duration

In Brief

Tatsuki et al., 2016, Neuron 90, 70–85
April 6, 2016 ª2016 Elsevier Inc.
http://dx.doi.org/10.1016/j.neuron.2016.02.032

uedah-tky@umin.ac.jp

Tatsuki et al. present that a Ca2+dependent hyperpolarization pathway
underlies the regulation of sleep durationin mammals. They predicted the
hypothesis by a simple averaged-neuronmodel and verified it by phenotyping 21
KO mice and whole-brain imaging withpharmacological intervention.

Neuron

ArticleInvolvement of Ca2+-Dependent Hyperpolarization
in Sleep Duration in Mammals
Fumiya Tatsuki,1,5 Genshiro A. Sunagawa,2,5 Shoi Shi,1,5 Etsuo A. Susaki,1,2,5 Hiroko Yukinaga,2,5 Dimitri Perrin,2,4,5Kenta Sumiyama,3 Maki Ukai-Tadenuma,2 Hiroshi Fujishima,2 Rei-ichiro Ohno,1 Daisuke Tone,1 Koji L. Ode,1,2
Katsuhiko Matsumoto,2 and Hiroki R. Ueda1,2,*
1Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan
2Laboratory for Synthetic Biology
3Laboratory for Mouse Genetic Engineering
RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
4School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001,
Australia
5Co-first author*Correspondence: uedah-tky@umin.ac.jp
http://dx.doi.org/10.1016/j.neuron.2016.02.032

SUMMARY

The detailed molecular mechanisms underlying theregulation of sleep duration in mammals are still
elusive. To address this challenge, we constructed asimple computational model, which recapitulates
the electrophysiological characteristics of the slowwave sleep and awake states. Comprehensive bifurcation analysis predicted that a Ca2+-dependent
hyperpolarization pathway may play a role in slowwave sleep and hence in the regulation of sleep duration. To experimentally validate the prediction, we
generate and analyze 21 KO mice. Here we foundthat impaired Ca2+-dependent K+ channels (Kcnn2
and Kcnn3), voltage-gated Ca2+ channels (Cacna1g
and Cacna1h), or Ca2+/calmodulin-dependent kinases (Camk2a and Camk2b) decrease sleep duration, while impaired plasma membrane Ca2+ ATPase(Atp2b3) increases sleep duration. Pharmacologicalintervention and whole-brain imaging validated that
impaired NMDA receptors reduce sleep durationand directly increase the excitability of cells. Basedon these results, we propose a hypothesis that aCa2+-dependent hyperpolarization pathway underlies
the regulation of sleep duration in mammals.
INTRODUCTION
The invention of the electroencephalogram (EEG) more than 85
years ago (Berger, 1929; Caton, 1875) enabled the global characterization of the mammalian brain’s electrical behavior. During
sleep, an EEG mostly displays high-amplitude low-frequency
fluctuations (Walter, 1937), which are generated by synchronizedslow oscillations of the cortical neuron membrane potential; during waking, an EEG exhibits low-amplitude high-frequency fluctuations, which are generated by the irregular firings of cortical
70 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

neurons (Steriade and McCarley, 2005). These two states aremutually exclusive in a normal brain, and their ratio is homeostatically regulated (Steriade and McCarley, 2005). For example,
sleep pressure (or the need for sleep) is high after sleep deprivation and gradually decreases during sleep. Importantly, sleep
pressure can be measured as an EEG slow-wave activity, i.e.,an EEG delta power (Borbély et al., 1981; Nakazawa et al.,
1978; Webb and Agnew, 1971). However, the detailed molecularmechanisms underlying mammalian sleep/wake cycles are still
elusive.
To elucidate the complex dynamics of sleep/wake cycles, anumber of computational models have been proposed. The
two-process model attempts to capture the relatively slow
and macroscopic dynamics of sleep by describing two regulatory processes, S and C, which are respectively a sleep-dependent homeostatic process and a sleep-independent circadian
process (Borbély, 1982). Process S represents the sleep pressure, which increases in proportion to the quantity and qualityof the preceding awake state and decreases in the subsequent sleep state; process C is sleep regulation by the circadianclock, which has been molecularly identified in fly and mammals
by forward genetics (Borbély, 2001; Lowrey and Takahashi,2011). Although the two-process and similar models can explain
the slow and macroscopic dynamics of sleep/wake cycles, they
are too simple to predict the details underlying molecular mechanisms, particularly of the homeostatic process S, which remain
elusive.
Another series of computational models were derived to
explain the relatively fast and microscopic dynamics of experimentally observed electrophysiological behavior. The highamplitude low-frequency oscillations are observed by EEG and
by intracellular and extracellular electrophysiological recording
during slow-wave sleep (SWS), which has two electro-physiologically different phases: the depolarized, bursting phaseand the hyperpolarized, silent phase (Steriade, 2003; Steriade
et al., 2001). Several computational models for these phases,
based on populations of interacting neurons, have been proposed (Bazhenov et al., 2002; Chen et al., 2012; Compte
et al., 2003; Hill and Tononi, 2005; Sanchez-Vives et al., 2010;

GABAA receptor

gGABA

Peak Freq.
(Hz)

Voltage-gated Na+ channel

gNa

Persistent Na+ channel

gNaP

2+

Voltage-gated Ca2+ channel

gCa

K+

Voltage-gated K+ channel

gK

K+

Leak K+-channel

gL

K+

Fast A-type K+ channel

gA+

Inwardly rectifying K+ channel

gAR

K+

Slowly inactivating K+ channel

gKS

K+

Ca2+-dependent K+ channel

gKCa

Ca

K

2+

Ca

B

gNMDA

+

Na

Soma

NMDA receptor

MP
(mV)

Na+

gAMPA

2+

Ca -pump/exchanger

ΤCa

Original

gNMDA

0.7

0.9

0
20

0 Time (sec) 5 0 Time (sec) 5

gCa

0.1

0.3

40

0
20

MP
(mV)

MembranePotential(mV)

Peak Freq.
(Hz)

-70

0 Time (sec) 5 0 Time (sec) 5

ΤCa

0.7

0.9

40

0
20

MP
(mV)

12

Intracellular
Calcium(μM)

0 Time (sec) 5 0 Time (sec) 5

25

-80

D

0.6

0
20

-80

30

0

gKCa

0.4
30

-80
Peak Freq.
(Hz)

DendriteC

AMPA receptor

MP
(mV)

Na+
Ca2+
Cl-

Peak Freq.
(Hz)

A

0

10

Time (sec)-80

E

Randomly generated parameter setsover 10,000,000

0 Time (sec) 5 0 Time (sec) 5

Wild-type
Intracellular calciumn = 1,113

Membrane potential
n = 1,113
-2

-1

0

1

-2

2

-1

0

1

2

Wake
Slow-wave with few spikes
Cyclic firing with weak synaptic currents

Normalized MP

P
Na
a
KC
Ca
KS
A
K
Na
L

10-2
10-1
1
Parameter values (Original = 1)

0

10

I

NMDA receptor KO

6

Voltage-gated Ca2+-channel KO

40

a

ΤC

D
M

G
AB
A

g A

AM
P

N

g A

aP
N

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20

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Bifurcation from Sleep to Awake (%)

40

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g

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ΤC

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g DA

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a

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100

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Bifurcation from Sleep to Awake (%)

60

0

-3

6

100

80

3

Bursting cycles

H

Wild-type
100

0

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Bifurcation from Sleep to Awake (%)

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10-3

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g g Τ
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A
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F

Parameter sets

Bifurcation analysis

Normalized Ca2+

Parameter sets

Parameter sets with slow-wave-sleep firing patterns
1,113

Figure 1. The Averaged-Neuron Model Predicts that a Ca2+-Dependent Hyperpolarization Pathway Plays a Pivotal Role in Slow-Wave Sleep(A) Schematic diagram of the AN model. Light red: extrinsic ion channels. Light blue: intrinsic ion channels and Ca2+-pumps/exchangers.
(B) Representative SWS firing patterns and intracellular Ca2+ concentration.
(legend continued on next page)

Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 71

Timofeev et al., 2000). These ‘‘neuron-network’’ models
elegantly recapitulate the behavior of cortical and thalamic neurons. These studies indicate that the transition from bursting
to silent phase is facilitated by (1) the enhancement of Ca2+and Na+-dependent K+ channels in pyramidal neurons, (2) the
inhibition of the excitatory synaptic current between pyramidal
neurons, or (3) the enhancement of feedback repression among
pyramidal neurons and interneurons.
To date, however, genetic evidence for these mechanisms
has been lacking especially in mammals. Moreover, the simulated slow-wave oscillation models are heavily dependent on
the given parameter values, and thus their predictive power islimited. In part, this is because the neuron-network models
possess such a huge parameter space that the number andtype of parameter sets (and a common critical pathway) thatcan generate slow-wave oscillations cannot be identifiedcomprehensively. A simple, but not simplistic, computationalmodel is required to elucidate the underlying mechanisms of
sleep/wake cycles.
Various hypotheses have been proposed to connect fast/microscopic and slow/macroscopic dynamics, but they have notbeen fully integrated into computational models. The ‘‘sleepinducing substance (SIS)’’ hypothesis (or ‘‘sleep-regulatory substance’’ hypothesis, in general) (Clinton et al., 2011; Krueger
et al., 2008; Obal and Krueger, 2003), in which SISs accumulate
during waking and gradually degrade during sleep, has existed
since 1909 (Ishimori, 1909). In the SIS hypothesis, process S is
intuitively represented as the concentration of SISs (Factor S).
Several candidate molecules for the SISs have been identified
including adenosine, nitric oxide, prostaglandin D, tumor-necrosis factor, interleukin 1, and growth-hormone-releasing hormone. All of these molecules can induce sleep when injected
into animals. However, it remains difficult to explain fast electrophysiological dynamics using the slow timescale observed forthe accumulating SISs. Therefore, it is important to identify themolecular targets of SISs to understand the shift between slow
and fast dynamics.
Alternatively but not exclusively, the ‘‘synaptic homeostasis’’
hypothesis proposes that synaptic strength of cortical neurons
represent process S (Tononi and Cirelli, 2006). For example, during waking, synaptic strength increases via learning processes
such as long-term potentiation, whereas during much of sleep,
synaptic strength decreases. Hence, the synaptic homeostasis
hypothesis attempts to connect the fast dynamics of the corticalneurons’ membrane potential (slow-wave oscillations) withthe relatively slow dynamics for process S (synaptic strength).
Although some experimental results support the synaptic
homeostasis hypothesis (Vyazovskiy et al., 2008), others challenge it (Yang et al., 2014). In addition, computational neuronnetwork models apparently contradict this hypothesis, because

the enhancement of excitatory synaptic current between pyramidal neurons (i.e., increased synaptic strength) in the current
neuron-network models enhances the awake rather than thesleep state, by depolarizing the pyramidal neurons. Hence,
we need a computational model that is compatible with the relatively slow homeostatic dynamics (e.g., of process S) and therelatively fast electrophysiological dynamics (e.g., slow-wave
oscillations).
RESULTSThe Computational Averaged-Neuron Model
Recapitulates Slow-Wave Sleep Firing Patterns
To reproduce the oscillating bursting and silent phases ofcortical neurons during slow-wave sleep, we simplified theneuron-network models (Bazhenov et al., 2002; Chen et al.,2012; Compte et al., 2003; Hill and Tononi, 2005; Sanchez-Vives
et al., 2010; Timofeev et al., 2000). We first performed mean-fieldapproximations of a population of neurons to construct an ‘‘averaged-neuron’’ model (AN model) (Figure 1A; Table S1). We
assumed, as a first-order approximation, that the averaged
neuron could interact with itself (directly or indirectly) throughexcitatory (Na+ currents mediated by AMPA receptors, IAMPA
and Ca2+ currents mediated by NMDA receptors, INMDA), or
inhibitory (Cl currents mediated by GABAA receptors, IGABA)
synaptic currents. In addition to extrinsic currents, we incorporated, as intrinsic currents, depolarizing Na+ and Ca2+ currents
and hyperpolarizing K+ currents. The depolarizing Na+ andCa2+ currents are mediated by voltage-gated (INa) or the persistent (INaP) Na+ channels and voltage-gated Ca2+ channels (ICa),
respectively, whereas the K+ currents are mediated by several
types of K+ channels, including voltage-gated (IK), leak (IL), fastA-type (IA), inwardly rectifying (IAR), slowly inactivating (IKS), andCa2+ dependent (IKCa). We also assumed that Ca2+ pumps/
exchangers turn over Ca2+ with the time constant, tCa. Notably,
the AN model recapitulated the SWS firing patterns, with its alternating bursting and silent phases (Figure 1B). Moreover, theparameter values in the AN model that generated slow-wave
oscillations were similar to those in neuron-network models,
implying that sufficient information to generate SWS was preserved in this simple model.
Close inspection showed that the SWS firing patterns in the
AN model were characterized by a bursting phase, in which
Ca2+ enters the cell mainly through the NMDA receptor (NMDAR)
and voltage-gated Ca2+ channels (Cav channels), followed by asilent phase, in which accumulated Ca2+ evokes Ca2+-dependent K+ channels (KCa channels) (Figure 1B; Figure S1A). It
then exits the cell through Ca2+ pumps/exchangers with tCa (Figure 1A). To investigate the role of this Ca2+-dependent hyperpolarization pathway in the SWS firing patterns, we performed a(C) The downregulation of the conductance of KCa channels (gKCa), Cav channels (gCa), and NMDARs (gNMDA) and the time constant for Ca2+ efflux (tCa) result in thetransitions from SWS (red) to awake (blue) firing patterns.
(D) The workflow of a random parameter search to identify parameter sets that result in SWS firing patterns.
(E) The normalized membrane potential and intracellular Ca2+ are shown for all 1,113 parameter sets yielded by the parameter search.
(F) Bifurcation analysis of single parameter sets in 1,113 parameter sets that elicited SWS firing patterns.
(G–I) Summary of bifurcation analysis under WT (G), NMDAR KO (H), or Cav channel KO (I) conditions. Red text indicates the conductance and time constantrelated to the Ca2+-dependent hyperpolarization pathway.

72 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

bifurcation analysis (i.e., a gradual change in parameter values)of the conductance of each intrinsic and extrinsic current andof the Ca2+ efflux rate. As expected, we found that downregulating the conductance of the NMDAR (gNMDA), Cav channels (gCa),
KCa channels (gKCa), and/or reducing the time constant (tCa) of
Ca2+ efflux mediated by Ca2+ pumps/exchangers caused thetransition from SWS to awake firing patterns (Figure 1C; Figures
S1B–S1G), implying that this Ca2+-dependent hyperpolarization
pathway may have a pivotal role in regulating the SWS firing
patterns.
The Averaged-Neuron Model Predicts that a Ca2+Dependent Hyperpolarization Pathway May Play a Rolein Slow-Wave Sleep and hence in the Regulation of Sleep
Duration
To investigate the generality of these findings, we sought to identify a broad variety of parameter sets that could generate SWS
firing patterns (Figure 1D). We comprehensively searched forthe conductance of the intrinsic (0.01–100 mS/cm2) and extrinsic(0.002–20 mS) currents and the time constant for Ca2+ efflux (10–1,000 ms) that resulted in this pattern. In this search, we generated more than 10,000,000 random parameter sets from exponential distributions. From these, we identified 1,113 parameter
sets that generated SWS firing patterns. The success rate was5.7 3 10 3%. These patterns exhibited intracellular Ca2+ waves
that were coupled with the membrane potential (Figure 1E).
If a particular molecular mechanism is targeted by the homeostatic process S, it should control, directly or indirectly, thetransitions between the SWS and awake state, because theaccumulation of factor S (or the increase of its quality) presumably facilitates the transition from the awake state to SWS, while
its degradation (or the decrease of its quality) enhances the transition from SWS to the awake state. To identify potential targets
for factor S, we performed a comprehensive bifurcation analysisof the 1,113 SWS parameter sets and searched for channel
conductance or the time constant that could contribute to thetransition from SWS to awake firing patterns. In this analysis,
each channel conductance or the time constant was graduallychanged within the range from 10 3 to 10 times its value inthe original parameter set (Figure 1F). Indeed, downregulationof the Ca2+-dependent hyperpolarization pathway led to thetransition from slow-wave-sleep to awake firing patterns in 848(76.2%), 581 (52.2%), and 861 (77.4%) cases with altered
gKCa, gNMDA, and tCa, respectively (Figure 1G). These results
were consistent with the suggested relationship between
cortical delta waves and KCa conductance (Steriade et al.,1990) and also predicted that an impairment of KCa channels
or NMDARs, or an enhancement of Ca2+ pumps/exchangers
would facilitate the transition from SWS to awake firing patterns
and hence decrease the sleep duration (i.e., impairment of Ca2+pump/exchanger would increase the sleep duration).
To verify the role of the Ca2+-dependent hyperpolarization
pathway in SWS, we conducted the parameter searches under
the condition of knocked out (KO) KCa channels (gKCa = 0) or
NMDARs (gNMDA = 0). The success rate of the KCa channel KOparameter search was severely decreased (1.1 3 10 5%)
compared to the wild-type (WT) parameter search, suggestingthat KCa channels are important for generating SWS firing pat-terns, whereas the success rate of the NMDAR KO parametersearch was not dramatically reduced (Figure S1L). Since theintracellular Ca2+ oscillated together with the membrane potential of SWS firing patterns in the NMDAR KO search (Figure S1H),
we speculated that there might be an alternative Ca2+ entry
mechanism. Indeed, bifurcation analysis revealed that changes
in gCa led to the transition more often (836 cases, 71.1%, Figure 1H) in the NMDAR KO search than in the WT parametersearch (108 cases, 9.7%, Figure 1G).
Therefore, we next conducted the parameter search under the
double KO of Cav channels (gCa = 0) and NMDARs (gNMDA = 0) to
confirm the redundant role of these Ca2+ entry mechanisms. In
fact, the success rate of the parameter search was severely
decreased (9.2 3 10 5%) compared to the WT parametersearch (Figure S1L). We also noted that the success rate was
not reduced under the Cav channel single KO search (Figure S1L)
and that these SWS firing patterns were also accompanied byintracellular Ca2+ waves (Figure S1I). As expected, we foundthat the downregulation of NMDARs (gNMDA) under the Cav channel KO condition more often (895 cases, 88.5%, Figure 1I) led tothe transition than in the WT parameter search (581 cases,
52.2%, Figure 1G). Overall, these results suggested that Cav
channels and NMDARs have redundant roles in Ca2+ entry to
generate SWS firing patterns and hence that the Ca2+-dependent hyperpolarization pathway is involved in SWS. We also
found that there were no marked changes in the success rate
(7.6 3 10 3%) or bifurcation analysis when we imposed the
KO of GABAA receptors on the random parameter search (Figures S1J and S1K). This result is consistent with a previous
report, in which the hyperpolarization during SWS is not mediated by GABAergic inhibition (Steriade et al., 2001). From thesebifurcation analyses, we predicted that impairment of ion channels related to the Ca2+-dependent hyperpolarization pathway
might decrease the sleep duration by having the cortex tend to
generate awake firing patterns than SWS firing patterns. On
the other hand, we predicted the impairment of Ca2+-pump/exchanger would increase the sleep duration by facilitating theCa2+-dependent hyperpolarization pathway.
Impairment of Ca2+-Dependent K+ Channels Decreases
Sleep Duration
To validate the AN model, we first tested the prediction that
downregulation of KCa channels would decrease the sleep
duration. In the mouse genomes, eight genes (KCa1.1 [Kcnma1],
KCa2.1 [Kcnn1], KCa2.2 [Kcnn2], KCa2.3 [Kcnn3], KCa3.1 [Kcnn4],
KCa4.1 [Kcnt1], KCa4.2 [Kcnt2], and KCa5.1 [Kcnu1]) are categorized into the KCa channel family according to their sequencesimilarity (Wei et al., 2005).
To knock out all of the KCa channel family members, we usedthe ‘‘triple-target CRISPR’’ method (Sunagawa et al., 2016)(Figures S2A–S2H; Table S2). We also used highly accurate
sleep/wake recording system called the Snappy Sleep Stager(SSS) (Sunagawa et al., 2016). We noted that there was no significant influence on sleep phenotype of the triple-target CRISPR
itself by creating Tyrosinase (Tyr) KO mice (Sunagawa et al.,2016). Sleep/wake phenotyping was performed for animals
with KO genotype, which was confirmed by qPCR and/or genomic sequencing (Figure S3; Tables S3 and S4). As
Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 73

Kcnma1 (n = 5)

2

Mouse

1

3

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5

Kcnn1 (n = 7)

6

7

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Kcnn4 (n = 13)

1

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Kcnn2 (set1, n = 5)

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7

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Kcnu1 (n = 15)

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Time (Day)B

Kcnma1 (n = 5)

Kcnn1 (n = 7)

Kcnn2 (set 1, n = 5)

Kcnn3 (set 1, n = 6)

0

0

60

Sleep duration (min)

30
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24

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Kcnn4 (n = 13)

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6

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Time (hr)C
Kcnma1
Kcnn1
Kcnn2 (set 1)
Kcnn3 (set 1)
Kcnn4
Kcnt1
Kcnt2
Kcnu1
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Sleep duration (min)
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Kcnn3 (set 2, n = 7)E

Kcnn3 (set 2, n = 7)

Mouse

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0.04

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Amplitude

Sleep duration (min)600

Sleep duration (min)

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After SD
Basal

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n.s.

n.s.

n.s.

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Basal

300

NREM duration (min)400

After SD
Basal

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n.s.

400

REM duration (min)

WT

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74 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

40
30
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Basal

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800

Basal

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EEG/EMG

After SD3 4 5Time (Day)

0.04

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NREM duration (min)

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n.s.

WT (n = 4)

Mouse

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H

EEG/EMG
Sleep duration (min)60
0

Kcnn2 (n = 9)

0.04

PWS

After SDG

0.6

Amplitude

REM duration (min)

0.2

900

Basal

800

Sleep duration (min)700

After SD

600

Sleep duration (min)Figure 2. Impairment of Ca2+-Dependent K+
Channels Decreases Sleep Duration

Kcnn3 (set 1, n = 6)

80
60
40
20
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Sleep duration (min)

A

60

0

(A) Sleep duration (per hour) over 6 days for KO
mice of eight KCa channels.
(B) Sleep duration (per hour) over 24 hr, averaged
over 6 days in the KO mice of KCa channels. Redlines: the mean sleep duration at each time of day
for each strain. Gray: WT (n = 108). Shaded area:
SEM for each time point.
(C) Distributions of sleep/wake parameters of the
KO mice of KCa channels. Histogram: WT (n = 108).
Black dashed line and gray shade: the mean and
1 SD range from the recording of WT.
(D–F) Sleep-duration phenotype for another groupof Kcnn2 and Kcnn3 KO mice (set 2). Sleep duration (per hour) over 6 days (D), sleep duration(per hour) over 24 hr, averaged over 6 days (E),and distributions of sleep/wake parameters (F) are
shown.
(G and H) Sleep-duration phenotype based on
EEG/EMG for WT (n = 4) and Kcnn2 KO mice(set 1, n = 9). Sleep duration (per hour) over 6 days
(G) and sleep duration, NREM sleep (SWS) duration, and REM sleep duration for 1 day are shownin bar graph (H).
(I) Sleep-duration phenotype based on EEG/EMG
for sleep-deprived WT (n = 3) and Kcnn2 KO mice(set1, n = 7). Sleep duration, NREM sleep (SWS)
duration, and REM sleep duration for ZT12–ZT24
are shown in bar graph.
WT, C57BL/6N male mice. Error bars, SEM, *p <0.05, **p < 0.01, ***p < 0.001, see also Supplemental Experimental Procedures for details.

predicted, Kcnn2, Kcnn3, Kcnn4, Kcnt1, and Kcnu1 KO mice exhibited significantly decreased sleep duration (Figures 2A and
2C). The Kcnn2 KO mice caused the largest decrease in sleep
duration, i.e., the strongest short-sleeper phenotype, 606.3 ±14.7 min (mean ± SEM, n = 5), which was 129.3 min (2.6 SD)
shorter than that of WT mice (p < 0.001). Kcnn3 KO mice also exhibited a strong short-sleeper phenotype, 629.1 ± 11.8 min(mean ± SEM, n = 6), which was 106.6 min (2.1 SD) shorter
than that of WT mice (p < 0.001). In fact, the Kcnn2 and Kcnn3
KO mice exhibited shorter sleep durations than WT mice overmost times of the day (Figure 2B). Both of these mutants displayed lower Pws (the transition probability that an awake state
will switch to a sleep one) but a normal Psw (the transition probability that a sleep state will switch to an awake one) comparedwith WT mice (Figure 2C), indicating that their short-sleeper
phenotype was caused by an increase in awake-state stabilization (i.e., longer episode durations of an awake state) but notby a decrease in sleep-state stabilization. We note that Kcnn2
KO mice were previously reported to have a (not-significant)tendency of decrease in sleep duration (Cueni et al., 2008). Our
non-invasive sleep phenotyping clearly revealed the significant
short-sleeper phenotype in Kcnn2 KO mice. As further validation, we generated another group of Kcnn2 and Kcnn3 KO
mice by an independent CRISPR/Cas9 probe set (set 2, Figures
S2J and S2K; Table S2) and confirmed the observed shortsleeper phenotypes (Figures 2D–2F). These results strongly suggested that the observed short-sleeper phenotype of Kcnn2 andKcnn3 KO mice cannot be attributed to the possible off-target
effect of CRISPR, but rather to the common genomic defects
in Kcnn2 and Kcnn3 gene, respectively.
To exclude the possibility that a gene KO might affect the
respiration phenotype and hence that SSS system could miscalculate their sleep duration, we conducted the basal EEG/EMG
recording of Kcnn2 KO mice. As a result, the observed shortsleep phenotype of Kcnn2 KO mice was also confirmed by thebasal EEG/EMG recording, where Kcnn2 KO mice exhibitedthe significant decrease of NREM sleep (SWS) duration in addition to the significant decrease of total sleep duration (Figures
2G, 2H, and S2L). To further validate the role of Kcnn2 gene in
the regulation of sleep duration, we conducted a sleep deprivation (SD) experiment to Kcnn2 KO mice and WT mice (Figure 2I).
We compared NREM sleep (SWS) duration between the basal
recording and the recovery period after 8 hr SD for Kcnn2 KO
mice and WT mice, respectively. The WT mice exhibited a significant change in NREM sleep (SWS) duration (ZT12–ZT24)
between the basal recording and the recovery period after 8 hr
SD (ZT4–ZT12) (p < 0.01, Figure 2I). On the other hand, Kcnn2
KO mice exhibited no significant change in NREM sleep (SWS)
duration (Figure 2I), indicating a change in the regulation of sleep
duration in Kcnn2 KO mice. These results further support the hypothesis that the Ca2+-dependent hyperpolarization mechanism
underlies the regulation of sleep duration in mammals.
We also noted that Kcnma1 exhibited decreased sleep duration during morning and increased sleep duration during night(Figure S2I), which is consistent with the previously reported
circadian phenotype of the KO animal for this gene (Meredith
et al., 2006), further validating our methods for producing andphenotyping KO mice. Taken together, these results validated

the first prediction of the AN model that an impairment of KCachannels would decrease sleep duration.
Impairment of Voltage-Gated Ca2+ Channels Decreases
Sleep Duration
To further validate the AN model, we next tested the second prediction that the downregulation of Cav channels would decrease
the sleep duration. The Cav channels are Ca2+ channels that open
in response to membrane depolarization resulting in Ca2+ influx.
They are usually composed of four or five distinct subunits (a1, a2,b, g, and d). Among them, the a1 subunit mainly determines thechannels’ responsiveness and behavior in response to membrane depolarization. At least ten a1 subunits are found in mammals, and they are divided into three well-defined and closely
related groups by their sequence similarity (Catterall et al., 2005).
We created KOs of all of the ten Cav channel a1 subunits(Cav1.1 [Cacna1s], Cav1.2 [Cacna1c], Cav1.3 [Cacna1d], CaV1.4[Cacna1f], Cav2.1 [Cacna1a], Cav2.2 [Cacna1b], CaV2.3[Cacna1e], CaV3.1 [Cacna1g], CaV3.2 [Cacna1h], and CaV3.3[Cacna1i]) by injecting three gRNAs for each gene (Figures
S4A–S4J and S4M–S4R; Tables S2 and S3). We confirmed thelethal phenotype seen in Cacna1s, Cacna1c, and Cacna1a biallelic KO mice generated by conventional methods (Austin et al.,
2004; Jun et al., 1999; Seisenberger et al., 2000) also occurred in
KO mice generated by the triple-target CRISPR method. We also
observed the lethal phenotype in Cacna1d KO mice generated
by the triple-target CRISPR method, which targeted all known
isoforms of Cacna1d, including two isoforms that were not targeted in viable Cacna1d KO mice generated by conventional
methods (Namkung et al., 2001; Platzer et al., 2000). As predicted from the AN model, Cacna1g, Cacna1h, and Cacna1f
KO mice exhibited significant decrease in sleep duration (Figures 3A–3C). In particular, Cacna1g and Cacna1h KO mice
showed strong short-sleeper phenotypes, with sleep durationof 655.1 ± 13.4 min (mean ± SEM, n = 10) and 647.6 ±20.1 min (mean ± SEM, n = 5), respectively, which were80.5 min (1.59 SD, p < 0.001) and 88.1 min (1.73 SD, p <0.001) shorter than that of WT (Figure 3C). As further validation,
we generated another group of Cacna1g and Cacna1h KO
mice by independent CRISPR/Cas9 probe sets (set 2) andconfirmed the observed short-sleeper phenotypes (Figures
3D–3F; Figures S4K, S4L, and S4S–S4U; Tables S2 and S3).
These results strongly suggested that the short-sleeper phenotype of Cacna1g KO and Cacna1h KO mice cannot be attributed
to the possible off-target effect of CRISPR but rather to the common genomic defects in Cacna1g and Cacna1h genes, respectively. The short-sleeper phenotype of Cacna1g KO mice in thisstudy was consistent with the previous reports (Anderson
et al., 2005; Lee et al., 2004). No significant change in sleep duration of Cacna1b KO mice was also consistent with the previous
report (Beuckmann et al., 2003). Taken together, these results
confirmed the second prediction that an impairment of Cav
channels would decrease sleep duration.
Impairment of NMDA Receptors Decreases Sleep
Duration
To further validate the AN model, we examined the third predictionthat downregulation of NMDARs would decrease the sleep
Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 75

Sleep duration (min)60
0A
Cacna1b (n = 3)

Cacna1e (n = 3)

B
Cacna1f (n = 10)

Cacna1b (n = 3)

Cacna1e (n = 3)

Cacna1f (n = 10)

0

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60

1

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3

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Cacna1g(set1, n = 10)

3

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1

Cacna1h(set1, n = 5)

2

3

4

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6

Sleep duration (min)

Mouse

30
7

Cacna1i (n = 8)

0
6

12

18

24

6

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6

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18

Cacna1g (n = 10)

Cacna1h (n = 5)Cacna1i (n = 8)

0

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6

0.04

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24

60
30

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Time (Day)

6

12

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12

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Time (hr)C
Cacna1b
Cacna1eCacna1f
Cacna1g (set1)
Cacna1h (set1)
Cacna1i
700

800

900

0.2

0.4

D

0.6

Cacna1h (set 2, n = 5)

Mouse

Cacna1g (set 2, n = 5)

2

3

4

5

6

7

0.04

0.06

0.02

PWS

PSW

Cacna1g (set 2, n = 5)Cacna1h (set 2, n = 5)E

Sleep duration (min)
60
0

1

0.02

0.8

Amplitude

1

2

3

4

5

6

7

Sleep duration (min)600

Sleep duration (min)

60
30
0
0

6

12

18

24

Time (Day)

0

6

12

18

24

Time (hr)F
Cacna1g (Set2)
Cacna1h (Set2)
600

700

800

900

Sleep duration (min)

0.2

0.4

0.6

0.02

0.8

0.04

PWS

Amplitude

0.06

0.02

0.04

0.06

PSW

Figure 3. Impairment of Voltage-Gated Ca2+ Channels Decreases Sleep Duration(A) Sleep duration (per hour) over 6 days for KO mice of the Cav channel family.
(B) Sleep duration (per hour) over 24 hr, averaged over 6 days in KO mice of the Cav channel family. Red lines: the mean sleep duration at each time of day for eachstrain. Gray: WT (n = 108). Shaded area: SEM for each time point.
(C) Distributions of sleep/wake parameters of KO mice of the Cav channel family. Histogram: WT (n = 108). Black dashed line and gray shade: the mean and 1 SD
range from the recording of WT.
(D–F) Sleep-duration phenotype for another group of Cacna1g and Cacna1h KO mice (set 2). Sleep duration (per hour) over 6 days (D), sleep duration (per hour)over 24 hr, averaged over 6 days (E), and distributions of sleep/wake parameters (F) are shown.
WT, C57BL/6N male mice. Error bars, SEM, *p < 0.05, **p < 0.01, ***p < 0.001, see also Supplemental Experimental Procedures for details.

duration. In the mouse genome, seven different subunits are categorized into the NMDAR family based on their sequence similarity.
These NMDAR subunits form three well-defined and closely
related subfamilies: the GluN1 (Nr1) subunit, four distinct GluN2
subunits, GluN2A (Nr2a), GluN2B (Nr2b), GluN2C (Nr2c), andGluN2D (Nr2d), and two GluN3 subunits, GluN3A (Nr3a) and
GluN3B (Nr3b). NMDARs function as a heterotetramer in which
76 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

Nr1 subunits typically assemble with Nr2 subunits or a mixtureof Nr2 and Nr3 subunits (Cull-Candy and Leszkiewicz, 2004; Paoletti, 2011; Traynelis et al., 2010). In contrast to the KCa channelfamily, KO mice of either Nr1 or Nr2b, which are two main subunitsof NMDAR family, exhibit lethal phenotype (Forrest et al., 1994;
Li et al., 1994; Sunagawa et al., 2016). Therefore, we inhibitedthe NMDARs pharmacologically. First, the intraperitoneal (i.p.)

A PCP

Figure 4. Impairment of NMDA Receptors
Decreases Sleep DurationB(mg/kg)

26.7

0

200

0.06
0.09

150

0.04

100

0.02

50
0

72

PCP (mg/kg)

2

0.04

100

0.02

50
0

0.00

(A and C) Dose-dependent sleep phenotype resulting from a single i.p. injection of phencyclidine(PCP) (A) and MK-801(C). Heatmaps: the sleep
duration (per hour) for all mice. Red bar: the periodused for comparing the parameters in (B) and (D),respectively.
(B and D) Both PCP (B) and MK-801 (D) reduced
the sleep duration and Pws in a dose-dependent
manner for 5 hr (ZT2 to 6) after their i.p. injections.
(E) Sleep phenotype resulting from a single i.p.
injection of MK-801 by the EEG/EMG recording.
(F) The sleep duration, NREM sleep (SWS) duration, REM sleep duration, Pws and Psw for 5 hr (ZT2
to ZT6) after MK-801 i.p. injection by the EEG/
EMG recording.
Error bars, SEM, *p < 0.05, **p < 0.01, ***p < 0.001,
see also Supplemental Experimental Proceduresfor details.

72

F

EEG/EMG

EEG/EMG

NREM duration (min)60

Sleep duration (min)36
48Time (hr)

20
2
2

24

0.
0

12

0.0

MK−801 (mg/kg)

20
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20
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PSW

60

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Sleep duration (min)

0.2

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200

PWS

i.p.

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Sleep duration (min)DMK-801(mg/kg)

E

0.00

0

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.7
26
.3
13

36
48
Time (hr)

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.7
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.3
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Sleep duration (min)Sleep duration (min)

i.p.

250
150

200
150

PSW

200

PWS

REM duration (min)Sleep duration (min)would increase the excitability of corticalneurons, especially glutamatergic pyrai.p.
60
midal neurons that we modeled in
100
100
0
50
50
this study. This prediction is some00what counter-intuitive, because NMDARs
0
2
0
2
2
n.s.
n.s.
mediate ‘‘excitatory’’ positive-ion cur0
0
12
24
36
48
60
72
0.12
25
rents from outside to the inside ofTime (hr)
0.04
20
0.09
neurons, but it makes sense, given that
0.03
15
the Ca2+-dependent hyperpolarization
0.06
0.02
10
pathway is initiated by Ca2+ entry through
0.03
0.01
5
NMDARs. To test this prediction, we per0
0.00
0.00
0
2
0 2
0 2
formed a whole-brain imaging of neural
MK−801 (mg/kg)
activity at single-cell resolution using
CUBIC analysis (Susaki et al., 2014) andArc-dVenus mice, in which a destabilized
injection of phencyclidine (PCP), a well-known antagonist of version of yellow fluorescence protein Venus is expressed upon
NMDARs, into WT mice resulted in significantly decreased sleep neural excitation, under control of Arc gene promoter. To miniduration as predicted (Figures 4A and 4B). We also confirmed mize temporal variations, we sampled Arc-dVenus mice every
this decreased sleep duration by a more specific NMDAR antag- 6 hr for 1 day under constant darkness, with or without the
onist, MK-801 (Figures 4C and 4D) (Koek et al., 1988). The chronic administration of NMDAR antagonist, MK-801. We
observed short-sleep phenotype was also confirmed by EEG/ noticed a temporal variation of Venus signals in the Arc-dVenus
EMG recording, where the MK-801-treated mice exhibited the mice without drug administration (Figure S6A; Movie S1). Venus
significant decrease of NREM sleep (SWS) duration in addition signals were relatively low during the subjective day (circadian
to a significant decrease of total sleep duration (Figures 4E and time [CT] 4 and 10), when mice are relatively inactive, and were4F). We also note that this short-sleep phenotype was observed relatively high during the subjective night (CT16 and CT22), ain the other strain of mice, C57BL/6J (Figures S5A–S5D). In addi- higher activity time for mice (Figure S6B). In contrast, in thetion, we chronically administered MK-801 to C57BL/6J mice (the MK-801-treated Arc-dVenus mice, the Venus signals exhibited
drug was diluted into sterile water to be 16, 32, 64, and 96 mg/L different temporal patterns (Figure S6A; Movie S2). Importantly,
and administered as drinking water) and confirmed the decreased these altered Venus expression patterns matched the altered
sleep-duration phenotype (Figures S5E–S5G). Taken together, behavioral patterns of the MK-801-treated mice (Figure S6B).
these results confirmed our prediction that impaired NMDAR Notably, close inspection of whole-brain single-cell resolution
imaging data revealed that some cells were highly activatedfunction would decrease sleep duration.
with high-intensity Venus signals both in MK-801-treated anduntreated mice (Figure S6C). Importantly, the MK-801-treatedImpairment of the Ca2+-Dependent Hyperpolarization
mice exhibited significantly more hyper-activated cells than the
Pathway Increases Neural Excitation
By using the chronic pharmacological impairment of NMDARs, untreated mice did (Figure 5A). We also noticed that the total
we also tested our prediction that this impairment of NMDARs number of hyper-activated cells was well correlated with the total
MK-801(mg/kg)

Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 77

B

A Whole brain
CT10

CT16

CT22

MK-801 (+) MK-801 (-)

CT4

Figure 5. Impairment of the Ca2+-Dependent Hyperpolarization Pathway Increases
Neural Excitation

Normalized intensity-1 0 1 2 3 4 5

x103
80
Cells

60

(A) Temporal variation of the number of hyperactivated cells in the whole brain. Left: representative 3D-reconstituted brain images of the
Arc-dVenus signals (green) from hyper-activatedcells shown for mice with or without the chronic
administration of MK-801. Gray, TOPRO3 signals.
Scale bar, 2,000 mm. Right: the number of hyperactivated cells in the whole brain. CT, Circadiantime.
(B) Heatmap of the brain regions, clustered basedon the normalized intensity across the four time
points.
(C–E) Temporal variation of the number of hyperactivated cells in specific clusters. Left: 3Dreconstituted brain images of the Arc-dVenus
signals from hyper-activated cells that located inregions belonging to the same heatmap cluster(shown in pink in C, yellow in D, and blue in E).
Gray, TOPRO3. Scale bar, 2,000 mm. Right: estimated number of hyper-activated cells.
(F) Hierarchical representation of the Allen Brain
Atlas annotation. Nodes represent brain regions
and are colored based on the cluster they belong
in the heatmap. Light gray: low-signal regions
excluded from the clustering. CH, cerebrum; BS,brain stem; CB, cerebellum.
Error bars, SEM.

MK-801(-)
MK-801(+)

40
20
0

CT4 CT10CT16CT22

C Pink cluster
CT10

CT16

CT22

MK-801 (+) MK-801 (-)

CT4

Cells / mm3

400
300

MK-801(-)
MK-801(+)

200
100
0

CT4 CT10CT16CT22

D Yellow cluster
CT10

CT16

CT22

MK-801 (+) MK-801 (-)

CT4

Cells / mm3450

MK-801(-)
MK-801(+)

300
150
0

CT4 CT10CT16CT22

E Blue cluster
CT10

CT16

CT22
120
Cells / mm3MK-801 (+) MK-801 (-)

CT4

MK-801(-)
MK-801(+)

80

other time points (CT10, CT16, or CT22)(Figures 5D and 5F). In contrast, brain
0
CT4 CT10CT16CT22
regions in the blue cluster such as hippocampus exhibit higher intensity at certain
MK-801 (-)
MK-801 (+)
time points in the untreated mice,
Hippocampal
F
whereas they exhibit relatively low intenCerebral nuclei
Hindbrain
formationsity in MK-801-treated mice (Figures 5E
Cerebellar cortex
and 5F).
ThalamusIn this study, we modeled glutamaterIsocortex
gic pyramidal neurons in the layers
CBCH
4-6. To stringently validate the prediction
CorticalBS
subplatefrom the AN model, we next performed
MidbrainOlfactory areas
Hypothalamus
temporal profiling of cellular activities fordifferent layers in the cerebral cortex.
We found each layer exhibits higher inintensity of hyper-activated cells (Figure S6D), which allowed us tensity in subjective day (CT4 or CT10) in the MK-801-treatedto perform the simplified computational analysis and obtained mice than in the untreated mice, whereas they exhibit comparaspatio-temporal profiling of cellular activities for different regions ble intensity in subjective night (CT16 or CT22) (Figures 6A–6C;
over the entire brain (Figure 5A). Subsequent hierarchical clus- Movie S4). In particular, the layer 4-6 exhibited significant (p <tering analysis (Figure 5B; Table S6) revealed that individual brain 0.05) and strongest (400 hyper-activated cells/mm3) response
regions follow a few common patterns of cellular activities (Fig- to the MK-801 injection (Figures 6C and 6D, right). To determine
ures 5C–5E; Figure S6E; Movie S3). For example, brain regions the cellular identify of hyper-activated cells in the layer 4-6 of thein the pink cluster such as various regions of cerebral cortex MK-801-treated mice, we conducted fluorescence in situ hybridexhibit higher intensity in subjective day (CT4 or CT10) in the ization (FISH) analysis of WT mice intraperitoneally injected with
MK-801-treated mice, whereas they exhibit comparable inten- or without MK-801 and performed double-staining of c-Fos andsity in subjective night (CT16 or CT22) (Figures 5C and 5F). Brain Nr2b (Figures S6F and S6G; Figures 6E and 6F; Table S5),
regions in the yellow cluster such as cerebellar cortex exhibit Vglut1/2 (glutamatergic cell makers, Figure S6H; Figures 6Hmuch higher intensity in early subjective day (CT4) in the MK- and 6J; Table S5), or Gad1/2 (GABAergic cell makers, Figure S6I;
801-treated mice, whereas they exhibit comparable intensity in Figures 6I and 6J; Table S5), respectively. As a result, we found78 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

CT16

CT22

CT4

CT10

CT16

CT22

CT4

CT10

40

A Layer 1

B Layer 2 and 3
CT4

CT10

CT16

CT22

CT10

CT16

CT22

CT16

CT22

MK-801 (+)

MK-801 (-)
MK-801 (+)

C Layer 4, 5 and 6

D
Layer 2 and 3

200

MK-801 (+)

4

10

16

22

Nr2b

F

Merge

c-Fos

Nr2b

400

0

200

4

10

16

0

22

4

10

CT

16

22

CT

G Layer 4, 5 and 6

Merge

L1

c-Fos
c-Fos/Nr2b

350

L1

300

L2/3

L2/3

MK-801 (+)

L4

MK-801 (-)

MK-801(-)
MK-801(+)

600

100

CT

c-Fos

Layer 4, 5 and 6800

MK-801(-)
MK-801(+)

200

100
0

300

Cells / mm3

Cells / mm3MK-801(-)
MK-801(+)

Cells / mm3MK-801 (-)

Layer 1
300

L5

250

L4

Cells

CT4

E

CT10

MK-801 (-)

CT4

L5

200
150
100

L6

L6

50
0
MK (-) MK (+)

H

I
Vglut1/2

c-Fos

Merge

MK-801 (+)

MK-801 (+)

c-Fos

J
Layer 4, 5 and 6
Gad1/2

Layer 4, 5 and 6
c-Fos
c-Fos
c-Fos/Vglut
c-Fos/Gad
100
100

Merge
Positive cells (%)

Layer 4, 5 and 6

80

80

60

60

40

40

20

20
0

0
MK (+)MK (+)

Figure 6. Impairment of the Ca2+-Dependent Hyperpolarization Pathway Increases the Excitability of Glutamatergic Pyramidal Neurons(A–D) 3D-reconstituted brain images of the Arc-dVenus signals from hyper-activated cells belonging to the layer 1 (A, cyan), layer 2 and 3 (B, magenta), and layer
4, 5, and 6 (C, yellow). Gray, TOPRO3 signals. Scale bar, 2,000 mm. (D) Estimated number of hyper-activated cells.
(E and F) Images of FISH analysis using c-Fos and Nr2b probes of the cerebral cortex from mice subjected to i.p. saline (E) or MK-801 (F) injection. Scale bar,
100 mm.
(G) The activated cells expressing c-Fos mRNA in layer 4, 5, and 6 of c-Fos and Nr2b double-stained sections (E and F) were counted.
(H and I) Images of FISH analysis of layer 4, 5, and 6 from MK-801-treated mice using c-Fos and either Vglut1/2 (H) or Gad1/2 (I) probes, respectively. White
arrowheads, Vglut1/2-negative cell. White arrows, Gad1/2-positive cell. Scale bar, 100 mm.
(J) The active cells responded to MK-801-injection in layer 4, 5, and 6 of c-Fos and Vglut1/2 or Gad1/2 double-stained sections (H and I) were counted.
Error bars, SEM.

Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 79

that MK-801-treated mice exhibited significantly more c-Fos-expressing cells in the layer 4-6 than the untreated mice did (Figures 6E–6G, p < 0.05). Importantly, almost all c-Fos-expressing
cells in the layer 4-6 also expressed Nr2b mRNA (Figure 6G, p <0.05), strongly suggesting that pharmacological inhibition of
NR2B-containing NMDARs increased not indirectly but rather
directly the excitability of the neuron. The Ca2+-dependent
hyperpolarization hypothesis predicts that the inhibition of
Ca2+ entry will induce the excitability of both glutamatergic and
GABAergic neurons through the inhibition of Ca2+-dependent
hyperpolarization pathway. Indeed, both numbers of glutamatergic and GABAergic neurons that express c-Fos were increasedin MK-801-treated mice compared to untreated mice (Figures
6G and 6J), which was consistent with the direct excitation ofthese neurons by inhibiting NMDARs. We also note that mostof the c-Fos-expressing cells in the layer 4-6 were glutamatergic
cells (Figures 6H–6J). Taken together, these results confirmed
the fourth prediction that impaired NMDAR functioning directly
increases the excitability of neurons, especially glutamatergic
pyramidal neurons that are modeled in this study.
Impairment of Plasma Membrane Ca2+ ATPase
Increases Sleep Duration
To further validate the AN model, we next tested the last prediction that downregulation of Ca2+ pump would increase the sleep
duration. The plasma membrane Ca2+ ATPases (PMCAs), a major member of Ca2+ pump, are transport proteins located in theplasma membrane of cells. PMCAs, whose function is poweredby hydrolysis of ATP, pump Ca2+ out of cells.
We created KOs of all of the four PMCAs (PMCA1 [Atp2b1],
PMCA2 [Atp2b2], PMCA3 [Atp2b3], and PMCA4 [Atp2b4]) by injecting three gRNAs for each gene (Figures S7A–S7D, S7J, S7K,
and S7N; Tables S2 and S3). We confirmed the lethal phenotype
seen in Atp2b1 biallelic KO mice generated by conventional
methods (Okunade et al., 2004) also occurred in KO mice generated by the triple-target CRISPR method. As predicted from theAN model, Atp2b3 KO mice exhibited a significant increase in
sleep duration with 777.8 ± 8.5 min (mean ± SEM, n = 12) in one
day, which was 53.6 min (1.07 SD, p < 0.05) longer than that of
WT mice (Figures 7A–7C). To confirm the significance of theobserved long-sleeper phenotype, we generated another batchof Atp2b3 KO mice by the same CRISPR/Cas9 probe set (set
1 in UT, Figure S7L) and confirmed the reproducibility of theobserved long-sleeper phenotype (p < 0.01, Figures 7D–7F). As
further validation, we generated another group of Atp2b3 KO
mice by independent CRISPR/Cas9 probe set (set 2, Figures S7I
and S7M) and confirmed the significant long-sleeper phenotypes(p < 0.01, Figures 7D–7F; Tables S2 and S3). These results strongly
suggested that the observed long-sleeper phenotype of Atp2b3
KO mice cannot be attributed to the possible off-target effect of
CRISPR, but rather to the common genomic defects in Atp2b3
gene. Taken together, these results confirmed the last predictionthat an impairment of Ca2+ pumps would increase sleep duration.
Impairment of Calcium/Calmodulin-Dependent Protein
Kinase Decreases Sleep Duration
In this study, our average-neuron model and its experimental
validation suggested that the molecular components in the
80 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

Ca2+-dependent hyperpolarization pathway can be potential
molecular targets of homeostatic process S. In other words,
our model supposed that the underlying molecular mechanism
for homeostatic process S should target on the Ca2+-dependent
hyperpolarization pathway. Such molecular mechanism is also
supposed to be able to sense the awake and/or slow-wavesleep states (e.g., through the concentration or temporal patterns of Ca2+) to allow the reversible transitions between SWS
and awake states. Although there are many intracellular molecules meeting these criteria for the underlying molecular mechanisms for homeostatic process S, we focused, as a first step, on
the major protein kinase in the brain, calcium/calmodulin-dependent protein kinase type II (CaMKII), which is known to be able tointeract with some of the Ca2+-dependent hyperpolarization
pathway (i.e., CaMKII is also a well-known protein family, which
can bind to NMDARs or L-type Ca2+ channels in response toCa2+ influx) (Leonard et al., 1999; Rose et al., 2009). CaMKII isformed by 12 catalytically active subunits consisted of four
different subunits (CaMKIIa [Camk2a], CaMKIIb [Camk2b],
CaMKIIg [Camk2g], and CaMKIId [Camk2d]) and their kinase activities are Ca2+ dependent. We thus created KOs of all of the four
CaMKIIs by injecting three gRNAs for each gene (Figures S7E–
S7H, S7P, S7Q, S7S, S7U, and S7V; Tables S2 and S3). Camk2a
KO and Camk2b KO mice exhibited a significant decrease in
sleep duration with 648.8 ± 29.5 min (mean ± SEM, n = 6) and
578.6 ± 14.7 min (mean ± SEM, n = 5) in 1 day, respectively,
which was 50.1 min (1.27 SD, p < 0.01) and 120.3 min (3.04
SD, p < 0.001) shorter than that of WT mice (Figures 7G–7I). As
further validation, we generated another group of Camk2a KO
mice and Camk2b KO mice by an independent CRISPR/Cas9
probe set (set 2, Figures S7O, S7R, and S7T; Tables S2 andS3) and confirmed the observed short-sleeper phenotypes (Figures 7J–7L). These results strongly suggested that the observed
short-sleeper phenotype of Camk2a KO and Camk2b KO micecannot be attributed to the possible off-target effect of CRISPR,
but rather to the common genomic defects in Camk2a andCamk2b gene, respectively. Taken together, these results further
support a hypothesis that Ca2+-dependent hyperpolarization
pathway underlies the regulation of sleep duration in mammals.
DISCUSSION
Ca2+-Dependent Hyperpolarization Pathway as a
Possible Molecular Target for Process S
In this study, we developed the AN model, which revealed that themolecular components of the Ca2+-dependent hyperpolarization
pathway are potential molecular targets of the homeostatic sleep
regulators (Figure 7M). This model suggests that assembled neurons themselves could have at least two default states, SWS andawake. It is consistent with the previous in vitro cortical modelthat showed transition from sleep-like state to wake-like state
by stimulation with mixture of neurotransmitters (Hinard et al.,2012). In particular, the Ca2+-dependent mechanism of SWS
firing patterns is consistent with the previous computational
studies in electric bursting of various types of cells (Arbib, 2003;
Izhikevich, 2007). How the molecules, such as KCa channels
Kcnn2 and Kcnn3, the Cav channels Cacna1g and Cacna1h,
and MK-801-targeted NMDAR subunits (possibly Nr1/Nr2b)

2

3

4

5

6

7

1

2

3 4 5 6Time (Day)Sleep duration (min)B
Atp2b2 (n = 3)

7

1

2

3

4

5

6

7

D

Atp2b3 (set 1, n = 9)0

1

Atp2b3 (set 1, n = 12)

Atp2b4 (n = 7)

30
0
6

12

18

24

0

6

12

18

24

0

6

12

18

24

Time (hr)

C

2

3

4

5

6

1

7

E

60

0

Atp2b3 (set 2, n = 9)

3

4

5

6

7

0

Atp2b3 (set 2, n = 9)

Atp2b3 (set 1, n = 9)
60

60

30

30

0

0
0

6

12

18

24

0

6

12

18

24

Time (hr)F

Atp2b2

2

60

Time (Day)Sleep duration (min)160

Sleep duration (min)Atp2b4 (n = 7)

Mouse

Atp2b3 (set 1, n = 12)

Mouse

Atp2b2 (n = 3)

Sleep duration (min)A

Atp2b3 (set 1)

Atp2b3 (set 1)
Atp2b3 (set 2)

Atp2b4
600

700

800

900

0.2

0.4

Sleep duration (min)

0.6

0.02

0.8

0.04

0.06

0.02

0.04

600

PSW

PWS

Amplitude

0.06

700

800

900

0.2

0.4

Sleep duration (min)

0.6

0.8

0.02

0.04

0.02

0.06

PWS

Amplitude

0.04

0.06

PSW

Sleep duration (min)

H

2

3

4

5

6

7

1

Camk2a(set1, n = 6)

2

3

4

5

6 7 1 2Time (Day)

3

4

5

6

7

1

2

3

4

5

6

7

Camk2a (set 2, n = 9)01

Camk2d (n = 5)

Camk2g (n = 5)

60
30
0
0

6

12

18

24 0

6

12

18

0

24

6

12

18

24 0

6

12

18

24

Time (hr)I2

K

Camk2b(set 1, n = 5)

Camk2b (set 2, n = 8)

3

4

5

6

7

1

2

3

4

5

6

7

60

0

Time (Day)Camk2a(set 2, n = 9)

Sleep duration (min)

1

60
Mouse

Mouse

Camk2g (n = 5)

Sleep duration (min)Camk2a (set1, n = 6) Camk2b (set 1, n = 5) Camk2d (n = 5)

Sleep duration (min)JG

Camk2b(set 2, n = 8)

60

60

30

30

0

0

6

12

18

0

24

0

6

12

18

24

Time (hr)LCamk2a (set 2)
Camk2b (set 2)
600

Camk2a (set 1)

700

800

900

0.2

Sleep duration (min)Camk2b (set 1)

0.4

0.6

0.8

Amplitude

Camk2dCamk2g
600

700

800

900

Sleep duration (min)

0.2

0.4

0.6

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0.04

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Figure 7. Impairment of Ca2+ Pumps Increases Sleep Duration and Impairment of CaMKIIs Decreases Sleep Duration(A) Sleep duration (per hour) over 6 days for KO mice of the PMCA family.
(B) Sleep duration (per hour) over 24 hr, averaged over 6 days in KO mice of the PMCA family. Red lines, the mean sleep duration at each time of day for eachstrain. Gray, WT (n = 108). Shaded area, SEM for each time point.
(C) Distributions of sleep/wake parameters of KO mice of the plasma membrane Ca2+ ATPase family. Histogram, WT (n = 108). Black dashed line and gray shade,the mean and 1 SD range from the recording of WT.
(D–F) Sleep-duration phenotype for another batch (set 1) and another group (set 2) of Atp2b3 KO mice. Sleep duration (per hour) over 6 days (D), sleep duration(per hour) over 24 hr, averaged over 6 days (E), and distributions of sleep/wake parameters (F) are shown.
(G) Sleep duration (per hour) over 6 days for KO mice of the CaMKII family by triple-target CRISPR.
(legend continued on next page)

Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 81

and the Ca2+ pumps Atp2b3 are targeted by the sleep homeostatic process S remains to be solved. One possibility is that process S may be represented as the amount of SISs, which thenregulate these molecules. In this context, it will be interesting to
examine the relationship of SISs (e.g., adenosine, NO, prostaglandin D, TNF, IL1, and GHRH) (Clinton et al., 2011; Krueger
et al., 2008; Obal and Krueger, 2003) to the molecules involvedin the Ca2+-dependent hyperpolarization pathway. Alternatively,
but not exclusively, process S may be represented directly as thequality or quantity of the molecules involved in the Ca2+-dependent hyperpolarization pathway. In that case, process S can be
identified as an activity-dependent effect (e.g., phosphorylation)
and/or a mechanism affecting the quantity (e.g., translation) of
these target molecules. Given the increasing evidence for the
post-translational modification of KCa channels, Cav channels,
NMDARs, and Ca2+ pumps, one of the plausible execution mechanisms of homeostatic process S might be activity-dependent
protein modifications (e.g., phosphorylation or dephosphorylation) of the Ca2+-dependent hyperpolarization pathway. The evidence that impairment of Camk2a and Camk2b, a well-known
bounder and modifier of Cav channels and NMDARs, decreased
sleep duration may support this mechanism. Interestingly, sinceprotein modifications are often involved in sleep-related neurological processes that occur over long timescales (e.g., hours
to days), such as learning, memory, and circadian clocks, a rolefor such activity-dependent protein modifications in the Ca2+dependent hyperpolarization pathway is compatible with theslow and macroscopic dynamics of sleep. Compared to the homeostatic (i.e., feedback) control expected for process S, circadian (i.e., feedforward) control of the quality and/or quantity of themolecules involved in the Ca2+-dependent hyperpolarization
pathway might be also important especially for process C of
sleep (Borbély, 1982). Recently, a dopamine synthesis pathway
was reported to be regulated by circadian clock in mammals(Chung et al., 2014). In this context, it will be interesting to
examine the relationship of awake-inducing substances (e.g.,
dopamine) to the molecules involved in the Ca2+-dependent hyperpolarization pathway. We also noted previous reports thatprocess C (circadian clock) is regulated by calcium-dependent
pathways such as rhyanodine receptors, calcium-dependent potassium channels, voltage-gated calcium channels, and calcium
binding proteins (Ding et al., 1998; Ikeda et al., 2003; Meredith
et al., 2006; Schmutz et al., 2014; Stadler et al., 2010). In either
case, gradual changes in the quality and/or quantity of suchtarget molecules in the Ca2+-dependent hyperpolarization
pathway, at least in theory, could eventually alter the fast/microscopic electrophysiological patterns of neurons, suggesting that
this mechanism is also compatible both with slow/macroscopic
homeostatic regulations and fast/microscopic electro-physi-

ology. Therefore, the Ca2+-dependent hyperpolarization hypothesis has the potential to connect the gaps between slow/
macroscopic homeostatic regulations and fast/microscopic
electrophysiological dynamics.
A Possible Role of Ca2+-Dependent Hyperpolarization
Pathway in Slow-Wave Sleep and Sleep Homeostasis
In this study, we performed mammalian reverse genetics withoutcrossing by combining the recently developed triple-target
CRISPR method and the non-invasive sleep/wake recording system SSS, which allowed us to comprehensively generate morethan 21 KO mice and efficiently analyze their sleep/wake phenotype. As a result, seven KO mice, including Kcnn2, Kcnn3,
Cacna1g, Cacna1h, Atp2b3, Camk2a, and Camk2b KO mice,
exhibited the significant change in sleep duration. These sleep/
wake phenotypes were confirmed by another group of corresponding KO mice by independent CRISPR/Cas9 probe set (set2). We also noted that pharmacological inhibition of NMDARs by
MK-801 reduced sleep duration. Although there is the possibility
that these gene knockouts might affect the phenotype of respiration and SSS system could miscalculate their sleep duration, theEEG/EMG recording of Kcnn2 KO mice and MK-801-treatedmice confirmed these mice exhibited the significant decrease of
NREM sleep (SWS) duration in addition to the significant decreaseof total sleep duration. These results supported the role of Ca2+dependent hyperpolarization pathway in the regulation of sleep
duration. In addition, these EEG/EMG results also suggest thatthe observed decrease of sleep duration was mainly due to thedecrease of NREM sleep (SWS) duration at least in Kcnn2 KO
mice and MK-801-treated mice. Further EEG/EMG recording of
the remaining six KO mice will clarify the relationship between
Ca2+-dependent hyperpolarization pathway and SWS.
Another interesting question is about the relationship between
Ca2+-dependent hyperpolarization pathway and sleep homeostasis. As the very first step, we conducted an SD experimentto Kcnn2 KO mice and WT mice, which revealed that WT mice exhibited significant change in NREM sleep (SWS) duration afterSD. On the other hand, Kcnn2 KO mice exhibited no significant
change in NREM sleep (SWS) duration, implying a possible roleof Kcnn2 in sleep homeostasis. Further experimental validations(e.g., the SD experiments of the remaining six KO mice and MK801-treated mice) will also clarify the relationship between Ca2+dependent hyperpolarization pathway and sleep homeostasis.
A Systems Biology Approach to Examine Sleep/Wake
CyclesAlthough the molecular identity of the sleep homeostatic process S is still unknown, our findings predict that process S
can be implemented as a neural-activity-dependent quality(H) Sleep duration (per hour) over 24 hr, averaged over 6 days in KO mice of the CaMKII family. Red lines, the mean sleep duration at each time of day for eachstrain. Gray, WT (n = 101). Shaded area, SEM for each time point.
(I) Distributions of sleep/wake parameters of KO mice of the CaMKII family. Histogram, WT (n = 101). Black dashed line and gray shade, the mean and 1 SD range
from the recording of WT.
(J–L) Sleep-duration phenotype for another group of Camk2a and Camk2b KO mice (set 2). Sleep duration (per hour) over 6 days (J) sleep duration (per hour) over
24 hr, averaged over 6 days (K), and distributions of sleep/wake parameters (L) are shown.
(M) A model diagram of Ca2+-dependent hyperpolarization pathway.
WT, C57BL/6N male mice. Error bars, SEM, *p < 0.05, **p < 0.01, ***p < 0.001, see also Supplemental Experimental Procedures for details.

82 Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc.

and/or quantity control of the Ca2+-dependent hyperpolarization
pathway. Therefore, the potential molecular targets for process S (e.g., Kcnn2, Kcnn3, Cacna1g, Cacna1h, Nr1/Nr2b, andAtp2b3) can be used to identify the missing switch (i.e., process S) between sleep/wake cycles. We also note that Camk2a
and Camk2b are one of the candidate molecules underlying homeostatic process S. In this study, we focused on the production
and phenotype-analysis of global knockout mice. The obvious
next step will be the cell-type-specific analysis of these regulators of sleep duration found in this study (Susaki and Ueda,
2016). This new research field, i.e., the system-level identification and analysis of the molecular networks underlying sleep/
wake cycles in mammals, will expand our detailed understanding of sleep regulation, sleep disorders, and its associated diseases such as psychiatric diseases in the years to come.
EXPERIMENTAL PROCEDURES
Details are also supplied in the Supplemental Experimental Procedures.
Simulations
The AN model was constructed by simplifying previously published HodgkinHuxley-type models of the electrophysiology of cortical neuron network into
the AN. The full system of equations is presented in Supplemental Experimental Procedures. The parameter values are shown in Table S1. Parametersearches for SWS firing patterns were conducted in the defined parameter
space including biophysically reasonable parameter values. In the bifurcation
analysis, each parameter value was gradually varied from the parameter setsthat give SWS firing patterns obtained from parameter searches. Transitions
from SWS firing patterns to awake firing patterns were then counted.
Animals and Sleep Phenotyping
All experimental procedures and housing conditions involving animals and theircare were approved by the Animal Care and the Use Committee. Sleep phenotyping was done non-invasively by SSS. Creation and sleep phenotyping ofseveral KO mice were done in University of Tokyo by using C57BL/6N mice(n = 101) as control information (Figures 3D–3F; Figures 7J–7M). All other experiments were done in RIKEN by using C57BL/6N mice (n = 108) published in ourprevious paper as control information (Sunagawa et al., 2016). The basal EEG/
EMG recording for Kcnn2 KO mice was performed in University of Tokyo. Sleep
deprivation experiment using gentle handling of Kcnn2 KO mice was conducted
after the basal EEG/EMG recording. CRISPR-KO animals were generated by
one-cell embryo microinjection of synthesized Cas9 mRNA and gRNAs (mix ofthree targeting sequences; template sequences are shown in Table S2) to
C57BL/6N fertilized eggs. KO genotype was confirmed by qPCR and/or
genomic sequencing. We also used Arc-dVenus Tg mice (line D) (Eguchi and Yamaguchi, 2009) for MK-801 administration experiments and CUBIC analysis.
Pharmacological Administration
C57BL/6N or C57BL/6J mice received an i.p. injection of synthesized PCPor MK-801 maleate in each concentration (Figures 4A–4D; Figures S5A–
S5D) at ZT2, respectively. For chronic administration of MK-801, the drug
was administered to C57BL/6J mice and Arc-dVenus mice as drinking waterin each concentration. For EEG/EMG recording and FISH, 2 mg/kg of
MK-801 was injected i.p. to C57BL/6N mice at ZT2.
CUBIC Analysis of Arc-dVenus Tg Animals
Fixed brains of Arc-dVenus mice were cleared and their whole brain images
were acquired and subjected to generate 3D NIfTI-1 files as previously reported (Susaki et al., 2014, 2015), with some modifications. The Arc-dVenus
signals were normalized and used for comparison. The hyper-activated cells
were detected using the Fiji software (Schindelin et al., 2012) or analyzed bycreating binary mask for each anatomical region and applied it to the reconstructed 3D images showing only hyper-activated cells. Querying specificregions relies on images aligned to the Allen Brain Atlas (Lein et al., 2007).
The intensity within each region can be also used to estimate the number ofhyper-activated cells. Clustering and hierarchical annotation of anatomical
regions were performed to the dataset (Figure 5; Figures 6A–6D).
Fluorescence In Situ Hybridization
The FISH protocol was based on TAI-FISH (Xiu et al., 2014) with some modifications. The RNA probes for hybridization (PCR primers for probe templates
are shown in Table S5) labeled with digoxigenin (DIG)-UTP or fluorescein (Flu)UTP and detected as cyanine 3 or Alexa Fluor 488 signals, respectively. Signals were amplified with Tyramide Signal Amplification system (PerkinElmer).
Images of these sections were acquired with an inverted confocal microscope(FV1200/IX83) and an upright fluorescent microscope (BX51, Olympus) andwere analyzed with ImageJ (Preibisch et al., 2009).
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
seven figures, six tables, and four movies and can be found with this article
online at http://dx.doi.org/10.1016/j.neuron.2016.02.032.
A video abstract is available at http://dx.doi.org/10.1016/j.neuron.2016.02.
032#mmc10.
AUTHOR CONTRIBUTIONS
H.R.U., F.T., G.A.S., and S.S. designed the study. F.T. constructed andanalyzed the averaged-neuron model. K.S., M.U.-T., R.O., and S.S. performed
the triple-target CRISPR. G.A.S., D.T., K.L.O., and S.S. performed the sleep
phenotype analysis. D.T. performed the sleep deprivation experiment. H.Y.
and E.A.S. performed CUBIC treatment of mice brains. K.M. synthesized
PCP. D.P. and H.Y. developed CUBIC informatics on spatio-temporal profilingof cellular activities of individual regions over the entire brain. E.A.S. and S.S.
performed in situ hybridization. M.U.-T. and H.F. performed the genotypingof animals. H.R.U., F.T., G.A.S., and S.S. wrote the manuscript. All authors
discussed the results and commented on the manuscript text.
ACKNOWLEDGMENTS
We thank S. Yamaguchi for Arc-dVenus mice, T. Mikami for SSS, and K. Miyamichi for sharing the modified TAI-FISH protocol and probes. We also thank all
the lab members at RIKEN QBiC, and University of Tokyo, in particular, J. Hara,
M. Shiokawa, S. Fujino, J. Yoshida-Garcon, A. Kishimoto, A. Nishiyama, Y.
Wada, and M. Nomura for their kind help in preparing the materials and supporting experiments, the LARGE, RIKEN CDB for housing the mice, K. Tainaka
for synthesizing PCP, A. Kuno for improving CUBIC protocol, S. Sugai for
helpful discussion. This work was supported by a grant from AMED-CREST(AMED/MEXT, H.R.U.), CREST (JST/MEXT, H.R.U.), Brain/MINDS (AMED/
MEXT, H.R.U.), Basic Science and Platform Technology Program for Innovative Biological Medicine (AMED/MEXT, H.R.U.), a Grant-in-Aid for Scientific
Research (S) (JSPS KAKENHI Grant Number 25221004, H.R.U.), a Grant-inAid for Scientific Research on Innovative Areas (JSPS KAKENHI Grant Number
23115006, H.R.U.), a Grant-in-Aid for Young Scientists (B) (JSPS KAKENHI
Grant Number 26870858, G.A.S.), a Grant-in-Aid for Scientific Research on
Innovative Areas (JSPS KAKENHI Grant Number 26113720, K.S.), a Grantin-Aid for Young Scientists (A) (JSPS KAKENHI Grant Number 15H05650,
E.A.S.), Brain Sciences Project of the Center for Novel Science Initiatives of
National Institutes of Natural Sciences (Grant Number BS261004 andBS271005, E.A.S.), The Tokyo Society of Medical Sciences (E.A.S.), JapanFoundation for Applied Enzymology (E.A.S.), the RIKEN Foreign Postdoctoral
Researcher Program (D.P.) and by the Medical Scientist Training Program by
the University of Tokyo (F.T.).
Received: June 1, 2015
Revised: December 3, 2015
Accepted: February 16, 2016
Published: March 17, 2016

Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 83

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Neuron 90, 70–85, April 6, 2016 ª2016 Elsevier Inc. 85

Neuron, Volume 90

Supplemental Information

Involvement of Ca2+-Dependent Hyperpolarization
in Sleep Duration in Mammals
Fumiya Tatsuki, Genshiro A. Sunagawa, Shoi Shi, Etsuo A. Susaki, Hiroko
Yukinaga, Dimitri Perrin, Kenta Sumiyama, Maki Ukai-Tadenuma, Hiroshi
Fujishima,
Rei-ichiro
Ohno,
Daisuke
Tone,
Koji
L.
Ode,
KatsuhikoMatsumoto, and Hiroki R. Ueda

1Supplemental Experimental Procedures

2

Averaged-neuron model3

To recapitulate the electrophysiological characteristics of the slow-wave-sleep and4awake states, a computational model was constructed as follows:𝐶𝐴𝑑𝑉= −𝐴(𝐼𝐿 + 𝐼𝑁𝑎 + 𝐼𝐾 + 𝐼𝐴 + 𝐼𝐾𝑆 + 𝐼𝐶𝑎 + 𝐼𝐾𝐶𝑎 + 𝐼𝑁𝑎𝑃 + 𝐼𝐴𝑅 )𝑑𝑡− 𝐼𝑁𝑀𝐷𝐴 − 𝐼𝐴𝑀𝑃𝐴 − 𝐼𝐺𝐴𝐵𝐴
5, where C is the membrane capacitance, A is the area of a single neuron, V is the6membrane potential, and I denotes the current of the indicated ion channels. Please7

note that both sides except extrinsic (synaptic) ion currents should be multiplied by 10

8

to adjust their units to nA if the numerical values listed in Table S1 are directly used in9

the numerical simulation. The intrinsic (non-synaptic) ion channels in the soma were

10modeled based on previous studies (Bazhenov et al., 2002; Chen et al., 2012;11

Compte et al., 2003; Hill and Tononi, 2005; Sanchez-Vives et al., 2010; Timofeev

12

et al., 2000) using Hodgkin-Huxley-type equations, with a gating variable x governed

13by the first-order kinetics equation:
𝜙 [𝑥∞ (𝑉 ) − 𝑥]𝑑𝑥
= 𝜙[𝛼𝑥 (𝑉 )(1 − 𝑥 ) − 𝛽𝑥 (𝑉 )𝑥] =𝑑𝑡𝜏𝑥 ( 𝑉 )14, where ϕ is the temperature factor (ϕ = 1). Each intrinsic current is given by the15

following equations (1) to (9):𝐼𝐿 = 𝑔𝐿 (𝑉 − 𝑉𝐿 )

1

(1)

3
𝐼𝑁𝑎 = 𝑔𝑁𝑎 𝑚𝑁𝑎ℎ (𝑉 − 𝑉𝑁𝑎 )
∞ 𝑁𝑎

(2)

𝑚𝑁𝑎∞ = 𝛼𝑚 /(𝛼𝑚 + 𝛽𝑚 )𝛼𝑚 = 0.1(𝑉 + 33)/[1 − 𝑒𝑥𝑝⁡(−(𝑉 + 33)/10)]𝛽𝑚 = 4𝑒𝑥𝑝⁡(−(𝑉 + 53.7)/12)𝑑ℎ𝑛𝑎= 4(𝛼ℎ (1 − ℎ𝑁𝑎 ) − 𝛽ℎ ℎ𝑁𝑎 )𝑑𝑡𝛼ℎ = 0.07exp⁡(−(𝑉 + 50)/10)𝛽ℎ = 1/[1 + exp⁡(−(𝑉 + 20)/10)]
𝐼𝐾 = 𝑔𝐾 𝑛𝐾4 (𝑉 − 𝑉𝐾 )
𝑑𝑛𝑘
𝑑𝑡

(3)

= 4(𝛼𝑛 (1 − 𝑛𝐾 ) − 𝛽𝑛 𝑛𝐾 )𝛼𝑛 = 0.01(𝑉 + 34)/[1 − exp(−(𝑉 + 34)/10)]
𝛽𝑛 = 0.125exp⁡(−(𝑉 + 44)/25)
𝐼𝐴 = 𝑔𝐴 𝑚𝐴3∞ ℎ𝐴 (𝑉 − 𝑉𝐾 )(4)

𝑚𝐴∞ = 1/[1 + exp⁡(−(𝑉 + 50)/20]𝑑ℎ𝐴
𝑑𝑡= (ℎ𝐴∞ − ℎ𝐴 ) /𝜏ℎ𝐴

ℎ𝐴∞ = 1/[1 + exp⁡((𝑉 + 80)/6)]
𝐼𝐾𝑆 = 𝑔𝐾𝑆 𝑚𝐾𝑆 (𝑉 − 𝑉𝐾 )

2

(5)

𝑑𝑚𝐾𝑆
= (𝑚𝐾𝑆∞ − 𝑚𝐾𝑆 )/𝜏𝑚𝐾𝑆
𝑑𝑡
𝑚𝐾𝑆∞ = 1/[1 + exp⁡(−(𝑉 + 34)/6.5)]
𝜏𝑚𝐾𝑆 = 8/[exp(−(𝑉 + 55)/30) + exp⁡((𝑉 + 55)/30)]
2
𝐼𝐶𝑎 = 𝑔𝐶𝑎 𝑚𝐶𝑎(𝑉 − 𝑉𝐶𝑎 )∞(6)

𝑚𝐶𝑎∞ = 1/[1 + 𝑒𝑥𝑝(−(𝑉 + 20)/9)]
𝐼𝐾𝐶𝑎 = 𝑔𝐾𝐶𝑎 𝑚𝐾𝐶𝑎∞ (𝑉 − 𝑉𝐾 )(7)

𝑚𝐾𝐶𝑎∞ = 1/[1 + (𝐾𝐷 /[𝐶𝑎2+ ])3.5 ]3𝐼𝑁𝑎𝑃 = 𝑔𝑁𝑎𝑃 𝑚𝑁𝑎𝑃
(𝑉 − 𝑉𝑁𝑎 )∞(8)

𝑚𝑁𝑎𝑃∞ = 1/[1 + exp⁡(−(𝑉 + 55.7)/7.7)]
𝐼𝐴𝑅 = 𝑔𝐴𝑅 ℎ𝐴𝑅∞ (𝑉 − 𝑉𝐾 )(9)ℎ𝐴𝑅∞ = 1/[1 + exp⁡((𝑉 + 75)/4)]
1

The extrinsic (synaptic) ion currents in the dendrite were also modeled based2

on previous studies (Bazhenov et al., 2002; Chen et al., 2012; Compte et al., 2003;

3

Hill and Tononi, 2005; Sanchez-Vives et al., 2010; Timofeev et al., 2000) using

4

Hodgkin-Huxley-type equations, with a gating variable s governed by the first-order5kinetics equation for AMPA and GABA receptors:𝑑𝑠𝑠= 𝛼⁡𝑓(𝑉 ) −
𝑑𝑡
𝜏

3

1

, and with gating variables s and x governed by the second-order kinetics equations

2

for NMDA receptors:𝑑𝑠𝑠= 𝛼𝑠 ⁡𝑥(1 − 𝑠) −
𝑑𝑡
𝜏𝑠
𝑑𝑥
𝑥
= 𝛼𝑥 𝑓 ( 𝑉 ) −
𝑑𝑡
𝜏𝑥

3

, where τ is the time constant for turnover of s and x, V is the membrane potential, and4f is a saturating function of V:𝑓(𝑉 ) = 1/[1 + exp⁡(−(𝑉 − 20)/2)]

5

Each extrinsic current is then given by the following equations (10) to (12):𝐼𝐴𝑀𝑃𝐴 = 𝑔𝐴𝑀𝑃𝐴 𝑠𝐴𝑀𝑃𝐴 (𝑉 − 𝑉𝐴𝑀𝑃𝐴 )(10)

𝑑𝑠𝐴𝑀𝑃𝐴
𝑠𝐴𝑀𝑃𝐴= 3.48𝑓(𝑉 ) −𝑑𝑡
𝜏𝐴𝑀𝑃𝐴
𝐼𝑁𝑀𝐷𝐴 = 𝑔𝑁𝑀𝐷𝐴 𝑠𝑁𝑀𝐷𝐴 (𝑉 − 𝑉𝑁𝑀𝐷𝐴 )(11)

𝑑𝑠𝑁𝑀𝐷𝐴𝑠𝑁𝑀𝐷𝐴= 0.5𝑥𝑁𝑀𝐷𝐴 (1 − 𝑠𝑁𝑀𝐷𝐴 ) −𝑑𝑡
𝜏𝑠𝑁𝑀𝐷𝐴
𝑑𝑥𝑁𝑀𝐷𝐴
𝑥𝑁𝑀𝐷𝐴= 3.48𝑓(𝑉 ) −𝑑𝑡
𝜏𝑥𝑁𝑀𝐷𝐴𝐼𝐺𝐴𝐵𝐴 = 𝑔𝐺𝐴𝐵𝐴 𝑠𝐺𝐴𝐵𝐴 (𝑉 − 𝑉𝐺𝐴𝐵𝐴 )(12)

𝑑𝑠𝐺𝐴𝐵𝐴𝑠𝐺𝐴𝐵𝐴= 𝑓 (𝑉 ) −
𝑑𝑡
𝜏𝐺𝐴𝐵𝐴
6

The Ca2+ concentration was also modeled by the following equation (13):4[𝐶𝑎2+ ]𝑑[𝐶𝑎2+ ]= −𝛼𝐶𝑎 (𝐴𝐼𝐶𝑎 + 𝐼𝑁𝑀𝐷𝐴 ) −
𝑑𝑡
𝜏𝐶𝑎
1

(13)

Please note that intrinsic (non-synaptic) ion currents (i.e. ICa) should be2

multiplied by 10 to adjust its unit to nA if the numerical values listed in Table S1 are

3

directly used in the numerical simulation.

4

The parameters values were based on previous studies (Bazhenov et al.,5

2002; Chen et al., 2012; Compte et al., 2003; Hill and Tononi, 2005;

6

Sanchez-Vives et al., 2010; Timofeev et al., 2000) with some modifications, and7listed in Table S1. The model was simulated using Mathematica (version 9.0,

8Wolfram Research, Inc.), with the NDSolve function, in which MaxSteps and9PrecisionGoal were chosen to be infinite and 15, respectively. Integration was

10

performed with the following initial values in each simulation, parameter search, and11bifurcation analysis: V = -45 mV, hNa = 0.045 (unitless), nK = 0.54 (unitless), hA =120.045 (unitless), mKS = 0.34 (unitless), [Ca2+] = 1 μM, sAMPA = 0.01 (unitless), sNMDA =13

0.01 (unitless), xNMDA = 0.01 (unitless), sGABA = 0.01 (unitless).

14
15

Parameter search16

We computationally searched for the parameter sets that yield slow-wave-sleep firing

17

patterns by numerically calculating the model’s behavior using parameter sets

18

randomly chosen from a large parameter space. The parameter space was defined to19

include biophysically reasonable parameter values: the conductance of intrinsic

20(non-synaptic) channels in the soma (gL, gNa, gK, gA, gKS, gCa, gKCa, gNaP, gAR) and21

extrinsic (synaptic) channels in the dendrite (gAMPA, gNMDA, gGABA) were generated by

22

selecting parameters from an exponential distribution bounded to the interval

230.01-100 mS/cm2 and 0.002-20 μS, respectively. The intervals used in the parameter5

1

searches for the conductance of the intrinsic and extrinsic channels were set to be the2same range after the consideration of the area of a neuron, A = 0.02 mm2 (i.e.

30.002-20 μS/ 0.02 mm2 = 0.01-100 mS/cm2). We also conducted a random parameter4

search for τCa (10-1000 ms). These parameters were chosen because they could be5perturbed by genetic/pharmacological manipulations, and we can predict the outcome

6of such perturbation based on our parameter search.

7

We analyzed the parameters that yielded a steady-state or periodic solution

8

with real values under the defined computer memory usage and calculation time. We

9note that most cases exhibited steady-state or periodic behavior. The major

10

frequency of the oscillatory behavior was then analyzed by fast Fourier transform. We

11also evaluated the fine structure of the oscillatory behavior by counting the number of

12

spikes in 1 second; the number of spikes was determined as half the number of times

13

the membrane potential crossed -20 mV. Solutions, in which the membrane potential

14

exceeded this threshold at almost all time points (> 95%), were eliminated at this15

point. Based on these characteristics, the solutions were classified into four16

categories: “Resting” (spike numbers per second < 2 or peak frequency = 0 Hz),17

“Sleep” (0 Hz < peak frequency < 10 Hz and spike number per second > 5 × peak18

frequency), “Wake” (peak frequency ≥ 10 Hz), and “Slow-wave with few spikes” (0 Hz

19< peak frequency <10 Hz and spike number per second < 5 × peak frequency).

20“Slow-wave with few spikes” indicated that the solution represented slow-wave

21

activity, with fewer than five spikes during one bursting phase of neural activity. All of22

the solutions classified as “Sleep” were then checked manually to select the ones that23

exhibit oscillatory membrane potential alternating between bursting and silent

24phases.

25

6

1

Bifurcation analysis

2

To analyze the behavior of the system around the parameter set that gave3

slow-wave-sleep firing patterns, we conducted two types (i.e. comprehensive and4

simplified versions) of bifurcation analysis. In the comprehensive bifurcation analysis

5(Figures 1C and 1F), each conductance or the time constant was gradually changed

6

one by one from 0.001 to 10 times its original value, and the solutions were7

automatically classified into four categories according to the criteria described above.

8

When the channel conductance of excitatory synaptic currents (gAMPA, gNMDA)

9decreased, the solution sometimes showed a cyclic firing pattern (peak frequency >1010 Hz) but weak synaptic currents. In these cases, we classified the behavior as

11“Wake” if either of the two excitatory synaptic currents (INMDA, IAMPA) was > 50% of that12of the original parameter set. Otherwise, the solutions were classified as “Cyclic firing

13with weak synaptic currents” (Figure 1F).

14

In the simplified bifurcation analysis (Figures 1G-1I; Figure S1K), each15

conductance or time constant was changed to have 0.001 times its original value. The

16

solution was classified into five categories as described above. When the parameters

17

failed to reach a steady-state or periodic solution under the defined computer memory

18

usage and calculation time, and led to complex solutions, we gradually decreased

19each conductance or the time constant from its original value to the one we calculated

20

under the defined memory usage and time. The number of solutions that resulted in21“Wake” between 0.001 and 1 times of its original value was counted for each22

bifurcation parameter over the entire parameter sets.

23

7

1

Bifurcation analysis under KO condition2

We first conducted parameter searches and subsequent bifurcation analysis under3

KO conditions by setting the target channel’s conductance to zero. The rest of the

4channel conductance and/or the time constant were generated from the same5

exponential parameter space as the wild-type parameter search.

6
7

Animals and sleep phenotyping

8

All experimental procedures and housing conditions involving animals and their care9were approved by the Animal Care and the Use Committee of Graduate School of

10

Medicine, the University of Tokyo or by the Animal Care and Use Committee of the11

RIKEN Kobe Institute, and all of the animals were cared for and treated humanely in

12accordance with the Institutional Guidelines for Experiments using animals. All mice13

were given food and water ad libitum, and kept at an ambient temperature and14

humidity. The light was controlled under 12-hr light/12-hr dark cycle. Creation and15

sleep phenotyping of Cacna1g (set 2), Cacna1h (set 2), Atp2b3 (set 1 in UT), Atp2b3

16(set 2), Camk2a, Camk2a (set 2), Camk2b (set 1), Camk2b (set 2), Camk2g, and17

Camk2d were done in University of Tokyo by using C57BL/6N mice (n = 101) as

18

control information (Figures 3D-3F; Figures 7J-7M). All other experiments were19

done in RIKEN by using the sleep phenotype of C57BL/6N mice (n = 108) published20

in our previous paper as control information (Sunagawa et al., 2016).

21
22

8

1

Design of target sequences for guide RNA (gRNA)2

The target sequences for Kcnma1, Kcnn1, Kcnn2, Kcnn3, Kcnn4, Kcnt1, Kcnt2,

3

Kcnu1 (Figures S2A-S2H), Cacna1a, Cacna1b, Cacna1c, Cacna1d, Cacna1e,

4

Cacna1f, Cacna1g (set 1 and 2), Cacna1h (set 1 and 2), Cacna1i, and Cacna1s5(Figures S4A-S4L) were designed using the on-line CRISPR guide RNA Design tool6

(Ma et al., 2013) (http://cas9.cbi.pku.edu.cn/index.jsp) or the mm10 CRISPR/Cas9

7

database (Sunagawa et al., 2016) (http://www.crispr.riken.jp/). Possible off-target

8

sequences within the mouse genome for each target sequence were checked using

9

the CRISPR Design Tool (Ran et al., 2013) (http://tools.genome-engineering.org).

10

The alternative target sequences (set 2) for Kcnn2, and Kcnn3 (Figures S2J

11and S2K), and the target sequences for Atp2b1, Atp2b2, Atp2b3 (set 1 and 2),

12

Atp2b4, Camk2a (set 1 and 2), Camk2b (set 1 and 2), Camk2g, and Camk2d

13(Figures S7A-S7I, and S7O) were selected from the list resulting from the mm1014

CRISPR/Cas9 database (Sunagawa et al., 2016) (http://www.crispr.riken.jp/).

15
16gRNA synthesis

17

Oligonucleotides (listed below, Hokkaido System Science) containing the target

18

sequences for Kcnma1 (Figure S2A) were annealed and inserted into the BbsI sites

19

downstream of the U6 promoter on the pX330 plasmid (Cong et al., 2013; Ran et al.,202013) (Addgene, #42230). The resulting vectors were designated pX330-Kcnma1-1,

21

pX330-Kcnma1-2, and pX330-Kcnma1-3. The gRNA templates for Kcnma1 were22

simultaneously fused to the T7 promoter and amplified from the pX330-Kcnma1-1,

23

pX330-Kcnma1-2, and pX330-Kcnma1-3 vectors by PCR using the primers listed24

below (Hokkaido System Science) (Wang et al., 2013).
9

1

Alternatively, the gRNA templates for Kcnn1, Kcnn2, Kcnn3, Kcnn4, Kcnt1,

2

Kcnt2, Kcnu1 (Figures S2B-S2H, S2J, and S2K), Cacna1a, Cacna1b, Cacna1c,

3Cacna1d, Cacna1e, Cacna1f, Cacna1g, Cacna1h, Cacna1i, Cacna1s (Figures

4S4A-S4L), Atp2b1, Atp2b2, Atp2b3, Atp2b4, CaMK2a, Camk2b, Camk2g, and5

Camk2d (Figures S4A-S4L, S7A-S7I and S7O) were directly synthesized and fused

6

to the T7 promoter by PCR. First, partial fragments of the gRNA templates including

7

each target sequence were amplified from the pX330 plasmids (Addgene, #42230) by

8

PCR with the Common Reverse primer (5'-AAAAGCACCGACTCGGTGCC-3',

9

Hokkaido System Science) (Wang et al., 2013) and Forward primer-1 for each target

10

sequence (Table S2, Hokkaido System Science). Subsequently, the T7

11

promoter-fused gRNA templates were amplified from the diluted PCR products by

12

PCR with the Common Reverse primer and Forward primer-2 for each target

13

sequence (Table S2, Hokkaido System Science).

14

The T7 promoter-fused gRNA PCR fragments were used as the template for15

in vitro transcription using the MEGAshortscript T7 kit (Life Technologies). The

16

gRNAs were puriﬁed using the MEGAclear kit (Life Technologies).

17
18

Oligonucleotide sequences for the Kcnma1 target sequences

19

Kcnma1-1:

20

Forward oligonucleotide: 5'-CACCGATGGATGCGCTCATCATAC-3'

21

Reverse oligonucleotide: 5'-AAACGTATGATGAGCGCATCCATC-3'

22

Kcnma1-2:

101Forward oligonucleotide: 5'-CACCGATAAACAATGGCTCCAGCC-3'

2

Reverse oligonucleotide: 5'-AAACGGCTGGAGCCATTGTTTATC-3'

3

Kcnma1-3:

4

Forward oligonucleotide: 5'-CACCGTCTATGAAGTATATTACGA-3'

5

Reverse oligonucleotide: 5'-AAACTCGTAATATACTTCATAGAC-3'

6
7

Oligonucleotide sequences for T7-gRNAs of Kcnma18Common Reverse oligonucleotide for T7-gRNAs:

9
10
11
12
13
14
15

5'-AAAAGCACCGACTCGGTGCC-3' (Wang et al., 2013)
Kcnma1-1 Forward oligonucleotide:
5'- GGGCCTAATACGACTCACTATAGGATGGATGCGCTCATCATAC-3'
Kcnma1-2 Forward oligonucleotide:
5'- GGGCCTAATACGACTCACTATAGGATAAACAATGGCTCCAGCC-3'
Kcnma1-3 Forward oligonucleotide:
5'- GGGCCTAATACGACTCACTATAGGTCTATGAAGTATATTACGA-3'

16
17

Cas9 mRNA synthesis18

p3s-Cas9HC (Cho et al., 2013) (Addgene, #43945), which includes a T7

19

promoter-fused Cas9 coding region, was digested with XbaI (TaKaRa), and used as11

1

the template for in vitro transcription using the mMESSAGE mMACHINE T7 kit (Life2Technologies). The Cas9 mRNA was puriﬁed using the MEGAclear kit (Life3Technologies).

45

One-cell embryo microinjection

6

C57BL/6N females (4-6 weeks old, CLEA Japan Inc) were superovulated and mated7with C57BL/6N males (CLEA Japan Inc). Fertilized eggs were collected from the8

ampulla of the oviduct of plugged C57BL/6N females by micro-dissection, and kept in

9

KSOM medium (Merck Millipore or ARK Resource) in a 5% CO2 incubator at 37°C.

10

Cas9 mRNA (100 ng/µl) and gRNAs (150 ng/µl in total) were co-injected into the11cytoplasm of fertilized eggs in M2 medium (Merck Millipore or ARK Resource) at

12room temperature. Details of the cytoplasmic injection were reported previously

13(Sumiyama et al., 2010). After microinjection, the embryos were cultured for 1 hr in

14

KSOM medium (Merck Millipore or ARK Resource) in a 5% CO2 incubator at 37°C,

15

and 15-30 embryos were then transferred to the oviducts of pseudopregnant female

16

ICR mice.

17
18

Genotyping of knockout mice by quantitative PCR (qPCR) and sequencing

19

The genomic DNA of wild type and KO mice was prepared from their tail using the20

DNeasy Blood & Tissue Kit (QIAGEN), according to the manufacturer's instructions.

21

qPCR for genotyping was performed using the ABI PRISM 7900/QuantStudio 7 Flex

22

(Applied Biosystems) and the SYBR Premix Ex Taq GC (TaKaRa). Primers for qPCR

23

(Table S3, Hokkaido System Science) were annealed to the targeting sequences.

24

The absolute target site abundance was calculated using a standard curve obtained12

1from wild-type genomic DNA. The amount of Tbp (Tsujino et al., 2013) was2

quantified and used as an internal control. When we could not confirm KO genotype

3

by qPCR, we performed sequencing or 2nd qPCR using the alternative primer which4

was independent of 1st qPCR. We performed sequencing to the mice in which we

5

could not confirm KO genotype by the 2 nd qPCR.

6Genotyping by sequencing was performed for mice which the relative amount7

of intact DNA in qPCR was higher than 0.2 in all of three target sites. The 0.7 ~ 1.8 kb

8around each target sequence was amplified by PCR with Ex Taq (TaKaRa) and the9primers (Table S4, Hokkaido System Science). The fragment was subcloned into10

pGEM-T-easy vector (Promega), and sequenced.

11
12

Pharmacological administration with SSS recording

13

C57BL/6J or C57BL/6N mice received an i.p. injection of PCP hydrochloride

14

synthesized according to the previous report (Prashad et al., 2005) (dissolved with

15

normal saline to be 13.3 and 26.7 mg/kg at 200 µl for Figures 4A and 4B; Figures

16

S5A and S5B) or MK-801 maleate (Tocris Bioscience, Catlog# 0924, Lot# 9A/151915,

17

diluted with natural saline to be 0.2, 2 and 20 mg/kg at 200 µl for Figures 4C and 4D;

18

Figures S5C and S5D) at ZT2, respectively. For chronic treatment of MK-801, the19drug (Wako, 136-17383, Lot# WEL3580) was diluted to sterile water to be 16, 32, 64

20

and 96 mg/l and treated to C57BL/6J mice as drinking water (Figures S5E-S5G). For

21

the CUBIC analysis, 64 mg/l of MK-801 was given to the treated group as drinking

22water (Figure 5; Figures 6A-6D; Figures S6A-S6E).

23

13

1

Pharmacological administration with EEG/EMG recording

2

C57BL/6N mice implanted with telemetry EEG/EMG recording device received an i.p.

3

injection of MK-801 maleate (Tocris Bioscience, Catlog# 0924, Lot# 1A/15826,

4

diluted with natural saline to be 2 mg/kg at 200 µl for Figures 4E and 4F) or natural

5

saline at ZT2, respectively. The recorded EEG/EMG data were analysed by the6

FASTER method. Further information on implantation of telemetry devices and data

7

analysis of EEG/EMG recordings has been published previously (Sunagawa et al.,82013).

9
10

Sleep deprivation

11

C57BL/6N mice and KO mice implanted with telemetry EEG/EMG recording device

12

were sleep deprived by gentle handling for eight hours. The deprivation started on

13

ZT4. Subsequently, mice were left to sleep freely for 2 days. The recorded EEG/EMG

14data were analysed by FASTER.

15
16

Brain sampling from Arc-dVenus mice with MK-801 administration

17

The Arc-dVenus transgenic mice (line D) (Eguchi and Yamaguchi, 2009) were18

housed in the SSS chamber under 12-hr light/12-hr dark cycle (LD conditions). On

19

day 0, MK-801 (Wako Pure Chemical Industries Ltd., 136-17383, Lot# WEL3580)

20

diluted to sterile water to be 64 mg/l was given to the treated group as drinking water

21

and sterile water to the control group. On day 5, the light condition was changed to22

constant darkness, and the animals with or without MK-801 administration were23

sacrificed at circadian time (CT) 4, 10, 16 and 22. We prepared samples from multiple
14

1

animals within ~30 min. The mice were deeply anesthetized with an overdose of2

pentobarbital (>100 mg/kg, i.p.; Somnopentyl, Kyoritsu Seiyaku), then transcardially3

perfused with PBS containing ~10 U/ml heparin (Wako Pure Chemical Industries Ltd.)4

to flush the blood vessels, followed by perfusion with 4% (w/v) paraformaldehyde

5(PFA) in PBS (pH 7.4) for fixation. The dissected brains were then post-fixed with 4%

6

PFA at 4°C for 24 hr, washed with PBS, and immersed in 20% (w/v) sucrose/PBS for

7

at least one day. After the sucrose/PBS replacement, the samples were stored in

8

O.C.T. compound at -80°C until use.

910

CUBIC protocol

11

The two CUBIC reagents (ScaleCUBIC-1 and -2) were prepared as previously

12

reported (Susaki et al., 2014; Susaki et al., 2015). The whole-brain clearing protocol

13

was slightly modified from the previous report. Each fixed brain was immersed in 1:1

14

diluted reagent-1:water with 2.5 μM nucleic acid stain TOPRO3 (Life Technologies,

15

T3605) at 37°C with gentle shaking for 3 hr, after which the solution was removed and16

the sample immersed in 7 ml of fresh reagent-1 with 2.5 μM TOPRO3. The reagent17

was replaced by fresh reagent-1 with 2.5 μM TOPRO3 on days 1 and 3. On day 6, the18treated brain was washed with PBS several times at room temperature with gentle

19

shaking overnight, and then immersed in 4 ml of 1.5 M NaCl/PBS with 2.5 μM

20

TOPRO3 for 2 days for additional staining. After staining, the sample was washed21

with PBS, immersed in 20% sucrose/PBS for at least 2 hr at 4°C, and stored in O.C.T.

22

compound at -80°C until use. Two days before imaging, the sample was thawed,

23

washed with PBS, immersed in 4 ml of 1:1 diluted reagent-2:PBS, and immersed in 4

24

ml of reagent-2 for 2 days. After imaging, the sample was again washed with PBS,

25

immersed in 20% sucrose/PBS, and stored in O.C.T. compound at -80°C.
15

1
2

Microscopy

3

Whole-brain fluorescence images were acquired with an LSFM (Ultramicroscope,4

LaVision BioTec) as reported previously (Dodt et al., 2007; Susaki et al., 2014;

5

Susaki et al., 2015). Venus signals were imaged using a 488 nm laser light6(Coherent Sapphire488LP-100) and emission filters 525/50 (Semrock). TOPRO3

7

signals were imaged using 588 nm laser light (Coherent Sapphire588LP-50) and8

emission filters 690/50 (Chroma), with a sCMOS camera (Andor Neo 5.5) and a9macrozoom microscope (Olympus MVX-ZB10) with a 0.63 × objective lens (Olympus

10

MVPLAPO0.63X). Each plane was illuminated from both the right and left sides of the11

sample, and the merged image was saved. Three-dimensional images were collected12

as a Z stack series: 10-μm steps x 668-847 planes for the dorsal-ventral direction and

1310-μm steps x 629-858 planes for the ventral-dorsal direction, with 800-ms

14

illuminations for Venus and 50-ms illuminations for TOPRO3.

15

Images of in situ hybridization samples were acquired as 8-bit TIFF images with

16

FV1200/IX83 inverted confocal microscopy (Olympus) with a 10 × (Olympus

17

UPLSAPO 10×/0.4) objective lens, 405/473/559 nm lasers and PMT detectors. 4.6818μm × 6 plane images were collected and stacked. We also used an upright19

fluorescent microscopy (BX51, Olympus) with a 4 × (Olympus UPlanApo 4×/0.16)

20

and 10 × (Olympus UPlanFL N 10×/0.3) objective lens, a CCD (Olympus DP30BW) or21

a cMOS (Andor Zyla) camera, and appropriate excitation/emission filter sets for DAPI,

22

Alexa-Fluor 488 and Cy3. A series of images covering all areas of a section were23

acquired and stitched with pairwise stitching plugin of ImageJ (Preibisch et al.,242009).

25
16

1

Image analysis

2

For each brain sample of CUBIC analysis, 3D NIfTI-1 files were generated from TIFF

3

stacks acquired in opposite directions, merged and registered as described4

previously (Susaki et al., 2014).

5

To normalize the Arc-dVenus signals, we extracted the pixels located only in6brain regions, by aligning the NIfTI-1 images with the Allen Brain Atlas (Lein et al.,72007). We then calculated the median intensity of these pixels for each brain, and8

normalized the data so that all of the brain samples had the same median intensity.

9

Overall brain images (Figure S6A) were obtained by applying the10normalization factors to the raw TIFF stacks and reconstructing them using the Imaris

11

software (Bitplane).

12

MK-801(+) and MK-801(-) were compared using the registered and13normalized NIfTI-1 files. For each time point, we first calculated the average

14

Arc-dVenus signal for both groups, using the “fslmerge –t” and “fslmaths –Tmean”

15

tools in the FSL software (Jenkinson et al., 2012). We then subtracted the MK-801(-)

16

average brain from the MK-801(+) brain at each time point using “fslmaths –sub.”17

The hyper-activated cells were analyzed using the Fiji software (Schindelin18

et al., 2012). For each horizontal slice of a given brain sample, we converted the19

image to 8-bit and successively ran the “Find Edges,” “setThreshold” (with20parameters 128, 255), “Threshold,” and “Analyze Particles” (with size = 10-500,

21

circularity = 0.3-1.0) commands. The number of hyper-activated cells in one brain

22

sample was the sum of the number of cells identified in each of its slices (Figure

23

S6C). When processing each slice, we also saved an image containing only the24

detected hyper-activated cells, so that we can reconstruct 3D images of cell17

1

hyper-activation throughout the brain. This detection method is shown for two2

MK-801(-) and MK-801(+) slices in Figure 5A, right.

3

We then analyzed region-specific pattern in cell hyper-activation. Querying

4

specific regions relies on images aligned to the Allen Brain Atlas. These images do

5

not have single-cell resolution, and the Fiji-based method cannot be applied. Instead,6

we created a binary mask for each region, and applied it to the reconstructed 3D

7images showing only hyper-activated cells. The intensity within each region can be8

used to estimate the number of hyper-activated cells in this region. For the whole9

brain, the relation between the total intensity i and the number of cells n is given by n

10

= 0.0123 × i, R2 = 0.94688 (Figure S6D). For each region, using the average intensity

11

iavg and the voxel dimension (25.8×25.8×40μm), we can therefore calculate nvol, the12number of cells per mm3 as, nvol = 461.961 × iavg (Figure 5A and 5C-5E, right13

panels; Figure 6D; Figure S6E, right panel).

14

Brain regions were then clustered based on their normalized intensity across15

the four time-points with or without MK-801 administration (Figure 5B), using the16

heatmap.2 function in the gplots R package (Warnes et al., 2014), with Euclidean

17

distance and the “ward.D2” clustering method. For each cluster, a binary mask was

18

created, and the cluster-specific hyper-activated cell signal was extracted (Figures

19

5C-5E, left panels; Figure S6E, left panels). Using the formula above, we also

20

estimated the number of hyper-activated cells in each cluster (Figures 5C-5E, right21

panels; Figure S6E, right panels). Using Cytoscape (Shannon et al., 2003), we

22

also constructed a tree showing the hierarchical annotation structure of the Allen

23

Brain Atlas. Nodes represent brain regions, and are colored based on the cluster they24

belong to Figure 5F. Regions with low signal (i.e. at most two non-zero values over25

the twenty brain samples) were excluded from the clustering and are colored in light26

gray. Spatio-temporal profiling data of the estimated number of hyper-activated cells
18

1

in each brain region are available in Table S6. To avoid showing the cell in multiple

2

clusters due to the tree structure of the brain annotation, only leaf regions are used in3

the image reconstruction when we construct 3D-reconstituted brain images (Figures

4

5C-5E).

5

Cell hyper-activation is not only region-specific, but also layer-specific. To

6investigate this, we also created binary masks for layers in the cerebrum. The same7

analysis as above was performed for layers 1, 2-3 and 4-6. We were therefore able to8

extract the hyper-activated cell signal for these three layers, as well as estimate the9number of hyper-activated cells (Figures 6A-6C).

10

For cell counting analysis of FISH images, Layer 4, 5 and 6 of cerebral cortex

11

in a section image (from visual to motor areas, see Figure S6G) were manually12

cropped. The background signals of c-Fos channel were eliminated by setting an

13

appropriate threshold. To detect sustainably activated neurons of which soma was

14strongly stained by the c-Fos probes, the particle analysis of ImageJ with a range of15

soma’s size was performed. The resulted mask image was manually curated to16

eliminate noise signals and add undetected active cells showing obvious

17

over-threshold signals, and also used to detect double-positive cells by merging Nr2b,

18

Vglut1/2, or Gad1/2 channels from corresponding sections. To count Nr2b, Vglut1/2,

19

Gad1/2 or DAPI-stained cells, the background signals of each channel were20

eliminated in the similar way, and then the images were subjected to the particle

21

analysis of ImageJ after changing to a binary image and calculating watershed.

22

To show the significant difference of the numbers of c-Fos and Nr2b

23

co-expression cells between MK-801 (+) and MK-801 (-), these cell counts were used

24

for F-test. To confirm that co-expression of c-Fos and Nr2b were not a coincidence,25

we performed Chi-squared tests against these cell counts. All statistical analyses
191

were performed via R version 3.1.0. p < 0.05 were considered significant in F-tests

2and Chi-squared tests.

3
4

Fluorescence in situ hybridization with MK-801 administration

5

Male C57BL/6N mice (6-week-old) purchased from CLEA Japan, INC were used.

6

MK-801 maleate (Tocris Bioscience, Lot# 9A/151915) was dissolved in normal saline7

and diluted to be 2 mg/kg at 200 µl. At ZT2, 200 μl of this solution or normal saline

8

were injected intraperitoneally into each mouse. The mice were sacrificed at ZT5, and9

their brain was used for in situ hybridization as follows.

10

The fluorescence in situ hybridization (FISH) protocol was based on

11

TAI-FISH (Xiu et al., 2014) with some modifications. RNase-free reagents and12materials were used during the procedure. For sample preparation, each mouse was

13anesthetized with an overdose of pentobarbital (>100 mg/kg, i.p.; Somnopentyl,

14

Kyoritsu Seiyaku), then transcardially perfused with cold PBS to flush the blood

15

vessels, followed by perfusion with 4% (w/v) paraformaldehyde (PFA) in PBS for

16

fixation. The dissected brain was then post-fixed with 4% PFA at 4°C overnight,17

washed with PBS, and immersed in 20% sucrose/PBS for one day.

18

Probes against Nr2b were amplified from mouse brain cDNA (9-week-old

19

C57BL6/N male brain cDNA, synthesized by ourselves). Probes against c-Fos,

20

Vglut1, Vglut2, Gad1, and Gad2 (kindly provided by Dr. Kazunari Miyamichi) were21

amplified from mouse brain cDNA (first strand cDNA mouse brain MD01, GenoStaff).

22

All the probe fragments were cloned into pCR-BluntII-TOPO vector (Life23

Technologies) for storage, and in vitro transcription (IVT) templates were generated

24

by PCR using the cloned vector. All primers used for cloning and IVT reactions are25

listed in Table S5. The RNA probes labelled with digoxigenin (DIG) -UTP or
20

1fluorescein (Flu) -UTP (Roche) were generated by IVT with T3 polymerase (Roche),2

purified on an Illustra ProbeQuant G-50 column (GE Healthcare). The resulted

3

concentrations of the IVT products were about 50~100 ng/µl, and approximately the4same amount of probes for the same gene were mixed for usage. Probe mixes used

5in this study are c-Fos-1 and 2 (c-Fos probes), Nr2b-1, 2 and 3 (Nr2b probes),

6

Vglut1-1, Vglut2-1, 2, and 3 (Vglut1/2 probes), or Gad1-1, 2, Gad2-1, 2, and 3

7(Gad1/2 probe). Pairs of probe mixes used in this study were c-Fos-Flu and Nr2b-DIG,

8

c-Fos-DIG and Vglut1/2-Flu, or c-Fos-DIG and Gad1/2-Flu, respectively.

9

For fluorescence in situ hybridization, sections (30-40 μm thickness) were10

prepared with a cryostat under RNase-free conditions. Sagittal sections from 3.8-3.9

11mm inside from lateral end of the right hemisphere were used for the analysis in this12

study. Each section was collected into PBS for washing O.C.T. compound, promptly

13

put on a MAS-coated slide glass (Matsunami glass ind., LTD., Japan) and dried up.

14

Then the sections were washed with PBS and treated with 10 μg/ml of protease K in

15

10 mM Tris-Cl (pH 7.4) and 1 mM EDTA for 10 min at 37°C. The treated sections

16

were post-fixed with 4% PFA in PBS for 10 min at room temperature and washed with

17

PBS. The sections were then acetylated with 0.25% acetic anhydride (v/v) in TEA

18solution (1.3% [v/v] triethanolamine, 0.25% [v/v] HCl) for 15 min and washed with

19

PBS. The samples were dried up again and subjected to hybridization by incubation

20

in the hybridization solution (50% formamide [v/v], 0.6 M NaCl, 10 mM Tris-Cl [pH 8.0],

21

5 mM EDTA, 0.3 mg/ml yeast tRNA, 0.1 mg/ml heparin, 1× Denhardt’s solution, 0.1%

22

Tween 20 [v/v] and 0.25% SDS [w/v]) with a pair of probe mixes (1:100-1:200 dilution)

23

overnight at 60°C. After hybridization, the sections were rinsed with 5× SSC, and then24

washed once in 50% (v/v) formamide in 2× SSC, once in 2× SSC, and twice in 0.2×

25

SSC for each 20 min at 65°C.

21

1

Two-color signal detection with the Tyramide Signal Amplification system was

2

performed at room temperature except for the antibody reactions (indicated below).

3

The hybridized sections were incubated in blocking buffer (1% [w/v] blocking reagent

4[Roche 11096176001] in 100 mM maleic acid, 150 mM NaCl and 0.3% Tween 20,

5adjusted to pH 8.0 with NaOH) for 1 hr, and then in the buffer containing anti-Flu-HRP

6(PerkinElmer NEF710001EA, 1:250) overnight at 4°C. On the second day, the7sections were washed three times in PBS containing 0.3% Triton X-100 (PBST) for 10

8

min each, then incubated in the amplification solution with biotin tyramide (1:100,

9PerkinElmer, NEL749A001KT) for 20 min, and washed once with PBST for 10 min.

10

HRP was inactivated with 2% (w/v) sodium azide in PBS for 20 min, followed by three11

washes in PBST for 10 min each to completely remove the sodium azide. The

12

sections were then incubated in the blocking buffer with anti-DIG-HRP (Roche

13

11207733910, 1:500) and streptavidin-Alexa Fluor 488 (Life Technologies, 1:250)

14

overnight at 4°C. On the third day, the sections were washed three times in PBST

15

and incubated in the amplification solution with cyanine 3-labeled tyramide (1:100,

16

PerkinElmer NEL744001KT) for 20 min, and further washed twice with PBST for 10

17min. The sections were embedded with an agent (Vectashield mounting medium with

18

DAPI, Vector Laboratories Inc.). We collected the FISH sections of four saline-treated19

and four MK-801-administrated mice from two independent experiments and two of

20

them with significant FISH signals were selected for further cell counting experiments.

21
22

Statistical analyses

23

Statistical analyses were performed by Microsoft Excel or R version 3.1.0.

24
25

For sleep phenotype analyses of CRISPR KO mice, normality was evaluatedby Kolmogorov-Smirnov test at the significance level of 0.05. The homogeneity of22

1variance for each group was evaluated by Bartlett's test when all the member of the2group was normal, or Levene's test when not. In both tests, significance level was set

3

to 0.05. The sleep phenotype of every KO strain was compared to the control

4(C57BL/6N, male, n=108 or n=101 in UT) by Dunnett’s test when the group has

5homogenous variance and Dunnett's Modified Tukey-Kramer pairwise multiple

6

comparison test when not.

7

To estimate the sample size for phenotype evaluation, we compared the8

sleep duration control dataset Xc (C57BL/6N, male, n=108 or n=101 in UT) to a9

simulated dataset Xs which have the same distribution except a mean of 1 SD (50.810̃ = Xc̃ ± 50.8). We randomly sampled k (2 ≤ k ≤ 20)min) less or much than control (Xs11data from Xs and compared with Xc by Dunnett’s test with the significance level of

12

0.05. This test was repeated 1000 times for one k. We calculated the rate of p < 0.05

13

as sensitivity for each k from 2 to 20. According to this calculation, we found that the14

sample size k ≥ 2 provides more than 25% sensitivity whereas the sample size k ≥ 6

15provides more than 50% sensitivity. In this study, we selected the sample size k ≥ 2 to16

achieve more than 25% sensitivity for the CRISPR knockout study.

17

For the analysis of the experiments by the EEG/EMG recording, means of18

two sets of data were evaluated. First, the normality was tested by Shapiro test at the19

significant level of 0.05. When the normality is confirmed in both groups, the20

homogeneity of variance was tested by F-test at the significance level of 0.05. When21

two groups were normal distributions with equal variance, Student's t-test, when the22groups are normal distributions without equal variance, Welch's t-test, otherwise

23

two-sample Wilcoxon test was applied.

24
25

The i.p. injection was performed blindly without informing the injector thecontent of the drug. Randomization was not performed for processing data.
23

1
2

In this study, p < 0.05 was considered as significant (*p < 0.05, **p < 0.01,***p < 0.001 and n.s. for not significant evaluations).

3

24

1

Supplemental Figure Legends

2

Figure S1. The averaged-neuron model recapitulates slow-wave-sleep and3awake firing patterns, related to Figure 1.

4(A) The ionic currents through NMDA receptors, voltage-gated Ca2+ channels,

5

Ca2+-dependent K+ channels, GABAA receptors, and Ca2+ efflux show alternating

6

bursting and silent cycles in representative slow-wave-sleep firing pattern shown in7

Figure 1B.

8(B-G) Representative awake firing patterns generated by lowering the parameter

9

value of gKCa (B), gNMDA (C), gCa (D), τCa (E), gNMDA / gCa / τCa (F), or gKCa / gNMDA / gCa /10τCa (G), respectively. The membrane potential and ionic currents through NMDA

11

receptors, voltage-gated Ca2+ channels, Ca2+-dependent K+ channels, and GABAA

12

receptors show bursting behaviors whereas intracellular Ca 2+ concentration and Ca2+

13

efflux exhibit more stable behaviors.

14(H-J) The parameter search under NMDA receptor KO (H), voltage-gated Ca2+

15

channel KO (I) and GABAA receptor KO (J) conditions yielded 1,176, 1,011 and 1,069

16

parameter sets that exhibited slow-wave-sleep firing patterns. The normalized

17

membrane potential and intracellular Ca2+ are shown for all parameter sets. Six

18cycles of bursting and silent phases for each slow-wave-sleep firing pattern are drawn.

19

The membrane potential and intracellular Ca2+ are normalized to have a mean of zero20

and one SD for each parameter set.

21(K) Summary of bifurcation analysis of parameter sets that elicited the22slow-wave-sleep firing pattern under GABAA receptor KO conditions. Graph shows

23

the percentage of the slow-wave-sleep parameter sets that switched from24slow-wave-sleep to awake firing patterns when the indicated parameter value was25

1decreased to 10-3 times its original value. Red text indicates the conductance and2time constant related to the Ca2+ hyperpolarization pathway.

3(L) Discovery rate of slow-wave-sleep firing patterns during parameter searches

4

under wild-type or ion channel KO conditions.

5

26

1

Figure S2. Impairment of Ca2+-dependent K+ channels decreases sleep duration,2

related to Figure 2.

3(A-H) Target sequences of the gRNAs for KO mice of the Ca2+-dependent K+ channel

4family [Kcnma1 (A), Kcnn1 (B), Kcnn2 (set 1, C), Kcnn3 (set 1, D), Kcnn4 (E), Kcnt15(F), Kcnt2 (G), and Kcnu1 (H)]; each gene had three target sequences. Mouse

6

genomic sequence data were obtained from GRCm38/mm10 via the UCSC Genome

7

Browser (Rhead et al., 2010) (http://genome.ucsc.edu/). Colored letters (blue, orange,

8

and green) show the 20-base target sequences. The target sequences were9

designed on the sense (+) or antisense (-) strand of genomic DNA.

10(I) Distributions of sleep duration of the KO mice of Ca2+-dependent K+ channels

11

compared in a different time of the day. Morning and night are defined as ZT0 to ZT1212and ZT12 to ZT24, respectively. Histogram at the top of each chart shows the results

13

from C57BL/6N male mice (n = 108). The error bar is the SEM for each strain, and the14

black dashed vertical line and gray shaded area are the mean and 1 SD range from15

the recordings of C57BL/6N male mice. *p < 0.05, **p < 0.01, ***p < 0.001, compared16

to C57BL/6N male mice, Dunnett’s test.

17(J and K) Target sequences of the gRNAs for independent CRISPR/Cas9 probe sets18

for knocking out genes of Kcnn2 (set 2, J) and Kcnn3 (set 2, K); each gene had three

19

target sequences. Mouse genomic sequence data were obtained from20

GRCm38/mm10 via the UCSC Genome Browser (Rhead et al., 2010)21(http://genome.ucsc.edu/). Colored letters (blue, orange, and green) show the22

20-base target sequences. The target sequences were designed on the sense (+) or23

antisense (-) strand of genomic DNA.

27

1(L) Sleep-duration phenotype based on EEG/EMG for wild-type mice (n = 4) and2

KCa2.2 (Kcnn2) KO mice (n = 9) generated by triple-target CRISPR. Average Pws (left)

3

and Psw (right) are shown in bar graphs. Error bars: SEM.

4

28

1

Figure S3. Genotyping of KO mice by CRISPR/Cas9 injection, related to Figure

2

2.

3

The genotype of KO mice for the Ca2+-dependent K+ channel family [Kcnma1 (A),4

Kcnn1 (B and C), Kcnn2 (D, K, M and N), Kcnn3 (E and L), Kcnn4 (F), Kcnt1 (G),5

Kcnt2 (H and I) and Kcnu1 (J)]; The relative amount of intact DNA for each target

6

sequence was measured by quantitative PCR (qPCR). The genomic DNA was

7

purified from the tail of each mouse. The relative amount of intact DNA for each target

8

sequence was scaled so that the level in a wild-type mouse was defined as 100%.

9Genotyping by sequencing was done for a Kcnn1 KO mouse (C), three Kcnt2 KO

10mice (I), and seven Kcnn2 KO mice (N). The sequences of mutant alleles in each11mouse are shown. The target sites are colored and underlined. The mutations are

12labeled in red. The numbers of each detected allele are put in parentheses. The KO

13

mice, which are confirmed by 1st qPCR, 2nd qPCR or sequencing, are labeled in red,14

green or blue, respectively.

15
16

29

1

Figure S4. Impairment of voltage-gated Ca2+ channels decreases sleep duration,2

related to Figure 3.

3(A-L) Target sequences of the gRNAs for KO mice of the voltage-gated Ca2+ channel

4family [Cacna1a (A), Cacna1b (B), Cacna1c (C), Cacna1d (D), Cacna1e (E), Cacna1f

5(F), Cacna1g (set 1, G), Cacna1h (set 1, H), Cacna1i (I), Cacna1s (J), Cacna1g (set 2,6K) and Cacna1h (set 2, L)]; each gene had three target sequences. Mouse genomic

7

sequence data were obtained from GRCm38/mm10 via the UCSC Genome Browser

8(Rhead et al., 2010) (http://genome.ucsc.edu/). Colored letters (blue, orange, and9green) show the 20-base target sequences. The target sequences were designed on

10

the sense (+) or the antisense (-) strand of genomic DNA. (D) The lowest variant

11(magenta) is an additional variant of Cacna1d predicted on C57BL/6 genome

12

sequence (GenBank, http://www.ncbi.nlm.nih.gov/nuccore/). Our triple gRNAs are13

designed to target all variants. A green square shows the region which used the14targeted disruptions of Cacna1d gene in previous reports (Namkung et al., 2001;

15

Platzer et al., 2000). There is a possibility that two variants (magenta) express in the16

KO mice in these reports.

17(M-T) The genotype of KO mice for the voltage-gated Ca2+ channel family [Cacna1b18(M), Cacna1e (N), Cacna1f (O), Cacna1g (set 1, P), Cacna1h (set 1, Q), Cacna1i (R),19

Cacna1g (set 2, S) and Cacna1h (set 2, T)]; the relative amount of intact DNA for

20

each target sequence was measured by quantitative PCR (qPCR). The genomic DNA

21

was purified from the tail of each mouse. The relative amount of intact DNA for each22

target sequence was scaled so that the level in a wild-type mouse was defined as

23

100%.

24

(U) Genotyping by sequencing was done for three of Cacna1h KO mice (set 2). The

25

sequences of mutant alleles in each mouse are shown. The target sites are colored30

1

and underlined. The mutations are labeled in red. The numbers of each detected2

allele are put in parentheses.

3

31

1

Figure S5. Impairment of NMDA receptors decreases sleep duration, related to2

Figure 43(A) Dose-dependent sleep phenotype resulting from a single i.p. injection of4phencyclidine (PCP) to C57BL/6J mice at ZT2. Heatmaps show the sleep duration

5(per hour) for all mice in three groups (0, 13.3, and 26.7 mg/kg). The 0 mg/kg group6

was used as a control. Red bar at the top of the plot shows the period used for7

comparing the parameters in Figure S5B.

8(B) PCP significantly reduced the sleep duration and Pws in a dose-dependent

9

manner for 5 hr (ZT2 to 6) after its i.p. injection. (Left) Average sleep duration. (Middle

10

and right) Average Pws and Psw. Error bars: SEM.

11(C) Dose-dependent sleep phenotype of a single i.p. injection of MK-801 to C57BL/6J

12

mice at ZT2. Heatmaps show the sleep duration (per hour) for all mice in four groups

13(0, 0.2, 2 and 20 mg/kg). The 0 mg/kg group was used as a control. Red bar at the top

14of the plot shows the period used for comparing the parameters in Figure S5D.

15(D) MK-801 significantly reduced the sleep duration and Pws in a dose-dependent

16

manner for 5 hr (ZT2 to 6) after its i.p. injection. (Left) Average sleep duration. (Middle

17

and right) Average Pws and Psw. Error bars: SEM.

18(E) Sleep phenotype during chronic administration of MK-801 to C57BL/6J mice.

19

Heatmaps show the sleep duration (per hour) for all mice in five groups, which were

20

given MK-801 in the drinking water at different concentrations (0, 16, 32, 64 and 96

21mg/l). The 0 mg/l group was used as a control.

22(F) Sleep duration (per hour) over 24 hr averaged over six days under the chronic

23

administration of MK-801. Red lines show the mean sleep duration at each time of32

1

day for each group. Gray line shows the data for C57BL/6N male mice (n = 108). The

2shaded area around the line is the SEM for each time point.

3(G) Chronic administration of MK-801 decreased the sleep duration in a4

dose-dependent manner. Panels show the averaged daily sleep duration, amplitude,

5

Pws, and Psw. Error bars: SEM.

6*p < 0.05, **p < 0.01, ***p < 0.001, compared to control group in each experiment,

7

Dunnett’s test or Dunnett’s modified Tukey-Kramer pairwise multiple comparison test

8

(B, Pws).

9

33

1

Figure S6. Impairment of the Ca2+-dependent hyperpolarization pathway

2

increases neural excitation, related to Figure 5 and 6.

3(A) Temporal variation of 3D-reconstituted brain images of TOPRO3-stained

4Arc-dVenus Tg mice with and without the chronic administration of MK-801. After5

measuring the sleep/wake phenotype under LD condition for 4 days, Arc-dVenus Tg

6

mice with (n = 3 per time point, the lower row) and without (n = 2 per time point, the7upper row) the chronic administration of MK-801 were kept in the dark, and the brains8

were sampled every 6 hr. The sampled brains were then cleared using the CUBIC

9reagents. Images were acquired with light-sheet fluorescence microscopy (LSFM)

10

from both the dorsal-to-ventral (D-V) and ventral-to-dorsal (V-D) directions.

11

Representative images of each time point acquired from the D-V direction are shown.

12

Green and gray indicate Arc-dVenus and TOPRO3 signals, respectively. Data were13

normalized so that the median intensity of the Arc-dVenus signals inside the brain

14

matched across all samples. CT: Circadian time. Bar: 2000 μm.

15(B) Sleep duration (per hour) over 24 hr averaged over 4 days before the animals

16

were set in constant darkness for brain sampling. The lines show the mean sleep

17

duration at each time of day for each group. Gray line shows the data for C57BL/6N

18

male mice (n = 108). The shaded area around the line is the SEM for each time point.

19

The size of the dot denotes the p-values when the sleep duration is tested by20

Dunnett’s test or Dunnett's Modified Tukey-Kramer pairwise multiple comparison test

21

with the wild-type animal recording as a control.

22(C) Quantification of hyper-activated cells. Hyper-activated cells were automatically

23

extracted using a macro developed with the Fiji software. Left: Representative slice

24

images at CT4 with and without the chronic administration of MK-801 are shown.

34

1

Middle: Magnified images are shown. Right: Blue and red indicate hyper-activated2

cells with and without the chronic administration of MK-801.

3(D) Relationship between the intensity of the signals extracted from detected

4

hyper-activated cells and the number of detected hyper-activated cells in the whole5

brain.

6(E) Temporal variation of the number of hyper-activated cells in the purple cluster.

7

Left: 3D-reconstituted brain images of the Arc-dVenus signals extracted from cells

8detected as hyper-activated and located in regions belonging to the purple cluster of9the heatmap (shown in purple). Gray indicates TOPRO3 signals. To avoid showing

10

the cell in multiple clusters due to the tree structure of the brain annotation, only leaf

11

regions are used in the image reconstruction. CT: Circadian time. Bar: 2000 μm.

12Right: Estimated number of hyper-activated cells in the purple cluster and at each13

time-point with (n = 3, red) or without (n = 2, blue) the chronic administration of

14

MK-801. Error bars: SEM.

15

(F and G) Images of FISH analysis (using c-Fos and Nr2b probes) of a sagittal

16

section from animals subjected to intraperitoneal saline (F) or MK-801 (G)17administration. The cropped regions shown in Figures 6E and 6F are indicated with18

white boxes. Cortical areas between dashed lines were used for cell counting

19

analysis in Figure 6G. The corresponding cortical areas were also used for cell

20

counting analysis in Figure 6J. Bar: 1 mm

21

(H and I) Images of FISH analysis of the cerebral cortex from an MK-801-treated22mouse using c-Fos and either Vglut1/2 (H) or Gad1/2 (I) probes, respectively. The

23

cropped regions shown in Figures 6H and 6I are indicated with white boxes. Bar: 100

24

μm.

35

1

Figure S7. Impairment of Ca2+ pump decreases sleep duration and impairment

2of CaMKII decreases sleep duration, related to Figure 7.

3(A-H) Target sequences of the gRNAs for KO mice of the plasma membrane Ca2+

4

ATPase and the calcium/calmodulin-dependent protein kinase family [Atp2b1 (A),5

Atp2b2 (B), Atp2b3 (set 1, C), Atp2b4 (set 1, D), Camk2a (E), Camk2b (F), Camk2d

6(G) and Camk2g (H)]; each gene had three target sequences. Mouse genomic

7

sequence data were obtained from GRCm38/mm10 via the UCSC Genome Browser

8(Rhead et al., 2010) (http://genome.ucsc.edu/). Colored letters (blue, orange, and9green) show the 20-base target sequences. The target sequences were designed on

10

the sense (+) or antisense (-) strand of genomic DNA.

11

(I and O) Target sequences of the gRNAs for independent CRISPR/Cas9 probe sets

12for KO mice of Atp2b3 (set 2, I), Camk2a and Camk2b (set 2, O); each gene had

13three target sequences. Mouse genomic sequence data were obtained from14

GRCm38/mm10 via the UCSC Genome Browser (Rhead et al., 2010)15(http://genome.ucsc.edu/). Colored letters (blue, orange, and green) show the16

20-base target sequences. The target sequences were designed on the sense (+) or17

antisense (-) strand of genomic DNA.

18

(J-N, P, and R-V) The genotype of KO mice for the plasma membrane Ca2+ ATPase19and calcium/calmodulin-dependent protein kinase family [Atp2b2 (J), Atp2b3 (K-M),

20

Atp2b4 (N), Camk2a (P and R), Camk2b (S and T), Camk2d (U) and Camk2g (V)];21

The relative amount of intact DNA for each target sequence was measured by

22

quantitative PCR (qPCR). The genomic DNA was purified from the tail of each mouse.

23

The relative amount of intact DNA for each target sequence was scaled so that the24level in a wild-type mouse was defined as 100%.

361(Q) Genotyping by sequencing was done for two Camk2a KO mice (set 1). The

2

sequences of mutant alleles in each mouse are shown. The target sites are colored3

and underlined. The mutations are labeled in red. The numbers of each detected

4

allele are put in parentheses.

5
6
7

37

1

Supplemental Movies

2

Movie S1. Whole-brain Arc-dVenus image of control mice, related to Figure 5A.

3

Representative reconstituted 3D images of the Arc-dVenus mouse brain at CT4 and4CT16 without the chronic administration of MK-801. Green: Arc-dVenus signals.

5

Gray: TOPRO3 signals. CT: Circadian time.

6

38

1

Movie S2. Whole-brain Arc-dVenus image of MK-801 treated mice, related to2

Figure 5A.

3

Representative reconstituted 3D images of the Arc-dVenus mouse brain at CT4 and4CT16 with the chronic administration of MK-801. Green: Arc-dVenus signals. Gray:

5

TOPRO3 signals. CT: Circadian time.

6

39

1

Movie S3. Brain-region analysis of hyper-activated cells in MK-801 treated and2

untreated mice, related to Figures 5C-5E.

3

Representative 3D-reconstituted brain images of the Arc-dVenus signals extracted

4

from cells detected as hyper-activated and located in regions belonging to the same5

heatmap cluster (shown in pink, yellow, and blue) at CT4 and CT16 with and without

6

the chronic administration of MK-801. Gray indicates TOPRO3 signals. CT: Circadian

7

time.

8

40

1

Movie S4. Cortex-layer analysis of hyper-activated cells in MK-801 treated and2

untreated mice, related to Figures 6A-6C.

3

3D-reconstituted brain images of the Arc-dVenus signals extracted from cells

4detected as hyper-activated and located in regions belonging to the same layer

5(shown in cyan (layer 1), magenta (layer 2-3), and yellow (layer 4-6)) at CT4 with and6without the chronic administration of MK-801. Grey indicates TOPRO3 signals. CT:

7

Circadian time.

8

41

1

Supplemental Tables

2

Table S1. Parameter values in the averaged-neuron model, related to3

Simulations in Experimental procedures.

4

Values from C to τGABA were based on previous studies (Bazhenov et al., 2002; Chen5

et al., 2012; Compte et al., 2003; Hill and Tononi, 2005; Sanchez-Vives et al.,6

2010; Timofeev et al., 2000). The revised values are indicated by asterisks. The area

7of neuron (A) was revised because we did not divide the model neuron into dendritic

8

and synaptic parts. The coefficient of Ca2+-entry (αCa) is estimated under the9

assumption that the average volume of a single neuron is ~10 pl. The increase in10

intracellular Ca2+ concentration caused by Ca2+ currents was determined as follows: 1

11

nA Ca2+ current for 1 ms causes 1 nA × 1 ms / ~10 pl = 1 p C/ ~10 pl = ~ 0.5 /10 F

12mol/L = ~ 0.5 μM increase in the intracellular Ca 2+ concentration, where F = ~0.96 ×

13

105 is the Faraday constant. The values from gL to τCa are an example of parameter

14

sets that yield slow-wave-sleep firing patterns in the wild-type parameter search. This

15

parameter set was used in Figures 1B and 1C; Figure 1F (dash line); Figures

16

S1A-S1G. In the parameter searches, 13 parameter values (gL to τCa) are randomly

17

searched.
C

Membrane capacitance

1 μF/cm2

A

Area of neuron

0.02 mm2 *VLLeak reversal potential-60.95 mV

VNa

Sodium reversal potential

55 mV

VK

Potassium reversal potential-100 mVτhA

Time-constant of hA

15 ms

VCa

Ca

2+

reversal potential

120 mV
2+

KD

Dissociation constant of Ca -dependent K
channels

VAMPA

APMA receptor reversal potential
42

+

30 μM
0 mV

VNMDANMDA receptor reversal potential

0 mV

VGABA

GABAA receptor reversal potential-70 mV

2+

αCa

Coefficient of Ca -entry

0.5 μM/(nA ms) *τAMPA

Time-constant of sAMPA

2 ms

τsNMDA

Time-constant of sNMDA

100 msτxNMDA

Time-constant of xNMDA

2 ms

τGABA

Time-constant of sGABA

10 ms
+

gL

The conductance of the leak K channels

0.03573 mS/cm2

gNa

The conductance of the voltage-gated Na+channels

12.2438 mS/cm2

gK

The conductance of the voltage-gated K+
channels

2.61868 mS/cm2

gA

The conductance of the fast A-type K+
channels

1.79259 mS/cm2

gKS

The conductance of the slowly inactivating K+
channels

0.0350135 mS/cm2

gNaP

The conductance of the persistent Na+
channels

0.0717984 mS/cm2

gAR

The conductance of the inwardly rectifying K+
channels

0.0166454 mS/cm2

gCa

The conductance of the voltage-gated Ca2+

0.0256867 mS/cm2

channels
gKCa

The conductance of the Ca2+-dependent K+
channels

2.34906 mS/cm2

gAMPA

The conductance of the AMPA receptors

0.513425 μS

gNMDA

The conductance of the NMDA receptors

0.00434132 μS

gGABA

The conductance of the GABAA receptors

0.00252916 μS

τCaThe time constant of Ca2+ efflux

121.403 ms

1

43

1

Table S2. Oligonucleotide sequences for the T7-gRNA templates, related to2

Animals and sleep phenotyping in Experimental procedures.

3

gRNAs were produced by the T7-gRNA templates of Kcnn1, Kcnn2, Kcnn3, Kcnn4,

4

Kcnt1, Kcnt2, Kcnu1, Cacna1a, Cacna1b, Cacna1c, Cacna1d, Cacna1e, Cacna1f,

5

Cacna1g, Cacna1h, Cacna1i, Cacna1s, Atp2b1, Atp2b2, Atp2b3, Atp2b4, Camk2a,6

Camk2b, Camk2g, and Camk2d which were made from oligonucleotides (Hokkaido

7

System Science). The alternative target sequence for Kcnn2, Kcnn3, Cacna1g,

8Cacna1h, Atp2b3, Camk2a, and Camk2b (set 2 in Figures S2J and S2K; Figures

9

S4K and S4L; Figures S7I and S7O) are shown as gRNA No. 4-6 in each gene.

10

44

1

Table S3. Primer sequences used in quantitative PCR (qPCR), related to2

Animals and sleep phenotyping in Experimental procedures.

3

1st qPCR primers (Hokkaido System Science) were used in genotyping of knockout

4

mice Kcnma1, Kcnn1, Kcnn2, Kcnn3, Kcnn4, Kcnt1, Kcnt2, Kcnu1, Cacna1b,5Cacna1e, Cacna1f, Cacna1g, Cacna1h, Cacna1i, Atp2b2, Atp2b3, Atp2b4, Camk2a,6

Camk2b, Camk2g, and Camk2d. The alternative 2nd qPCR primers were used in

7

genotyping of knockout mice of Kcnn2, Atp2b3, Camk2a, Camk2b, and Camk2g.

8
9

45

1

Table S4. Primer sequences used in cloning for genotyping, related to Animals

2and sleep phenotyping in Experimental procedures.

3

Primer sequences used in cloning for genotyping of Kcnn1, Kcnn2, Kcnt2, Cacna1h,

4

and Camk2a (Hokkaido System Science).
GenegRNA No.
1

Kcnn1
2 and 3
1
Kcnn2
2 and 3
1
Kcnt2

2
3
1

Cacna1h2
3
1

Camk2a2 and 3

PrimerSequence (5' to 3')

Forward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseForward
ReverseGATGACAGTGGACAGGCTGATGAGGC
AGGTCCATTCAGAAGCTCTGCTGCGGCTTCAGCTGGACCTGGTGGCTCTGAGC
GCTGTTCTTGGTGGACAATGGTGCCGGGATAACCTGTCCCTGCTGCTCC
GGGACACTTAAGACCTGTTAGACAACCTAAAGTACTTAAACTCCGGAGCACATGTTGATAGAAGCAGTGAACCAACACCACCTACCCTCTTTCTAGGCACCGC
CCCAGTGGCAAGCAAACATCTCGTCGAGGAGCTGTTTTTAGGATTGGGAAACAGGGGCGCATGACCATCACCCAGTGACTACACCAC
GTGGCACTGTGGCTGACTGAGCCTGAC
GTACCGCCATCCCACCCCATCTCTGCC
GACCCTCACTTAGGCTACCAGGCTCCTGGGATTCCCAAGCATACCCAGCC
CTATCCAGTGTGCGTGTTGCGGGACTCGCAGGATGCCCACCAGCTGGCCAC
GCCTCACAGGACTAGGCCGAGCCTC
GCATAGAGGCCCTTGCAGTGCCTCGTGACAGCCACTAAATCAGGCAACGTG
GAGGTCAAAGACCTGTGGAGAACCAGGGC
CCATTATTTGGGTTACACTAGGGGAAGAATTCCAGCAAAATCCAAAGGAG

5

46

1

Table S5. FISH primers, related to Fluorescence in situ hybridization in2

Experimental procedures.

3

FISH probes for Gad1, Gad2, Vglut1, Vglut2, c-Fos, and Nr2b were produced by in4

vitro transcription with T3-probe PCR fragments. These probes were used as mixes;

5c-Fos-1 and 2 (c-Fos probes), Nr2b-1, 2 and 3 (Nr2b probes), Vglut1-1, Vglut2-1, 2

6

and 3 (Vglut1/2 probes), or Gad1-1, 2, Gad2-1, 2 and 3 (Gad1/2 probes).
Gene

No.
1

Gad1
2
1
Gad2

2
3

Slc17a7(Vglut1)

1
1

Slc17a6(Vglut2)

2
3
1

c-Fos
2
1
Grin2b (Nr2b)2

PrimerSequence

Forward

GCCTGAAGATCTGTGGCTTC

ReverseAATTAACCCTCACTAAAGGGTAGCCCTTTTGTTTGGCATC

ForwardCACAAACTCAGCGGCATAGA

ReverseAATTAACCCTCACTAAAGGGGGACGAGCAACATGCTATGG

ForwardGGGATGTCAACTACGCGTTT

ReverseAATTAACCCTCACTAAAGGGTGCATCAGTCCCTCCTCTCT

Forward

CTCCAATCCCCTTCTTCTCC

ReverseAATTAACCCTCACTAAAGGGTGTGCATCCTTTGTCCATGT

ForwardAGCCAGGGCTACACAGAGAA

ReverseAATTAACCCTCACTAAAGGGTTTGCTAGGGCTGATTTGGT

ForwardCTGGCAGTGACGAAAGTGAA

ReverseAATTAACCCTCACTAAAGGGACACAACAAATGGCCACTGA

ForwardTAGCTTCCTCTGTCCGTGGT

ReverseAATTAACCCTCACTAAAGGGGGGCCAAAATCCTTTGTTTT

ForwardCCACCAAATCTTACGGTGCT

ReverseAATTAACCCTCACTAAAGGGGGAGCATACCCCTCCCTTTA

Forward

CTCCCCCATTCACTACCTGA

ReverseAATTAACCCTCACTAAAGGGGGTCAGGAGTGGTTTGCATT

ForwardAGCGAGCAACTGAGAAGACTG

ReverseAATTAACCCTCACTAAAGGGATCTCCTCTGGGAAGCCAAG

ForwardCCAGTCAAGAGCATCAGCAA

ReverseAATTAACCCTCACTAAAGGGCATTCAGACCACCTCGACAA

ForwardCGGGCTACTAACCTCACATGC

ReverseAATTAACCCTCACTAAAGGGCCAAACTGGAAGAACATGGAGGAC

Forward

ACTTCGTGAACAAGATCCGCAG

ReverseAATTAACCCTCACTAAAGGGCATGCAGGTCCCACTGAGAG

47

3

Forward

ATCTCTTCTATTGGCAGTTCCGAC

Reverse AATTAACCCTCACTAAAGGGGACTGTCCTTATATTGCTGCTTCCTC*T3 promoter sequence is underlined.

1

48

1

Table S6. Spatio-temporal profiling of the estimated number of hyper-activated2

cells in each brain region, related to Figure 5B

3

It contains temporal variation in the estimated number of hyper-activated cells for 826

4different brain regions, ordered alphabetically by their abbreviation. For each region,5

we give: abbreviation, full name, cluster (with “N/A” for regions filtered out before the6clustering due to low signals), normalized 8-point data for clustering and all 20-brains

7

data.

8

49

1

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53

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)

-0.1

0
0.1

0.1

0

-1

0.05

0

5

Time (sec)

-0.1

-0.1

0.7

-0.1

12

0
0.1

0.1

-1

0.05

0

5

Time (sec)

G

35

0

-0.1
0
5

Low gKCa (0.5 × Original)
Low gNMDA (0.75 × Original)
Low gCa (0.15 × Original)
Low ȉCa (0.8 × Original)
-3

-85
0.2

K

-0.1

0.7

-0.1

12

0
0.1

0.1

0

-1

0.05

0

5

Time (sec)Figure S1. F. Tatsuki et al.
Parameter sets

06

Membrane Potential
n = 1,069
-2
-1
0
1

0

100

gNMDA

gKCa

gCa

+
+
+

+
+

+
+

Normalized Ca2+
-3

2

3

0

6
-3

1.1×10-5

+
+
-

a

-85
0.2
-0.1
0

N
M
D
A

Low gNMDA (0.75 × Original)
Low gCa (0.15 × Original)
Low ȉCa (0.8 × Original)J
Parameter sets

2

PA

0.05

Normalized Ca2+

5
3

AR

Time (sec)Membrane Potential
n = 1,011
-2
-1
0
1

Normalized Ca2+
-3

ȉC

12
0.1

6

g

-0.1
35
0

Parameter sets

Parameter sets

NMDA receptor
currents
(nA)

2

AM

F
0
-3

0

N
aP

Low ȉCa (0.8 × Original)
-1
0

g

3

g

-0.1
Membrane Potential
n = 1,176
-2
-1
0
1

g

0.05
0
0.1

C
a

0
12

KC
a

-85
0.2

H

g

E
-0.1
-0.1

KS

5

I

g

0.1
35

Low gCa (0.15 × Original)

K

Time (sec)

D

A

10

g

-3

g

-1
0.05

Normalized MP

0.1

g

0
0
0.1

L

-1
12
-85
0.2
Parameter sets

0

N
a

-0.1

Normalized MP

Ca2+-efflux Voltage-gated
Ca2+ currents( μM/ms)
(nA)
0.1

g

0
NMDA receptor Membrane
Potential
currents(mV)(nA)

Time (sec)Parameter sets

0.7

Intracellular Voltage-gated
Ca2+-currents
Calcium(nA)(μM)

GABAA receptor
Ca2+-dependent
currents
K+ currents
(nA)
(nA)

Original

Normalized MP

0.05
-0.1

g

0.1
-85
0.2

Bifurcation from Sleep to Awake (%)

1235

Low gNMDA (0.75 × Original)% slow-wave-sleep firing patterns-0.1

Ca2+-efflux( μM/ms)C

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)

NMDA receptor Membrane
Potential
currents
(mV)
(nA)

-0.1

NMDA receptor Membrane
Potential
currents
(mV)
(nA)

0.7

Intracellular Voltage-gatedCalcium
Ca2+-currents
(μM)
(nA)

0
0.1
Ca2+-efflux( μM/ms)

NMDA receptor Membrane
Potential
currents
(nA)
(mV)
-85
0.2

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)

Intracellular Voltage-gatedCalcium
Ca2+-currents
(μM)
(nA)
35

Low gKCa (0.5 × Original)

Intracellular Voltage-gatedCalcium
Ca2+-currents
(μM)
(nA)

35

NMDA receptor Membrane
Potential
currents(mV)(nA)

Ca2+-efflux( μM/ms)B

Ca2+-efflux( μM/ms)

0.7

Intracellular Voltage-gatedCalcium
Ca2+-currents
(nA)
(μM)

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)
-0.1

Ca2+-efflux( μM/ms)

NMDA receptor Membrane
Potential
currents
(mV)
(nA)
-0.1
0

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)

Intracellular Voltage-gatedCalcium
Ca2+-currents
(μM)
(nA)
0.2

GABAA receptor
Ca2+-dependent
currents
K+-currents
(nA)
(nA)

Ca2+-efflux( μM/ms)A

NMDA receptor KO

0

0.7

0

3

0

Intracellular Calciumn = 1,176
-2
-1
0
1
2

Bursting cycles

06Voltage-gated Ca2+ channel KO

-0.1

0.7

3

0

Intracellular Calciumn = 1,011
-2
-1
0
1
2

Bursting cycles

0
6

GABAA receptor KO-1Time (sec)

30Intracellular Calciumn = 1,069
-2
-1
0
1
2

Bursting cycles
0
6

GABAA receptor KO

80

60

40

20

L

0.015

0.010

0.005

9.2×10-5

+
-

A

B

Kcnma1chr14(-)

23,900,000

23,700,000

23,500,000

chr8(-)

Kcnn1
70,855,000

70,845,000
Kcnn1

Kcnma1
ccctctttgaaaagcgtaaa (+)
Kcnn1 target sequence-1

gatggatgcgctcatcatac (+)
Kcnma1 target sequence-1

agaccaacaacacagtgccg (-)
Kcnn1 target sequence-3

gataaacaatggctccagcc (+)
gtctatgaagtatattacga (-)
Kcnma1 target sequence-2 Kcnma1 target sequence-3

CD

Kcnn2 (set 1)

chr18(+)

gttgaaggtgacgcggttaa (-)
Kcnn1 target sequence-2

45,600,000

45,650,000

chr3(+)

Kcnn3 (set 1)
89,550,000

89,600,000

89,650,000

Kcnn2

gcttagacaccacgatctcc (-)
Kcnn2 target sequence-1

gatcatcgtgtaccacgcca (+)
Kcnn2 target sequence-2

E

gtaccacgccagggaaatac (+)
Kcnn2 target sequence-3

24,375,000

gacacttggggtcttcatcc (-)
Kcnn3 target sequence-1

gctcctgcaaatacagcggt (+)
Kcnn3 target sequence-2

F

Kcnn4

chr7(+)

Kcnn3

24,380,000

gaggatacgctcgtaggtca (-)
Kcnn3 target sequence-3

Kcnt1

chr2(+)

25,880,000

25,910,000

Kcnn4

gggtgatcaaacacttaacc (-)
Kcnn4 target sequence-1

gctgtacatgaacacgcacc (+)
Kcnn4 target sequence-2

G

gcagacaatcttgccccaca (-)
Kcnn4 target sequence-3

140,300,000

gccacttacctatcgtcgtt (-)
Kcnt1 target sequence-1

gaaatcttccacatgaagcc (-)
Kcnt1 target sequence-2

H

Kcnt2

chr1(+)

Kcnt1

140,450,000

140,600,000

chr8(+)

gtgatgtgacgcccaagatc (+)
Kcnt1 target sequence-3

Kcnu1
25,860,000

25,890,000

25,920,000
Kcnu1

Kcnt2

gattccgggatttgctgcta (+)
Kcnt2 target sequence-1

ggaacaacttacttgatcgc (-)
Kcnt2 target sequence-2

gtgtagtttcatcttcggac (-)
Kcnt2 target sequence-3

I
Morning

gtcatgtacaatcgaaatcc (+)
Kcnu1 target sequence-1

gaagttggaaatacgtcaag (+)
Kcnu1 target sequence-2

J

Night

gaatcgaggctaatcctttg (+)
Kcnu1 target sequence-3

Kcnn2 (set 2)

chr18(+)

45,600,000

45,650,000
Kcnn2

Kcnma1
Kcnn1

ggagatcgtggtgtctaagc (+)
Kcnn2 target sequence-4

Kcnn2 (set 1)

ctaacaacctggcgctctac (+)
Kcnn2 target sequence-5

gtgctatccatcccatacct (+)
Kcnn2 target sequence-6

Kcnn3 (set 1)

K

Kcnn4
Kcnt1

chr3(+)

Kcnt2

Kcnn3 (set 2)
89,550,000

89,600,000

89,650,000
Kcnn3

Kcnu1
300

400

500

600

100

200

300

Sleep duration (min)400

ctctcggagaaaccttatcg (+)
Kcnn3 target sequence-4

L
n.s.

n.s.

0.04

0.03
PSW

PWS

0.03
0.02
0.01
0.00

0.02
0.01
0.00

WT Kcnn2

WT Kcnn2

Figure S2. F. Tatsuki et al.

gcgtatcctctacatcagcc (+)
Kcnn3 target sequence-5

gagcgtcttcatgacgaatc (-)
Kcnn3 target sequence-6

BC

Kcnma1

Genome DNA/ Tbp(wild-type =100%)

target-1

120

250

100

200

80

150

60

100

40

50

20

0

0

wt

1

E
Genome DNA/ Tbp(wild-type =100%)

2

4

3

5

target-2

wt

1

80
60

40

40

20

20
3

G

4

3

4

5

1

2

3

4

5

6

120

40

40

20

20
5

6

I

Kcnt2 #3

0

7

7

8

9

10

11

12

13

target-1

target-3
TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTC-GACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTATTCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTTTCAT------------------CATGTTTGAATTGATGATGTAT

wt

1

2

3

4

5

6

Genome DNA/ Tbp(wild-type =100%)

Kcnn2 (set 2)

140

Reference:
Mouse #6:

20

0

0

5

6

7

8

9

10

12

13

M

Kcnn3 (set 2)

120

11

14

15

target-2
TCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG
TCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG wild type (12)TTGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG 2 bp-mutation (1)TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGACCTCATAAAAAATCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG +12 bp (1)target-3
Reference: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCG GACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTATMouse #6: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCGCGACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTATTCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTT-- --CTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTAT

wt

1

2

120

100

100

100

80

80

80

60

60

60

40

40

40

20

20
wt

1

2

3

4

5

6

0

7

+1 bp (6)
-4 bp (7)3

Kcnn2 (set 1 for EEG/EMG, 1st qPCR)

120-3 bp (8)-2 bp, +6 bp (1)-2 bp, +6 bp (3)Reference: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGA
Mouse #6: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGATAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGA

40

20

0

target-1

GAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGCTG
CTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCAGAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGC--TAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCAGAAGTGCCCCCTCTGCCTTCCAGGTACCGATTCCGGGATTTGCAAATTCCTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCAGAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGCAAATCCCTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCA

60

40

wild type (6)-1 bp (1)-23 bp, +5 bp (5)

7

80

L
Genome DNA/ Tbp(wild-type =100%)

K

60

4

wild type (8)-8 bp (8)Reference: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCGGACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTATMouse #4: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCGGACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTAT

100

3

5

Reference: GAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGCTGCTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCA
Mouse #4: --------------(221base)-------------------------------------------TGGAATGATGACAGGTGAGTGGCA -221 bp, +3 bp (8)

120

2

4

target-2

80

1

3

TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTA--------CTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG

100

wt

2

Reference: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGATCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG
Mouse #4: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGATCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG

Kcnu1

120

1

Reference: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCGGACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTATMouse #3: TCTAAATTTGAAGATGTTTCTTCATCTGGTGTAGTTTCATCTTCGGACTGGTCACTTAGGAGATCGCATGTTTGAATTGATGATGTAT wild type (6)Kcnt2 #6

Jwttarget-1

Kcnt2 #4

60

4

0

Kcnt2

60

3

20

7

H

80

2

40

target-3
wt

100

1

60

target-2

80

wt

80

Reference: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGATCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG
Mouse #3: TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACTTGATCGCTGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG wild type (12)TAGATTTCTATGCAAAGAGCCAGTGATGGGAACAACTTACT-------TGGTTTTTTATGAAAAACAGTTTTAATCTCTCTTTAAATG -7 bp (4)

0

6

100

Reference: GAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGCTG CTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCA
Mouse #3: GAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGG-------- ------GACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCA -14 bp (7)GAAGTGCCCCCTCTGCCTCCCAGGTACAGATTCCGGGATTTGCTGACTAGGGGACCAAGGCTGGCAAAATGATGACAGGTGAGTGGCA +1 bp (7)

100

0

6

Kcnn4

Kcnt1

120

5

F

60

2

2

120

80

1

target-3

Reference: AGCATCGGGGCCCTTAACCGCGTCACCTTCAACACACGCTTCGTCACCAAGACGCTCATGACCATCTGCCCCGG CACTGTGTTGTTGGTCTTCAGCGTCTCCT
Mouse #7: AGCATCGGGGCCCTTA-CCGCGTCACCTTCAACACACGCTTCGTCACCAAGACGCTCATGACCATCTGCCCCGGCCCACTGTGTTGTTGGTCTTCAGCGTCTCCT -1 bp, +2 bp (5)AGCATCGGGGCCCTTA---------------------------------------------------------- CACTGTGTTGTTGGTCTTCAGCGTCTCCT -58 bp (9)AGCATCGGGGCC-------------------------------------------------------------- ---TGTGTTGTTGGTCTTCAGCGTCTCCT -65 bp (2)

100

wt

120

Reference: GCCTGGGACACCGCAGGGCCCTCTTTGAAAAGCGTAAACGGCTCAGTGACTATGCGCTCATCTTTGGCATMouse #7: GCCTGGGACACCGCAGGGCCCTCTTTGAAAAGCGTAAACGGCTCAGTGACTATGCGCTCATCTTTGGCAT wild type (12)GCCTGGGACACCGCAGGGCCCTCTTTGAAAAGCGT-----TATCAGTGACTATGCGCTCATCTTTGGCAT -7 bp, +2 bp (2)GCCTGGGACACCGCAGGGCCCTCTTTGAAAAGCG------GCTCAGTGACTATGCGCTCATCTTTGGCAT -6 bp (1)1000

Kcnn2 (set 1)
target-1(set 1)

Kcnn3

120

Genome DNA/ Tbp(wild-type =100%)

target-3

target-2

300

D

Kcnn1 #7

Kcnn1

Genome DNA/ Tbp(wild-type =100%)A

Kcnn2 (set 1 for EEG/EMG, 2nd qPCR)

20
wt

1

2

3

4

5

6

7

8

0

9

wt

2

3

5

6

7

8

9

N
Kcnn2 #2
Kcnn2-1

Kcnn2 #3

target-1

Kcnn2-1

Reference: CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATC TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA
Mouse #2 CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGA-- TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA -2 bp (8)CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCGTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA +1 bp (6)target-2 target-3

Kcnn2-2, 3

Kcnn2-1
Reference:
Mouse #6

Kcnn2 #7

target-1
CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGC---------------------TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA

Kcnn2-1-21 bp (10)target-2 target-3

Kcnn2-2, 3

Reference: GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTT
Mouse #6 GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCA-----GGGAAATACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCA-----GGGAAA--CAGGTAACACAGGCTCCACTGTTTT

Kcnn2 #9Kcnn2-1
Reference:
Mouse #9

Reference:
Mouse #3

wild type (16)Reference:
Mouse #9

GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAACACAGGTAACACAGGCTCCACTGTTTT

wild type (4)1 bp-mutation (10)
1 bp-mutation (1)-2 bp (1)

Kcnn2-1

Reference:
Mouse #7

GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAA TACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAAATACAGGTAACACAGGCTCCACTGTTTT +1 bp (10)GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATTACAGGTAACACAGGCTCCACTGTTTT +1 bp (6)-2 bp (14)target-2 target-3-2 bp (15)-31 bp (1)GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATGCAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAA--CAGGTAACACAGGCTCCACTGTTTT

Reference:
Mouse #5

Kcnn2 #8

target-1

Figure S3. F. Tatsuki et al.

target-1
CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATC TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCA----- --CGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCGTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA-7 bp (8)
+1 bp (2)target-2 target-3

Kcnn2-2, 3

CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGA--TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA

target-2 target-3

Kcnn2-2, 3

Reference:
Mouse #5

GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAA TACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGG---- ------TAACACAGGCTCCACTGTTTT -10 bp (6)GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCA-----GGGAAAATACAGGTAACACAGGCTCCACTGTTTT -5 bp, +1 bp (10)target-1
CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGA--TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTT-------------------------------GCTGAGGATGAGGCGGCGGCGGCAGCAGA

Kcnn2-1

Reference:
Mouse #7

Kcnn2-2, 3-5 bp (4)-5 bp, -2 bp (12)Kcnn2 #5

target-1
CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGA--TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA -2 bp (14)target-2 target-3

Kcnn2-2, 3

Reference: GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTT
Mouse #2 GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTT

Kcnn2 #6

Reference:
Mouse #3

GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAA-TACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGG---TACAGGTAACACAGGCTCCACTGTTTT-1 bp (6)
-3 bp (6)GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCA-----GGGAAACACAGGTAACACAGGCTCCACTGTTTT-5 bp, 1 bp-mutation (2)target-1

Reference:
Mouse #8

CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATC TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGACGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGA-- TCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA -2 bp (7)
CGCCAGGTTGTTAGAATTGTTGTGCTCCGGCTTAGACACCACGATCCTCCGGGGCTGAGGATGAGGCGGCGGCGGCAGCAGA +1 bp (8)Reference:
Mouse #8

GTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAA TACAGGTAACACAGGCTCCACTGTTTTGTCTCTCCACGATCATCCTGCTTGGTCTGATCATCGTGTACCACGCCAGGGAAATTACAGGTAACACAGGCTCCACTGTTTT +1 bp (16)target-2 target-3

Kcnn2-2, 3

A
chr8(+)

B

Cacna1a84,450,000

84,550,000

C

Cacna1bchr2(-)

24,750,000

24,650,000

Cacna1cchr6(-)

Cacna1a119,100,000

118,900,000

118,700,000

Cacna1b
Cacna1c

gacacggaaccatacttcat (+)
ggaccaaatgtcagccgtac (+)
gaactgagtgatgccgttgt (-)
Cacna1a target sequence-1 Cacna1a target sequence-2 Cacna1a target sequence-3

gtgatggtgacaccgaatgc (+)
ggaatgccgtaatgtatcat (+)
gacatcttacatacttgggc (-)
Cacna1b target sequence-1 Cacna1b target sequence-2 Cacna1b target sequence-3
gttatcgaacgtgctcctac (-)
ggaattcggtgatgtatgat (+)
gaacatcagatactcgaagt (-)
Cacna1c target sequence-1 Cacna1c target sequence-2 Cacna1c target sequence-3

D
chr14(-)

E

Cacna1d30,400,000

30,250,000

30,100,000

F

Cacna1e154,700,000

chr1(-)

154,500,000

Cacna1fchrX(+)

7,610,000

7,620,000

7,630,000

Cacna1eCacna1f

Cacna1d
gtgaaacagttctgccggac (-)
gttcatacctccgtaatggc (+)
gtccacgtgggtgttgaagt (-)
Cacna1e target sequence-1 Cacna1e target sequence-2 Cacna1e target sequence-3

gacaatcatgatgtttccga (-)
ggtcgtgacacatctccatc (-)
Cacna1f target sequence-1 Cacna1f target sequence-2

gattcttagggatgtatcgg (-)
Cacna1f target sequence-3

ggtgatgtatgacggcatca (+)
ggaagggagtatttacgggt (-)
gtgttgtgccttgcagatac (+)
Cacna1d target sequence-1 Cacna1d target sequence-2 Cacna1d target sequence-3

G

H

Cacna1g (set 1)

chr11(-)

94,460,000

94,430,000

I

Cacna1h (set 1)

chr17(-)

25,420,000

25,390,000

Cacna1gK

136,090,000

136,110,000

Cacna1g (set 2)

chr11(-)

94,460,000

Genome DNA/ Tbp(wild-type =100%)
Genome DNA/ Tbp(wild-type =100%)

P

N

100

80

80

80

60

60

60

40

40

40

20

20

20

0

0

3

1

2

Q

Cacna1f

0

3

wt

100

80

80

60

60

60

40

40

40

20

20
1

2

3

4

Genome DNA/ Tbp(wild-type =100%)

5

6

8

9

0

10

5

6

7

8

9

10

6

7

8

20
wt

1

2

3

T

Cacna1g (set 2)

120

4

0

5

100

80

80

60

60

40

40

20

wt

1

2

3

4

5

Cacna1h (set 2)

120

100

0

Cacna1h #1

7

4

Cacna1i

100

wt

3

120

80

0

2

R

Cacna1h (set 1)

120

1

100

S

U

wt

Cacna1g (set 1)120

25,390,000

gttcgagcacattagcatgc (+)
gccgaaatgcctgcatcaac (+) gtacttcaaccgaggcatca (+)
Cacna1h target sequence-4 Cacna1h target sequence-5 Cacna1h target sequence-6

120

100

2

Cacna1h (set 2)
25,420,000

O

Cacna1e

120

100

1

L

gagctatgatcagcgatctt (+)
Cacna1i target sequence-3

Cacna1h

gatatgggttacagaccgtg (-)
gctggataccttgcctatgc (+)
ggatagcgatcatgattccc (-)
Cacna1g target sequence-4 Cacna1g target sequence-5 Cacna1g target sequence-6

Cacna1b

wt

gatctcctcatagcagtcgc (-)
Cacna1i target sequence-2

Cacna1g

gatgttcccgatggtgcgaa (-)
gggctactactacatataca (+)
gtcctcctcatttgagtcta (-)
Cacna1s target sequence-1 Cacna1s target sequence-2 Cacna1s target sequence-3

120

ggagatcatgtactacgtga (+)
Cacna1i target sequence-1

chr17(-)

94,430,000

Cacna1s

M

80,350,000
Cacna1i

gttcggccagaaatgctacc (+)
ggctccggcgtttaaccttt (-)
gcgacgcagcttgctactaa (-)
Cacna1h target sequence-1 Cacna1h target sequence-2 Cacna1h target sequence-3

Cacna1s
136,070,000

chr1(+)

80,300,000

Cacna1hggcaaagatgaagtcgtcga (-)
gcaataccacctgtgtcaac (+)
gggaatcatgatcgctatcc (+)
Cacna1g target sequence-1 Cacna1g target sequence-2 Cacna1g target sequence-3J

Cacna1i

chr15(+)

20
wt

1

target-1

Reference:
Mouse #1:

CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG
CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG wild type (14)Reference:
Mouse #1:

CAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCGCAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCG wild type (12)Reference:
Mouse #1:

TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGG
CATCATGGCAGCCATCCTCGTCAATACTCTTGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGG--------CAGCCATCCTCGTCAATACTCT -8 bp (10)TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGTCGTCTCTCATGGCAGCCATCCTCGTCAATACTCT -2 bp, +7 bp (5)

2

3

4

0

5

Cacna1h #2

wt

target-1

Reference:
Mouse #2:

CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG
CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG wild type (14)Reference:
Mouse #2:

CAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCGCAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCG wild type (14)Reference:
Mouse #2:

TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCA TCATGGCAGCCATCCTCGTCAATACTCTGAG
TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCA --------GCCATCCTCGTCAATACTCTGAG -8 bp (9)TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCAGATCATGGCAGCCATCCTCGTCAATACTCTGAG +2 bp (7)

1

2

3

4

Cacna1h #5

CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG
CTTAACGCACAGCCCGCAGGTGGTTCGAGCACATTAGCATGCTGGTCATCATGCTGAACTGCGTGACACTGG wild type (15)Reference:
Mouse #5:

CAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCGCAGGCTGAGGATGGGGGTGCCGGCCGAAATGCCTGCATCAACTGGAACCAGTACTACAACGTGTGCCGCTCG wild type (7)Reference:
Mouse #5:

TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCA
TCATGGCAGCCATCCTCGTCAATACTCTTGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCA
ATCATGGCAGCCATCCTCGTCAATACTCT +1 bp (7)TGCGTCGCATTGTAGACAGCAAGTACTTCAACCGAGGCAGTACTTCATGGCAGCCATCCTCGTCAATACTCT +5 bp (4)target-2

target-3

target-3

target-3

Figure S4. F. Tatsuki et al.

target-1

Reference:
Mouse #5:

target-2

target-2

5

B

26.7

0.05

0

0.00

0.00

.7

.3

26

.7

13

0

26

.3

13

0

.7

72

3&3 PJNJDC57BL/6J(mg/kg)

0.10

0.02

26

C MK-801

60

0.15

0.04

50

.3

24
36
48
Time (hr)

100

13

12

0.20

0.06

150

0

0

0

0.08

200

PSW

13.3

60

PWS

0

Sleep duration (min)C57BL/6J
i.p.

PCP(mg/kg)Sleep duration (min)

A

i.p.

2

0

0.100

200

0.075

100

0.050

50

0.025

0

0.000

24

36
48
Time (hr)

60

0.0

20
2
2
0.
0

12

0.1

20
22
0.
0

0

0.2
PSW

150

20
2
2
0.
0

20

60

PWS

0.2

Sleep duration (min)Sleep duration (min)00.í PJNJ

72

E

1

2

3

4

5

16 mg/l (n = 6)

6

7 1

2

3

4

5

6

32 mg/l (n = 6)

7 1

2

3

4

5

64 mg/l (n = 6)

6

7 1

2

3

4

5

6

96 mg/l (n = 6)

7 1

2

3

4

5

6

7

Time (Day)Sleep duration (min)F
0 mg/l (n = 6)60

16 mg/l (n = 6)

MK-801 concentration
32 mg/l (n = 6)
64 mg/l (n = 6)

96 mg/l (n = 6)

30
0

0

6

12

18

24 0

6

12

18

24 0

6

12

18

24 0

6

12

18

24 0

6

12

18

24

Time (hr)G

Sleep duration (min)Amplitude
0.6

600
400

0.4

200

0.2

0

0

16 32 64 96

0.0

0

16 32 64 96

PWS
0.04
0.03
0.02
0.01
0.00

0

16 32 64 96

0.íFRQFHQWUDWLRQ PJO

Figure S5. F. Tatsuki et al.

PSW
0.05
0.04
0.03
0.02
0.01
0.00

0

16 32 64 96

Sleep duration (min)MK-801 concentration
0 mg/l (n = 6)

60

0

A

CT10

CT16

B

CT22

MK-801 (-)

Sleep duration (min)CT4

MK-801 (-)

60
45
30
15

P-values

Sleep duration (min)MK-801 (+)

0

Magnified
image

Brain slice

24

60

0.05
0.01
0.001
<0.0001

45
30
15
0

6

12
ZT

18

24

MK-801 (-)

x103
100
80
Cells

60
40
y = 0.0123x
R2 = 0.95

20
0Purple cluster
CT4

CT10

CT16

0

4
Intensity

8
x106

CT22

MK-801 (-)

MK-801(-)
MK-801(+)

Cells / mm3120

MK-801 (+)

F

80
40
0

CT4 CT10 CT16 CT22

G

MK-801 (-)

MK-801 (+)

c-Fos
DAPI

c-Fos
DAPI

Nr2b
DAPI

Nr2b
DAPI

H

18

D

Hyper-activatedcells

MK-801 (+)

E

12
ZT

MK-801 (+)

0

C

6

I

MK-801 (+), cerebral cortex

MK-801 (+), cerebral cortex

L1

L1

L2/3

L2/3

L4

L4

L5

L5

L6

c-Fos
Vglut1/2
DAPI

L6

c-Fos
Gad1/2
DAPI

Figure S6. F. Tatsuki et al.

A

B

Atp2b1chr10(+)

98,950,000

99,000,000

C

Atp2b2chr6(+)

113,800,000

ggatgcattacggaaaatac(+)
Atp2b1 target sequence-2

D

ggctggaattactgtgcgca(+)
Atp2b1 target sequence-3

ggacgtgacacttatcatcc(-)
Atp2b2 target sequence-1

gtagtggacgtcaccgacat(+)
Atp2b2 target sequence-2

E

Atp2b4chr1(+)

133,750,000

gcaaagagaagcagttccgg(-)
Atp2b4 target sequence-2

G
126,790,000

60,970,000

F

gtagggactcacgtgatgga(-)
Atp2b4 target sequence-3

H

I

Camk2g
20,760,000

chr14(+)

chrX(+)

catgatgcatcgccaagaga(-)
caacgcccggagaaaactga(-) aaccctcacgtccacgtgat(-)
Camk2g target sequence-1 Camk2g target sequence-2 Camk2g target sequence-3

K

Genome DNA/ Tbp(wild-type =100%)

Atp2b3 (set 1, 1st qPCR)

ggcagaactacgaagcctca(+)
Atp2b3 target sequence-4

Atp2b3 (set 1, 2nd qPCR)

N

100

100

100

100

80

80

80

80

60

60

60

60

40

40

40

40

20

20

20

20

0

0

0

2

3

wt

1

2

3

4

5

6

7

8

9

120

100

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

2

3

4

5

6

7

8

9

wt

1

3

wt

12

0

11

ggcttccgcctctttagcaa(+)
Atp2b3 target sequence-6

wt

1

2

3

4

5

6

7

O

Atp2b3 (set 2 in UT)

120

1

11

M

Atp2b3(set 1 in UT, 2nd qPCR)

120

wt

10

gccttggatgagcacaccat(-)
Atp2b3 target sequence-5

Atp2b4 (set 1)120

Atp2b3 (set 1 in UT, 1st qPCR)

Genome DNA/ Tbp(wild-type =100%)

Atp2b3120

173,550,000

Camk2gP

Camk2a (set2)
chr18(+)

60,970,000

60,980,000
Camk2actgtttgcgcactgtaaggt(-)
agcctggctaactcacgtgt(-)
aaccctcacatccacctgat(+)
Camk2a target sequence-4 Camk2a target sequence-5 Camk2a target sequence-6

Camk2b (set2)
chr11(+)

6,000,000
Camk2b

wt

1

2

3

4

5

6

7

8

9

gagtaacggtgtcccactca(+) ctcacctgagaaattccgtg(-)
cgcctcacacagtacatcga(+)
Camk2b target sequence-4 Camk2b target sequence-5 Camk2b target sequence-6

Q
Camk2a (set 1, 1st qPCR)

Camk2a (set 1, 2nd qPCR)

120
Genome DNA/ Tbp(wild-type =100%)

Atp2b3 (set2)

20,790,000

120

L

Camk2a #4

120

100

100

80

80

60

60

40

40

20

20

0

0

wt

1

2

3

4

5

6

Camk2a #6

target-1

Reference: ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAGTGCGCAAACAGGAAATTATCAAAGTGACAGAGCAG
Mouse #272: ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAGTGCGCA--CAGGAAATTATCAAAGTGACAGAGCAG -2 bp (10)ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAG-----------GAAATTATCAAAGTGACAGAGCAG -11 bp (6)target-1

Reference: ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAGTGCGCAAACAGGAAATTATCAAAGTGACAGAGCAG
Mouse #280: ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAGTGCGCAAAACAGGAAATTATCAAAGTGACAGAGCAG +1 bp (8)ATCACTGTCGTTATTTGTGTCCCCTCCTACCTTACAG------ -----GAAATTATCAAAGTGACAGAGCAG -11 bp (5)target-2

target-2Reference: GGTGACGAGTCAGCCTGCATCGCCTATATCCGCATCACTCAGTACCTGGATGCAGGCGGCATACCCCGCACG
Mouse #272: ---------(298base)------------------------------------------ATACCCCGCACG -298 bp (1)Reference: GGTGACGAGTCAGCCTGCATCGCCTATATCCGCATCACTCAGTACCTGGATGCAGGCGGCATACCCCGCACG
Mouse #280: ---------(301base)-------ATATCCGCATCACTCAGTACCTGGATGCAGGCGGCATACCCCGCACG

target-3
wt

1

2

3

4

-301 bp (1)target-3

Reference: AGATCGTCCACTTCCACAGATCTGGGGCGCCCTCCGTCCTGCCGCAGTAAGGATTCTTTCTTCCTGAATCT
Mouse #272: AGATCGTCCACTTCCACAGATCTGGGGCGCC------------GCAGTAAGGATTCTTTCTTCCTGAATCT

Reference: AGATCGTCCACTTCCACAGATCTGGGGCGCCCTCCGTCCTGCCGCAGTAAGGATTCTTTCTTCCTGAATCT
Mouse #280: AGATCGTCCACTTCCACAGATCTGGGGCGCC------------GCAGTAAGGATTCTTTCTTCCTGAATCT-12 bp (1)-12 bp (1)

6

S

R

Camk2b (set 1, 2nd qPCR)

Camk2b (set 1, 1st qPCR)

Camk2a (set 2, 2nd qPCR)

Camk2a (set 2, 1st qPCR)

120

120

120

100

100

100

100

80

80

80

80

60

60

60

60

40

40

40

40

20

20

20

20

0

0

0

120
Genome DNA/ Tbp(wild-type =100%)

ggtgatcctgtatatcctgc(-)
ggaaatccatcccttcgacc(+)
ggccatcgatgtactgtgtg(+)
Camk2b target sequence-1 Camk2b target sequence-2 Camk2b target sequence-3

120

wt

6,000,000
C
Camk2bctaccttacagtgcgcaaac(+) gatgcggatataggcgatgc(-)
gaatccttactgcggcagga(-)
Camk2a target sequence-1 Camk2a target sequence-2 Camk2a target sequence-3

atcccaccagcaagatgtag(-)
gatacgtttggcagggttga(-)
ctcagaggctgtgatacgtt(-)
Camk2d target sequence-1 Camk2d target sequence-2 Camk2d target sequence-3

wt

1

2

T
Genome DNA/ Tbp(wild-type =100%)

Camk2bCamk2a126,800,000

Atp2b2 (set 1)

gatgcccacaacagctatgc(-)
Atp2b3 target sequence-3

chr11(+)

60,980,000

Camk2dJgatgagagtcacgtcctgca(-) gttatccgcatgcctgatgg(+)
Atp2b3 target sequence-1 Atp2b3 target sequence-2

Camk2achr18(+)

Camk2dchr3(+)

Atp2b3

ggaggattcgcaacgagaag(-)
Atp2b2 target sequence-3

Atp2b4ggcaaaatgacgaatccacc(-)
Atp2b4 target sequence-1

73,550,000

Atp2b2

Atp2b1gctatacgcaactgagttgt(-)
Atp2b1 target sequence-1

Atp2b3 (set1)chrX(+)

113,850,000

3

4

5

6

7

8

9

Camk2b (set 2, 1st qPCR)

Camk2b(set 2, 2nd qPCR)

wt

2

9

U

wt

1

2

V

Camk2d (set 1)

3

4

0

5

Camk2g (set 1, 1st qPCR)

120

120

100

100

100

100

80

80

80

80

60

60

60

60

60

40

40

40

40

40

20

20

20

20

20

0

0

0

0

120

100
80

wt

1

2

3

4

5

6

7

8

wt

3

6

wt

1

2

3

4

Figure S7. F. Tatsuki et al.

5

wt

1

2

3

4

5

2

4

Camk2g (set 1, 2nd qPCR)

120

120

wt

0

wt

1

2

3

5