Neuroscience Research 118 (2017) 48–55

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Neuroscience Research
journal homepage: www.elsevier.com/locate/neures

Review article

Ca2+ -dependent hyperpolarization hypothesis for mammalian sleep
Fumiya Tatsuki a,c , Koji L. Ode a,b , Hiroki R. Ueda a,b,∗a

Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japanc
The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8865, Japanba r t i c l e

i n f oArticle history:
Received 22 February 2017
Received in revised form 24 March 2017
Accepted 27 March 2017
Available online 20 April 2017a b s t r a c t
The detailed molecular mechanisms underlying the regulation of sleep/wake cycles in mammals are
elusive. In this regulation, at least two mechanisms with fast and slow time scales are involved. In thefaster time scale, a state of non-rapid-eye-movement (NREM) sleep can be microscopically characterized by the millisecond-to-second-order electrical behavior of neurons, namely slow-wave oscillations
described by electrophysiology. In the slower time scale, the total duration of NREM sleep is homeostatically regulated by sleep pressure (the need for sleep), which is usually sustained for hours or even days
and can be macroscopically described by electroencephalogram (EEG). The longer dynamics of sleep
regulation are often explained by the accumulation of sleep-inducing substances (SISs). However, we
still do not have a concrete model to connect fast, microscopic dynamics and slow, macroscopic dynamics. In this review, we introduce a recent Ca2+ -dependent hyperpolarization hypothesis, in which theCa2+ -dependent hyperpolarization of cortical-membrane potential induces slow-wave oscillation. Slow
dynamics of the Ca2+ -dependent hyperpolarization pathway might be regulated by recently identiﬁed
sleep-promoting kinases as well as classical SISs. Therefore, cortical Ca2+ -dependent hyperpolarization
may be a fundamental mechanism connecting fast neural activity to the slow dynamics of sleep pressure.
© 2017 Published by Elsevier Ireland Ltd.

Contents
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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Slow dynamics of NREM sleep: process S and sleep-induced substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Fast dynamics of NREM sleep: electrophysiological neuronal activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Ca2+ -dependent hyperpolarization as a basis for the fast electrophysiological dynamics of cortical neurons in NREM sleep . . . . . . . . . . . . . . . . . . . . . . . 51
Post-translational modiﬁcation of the Ca2+ -dependent hyperpolarization pathway can connect the fast and slow dynamics of sleep regulation . 52
Psychiatric disorders and the Ca2+ -dependent hyperpolarization hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Author contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

1. Introduction
The sleep-wake cycle is one of the most fundamental phenomena governing the global state of an animal’s brain. The functionof sleep remains a mystery, but it apparently involves more than

∗ Corresponding author at: Department of Systems Pharmacology, Graduate
School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.
E-mail address: uedah-tky@umin.ac.jp (H.R. Ueda).
http://dx.doi.org/10.1016/j.neures.2017.03.012
0168-0102/© 2017 Published by Elsevier Ireland Ltd.

a simple resting of the body; instead, the sleep state is thought toactively guarantee fundamental brain functions such as memory
consolidation (Stickgold, 2005). Sleep also appears to play a vitalrole in other parts of the body, such as the immune system (Bryant
et al., 2004). The fundamental physiological importance of sleep
would require a homeostatic regulation mechanism to ensure thatan organism gets sufﬁcient sleep.
Humans need around eight hours of sleep per day on average(Roenneberg et al., 2012; Walch et al., 2016). Sleep loss leads toincreased sleep pressure on the next round of the sleep cycle, indicating that the total sleep amount is regulated homeostatically.

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49

Fig. 1. Regulation of mammalian sleep across a wide range of time scales.
Mammalian sleep is regulated through dynamic, microscopic neuronal electrophysiological activity that occurs on a time scale of milliseconds. The collective activity ofcortical neurons during sleep results in slow-wave oscillation in EEG with a frequency around one second. From a macroscopic point of view, the sleep-related behaviors ofmammals (e.g., transition from sleep to wake phase) occur on a time scale of minutes to hours. Furthermore, the effect of sleep deprivation lasts for days. One of the mostchallenging topics of sleep research is constructing a reliable model that accounts for the regulation of the multiple time scales involved in sleep. The upper-right panel
shows the conceptual behavior of “process S” as proposed by the two-process model. The lower-right panel shows EEG patterns of mice when sleeping or awake.

In other words, the core of the sleep-wake cycle involves a system with a negative feedback loop in which a prolonged awake
period inhibits wakefulness and promotes sleep. With this negative feedback loop, a system output (e.g., sleep duration) can bestable around a ﬁxed set point. In addition, the required sleep duration varies widely among species (Joiner, 2016; McNamara et al.,2008), implying that the set point for the homeostatic regulationof sleep duration is genetically determined. Therefore, one of thegoals in a system-level understanding of sleep-wake cycles is toelucidate the molecular components and interactions underlying
the homeostatic regulation of sleep duration.
The time scale of the system’s dynamics is an important starting
point for understanding the system’s architecture. The transductionof electrical impulses in neurons occurs on the order of milliseconds. The molecular mechanisms underlying this information
processing involve ion ﬂuxes and changes in ion-channel conformation, whereas processes occurring on a longer time scale, suchas transcription, translation, degradation, and protein modiﬁcation, are not directly involved in this electrical system. On theother hand, these longer time-scale processes play more dominant roles in slower biological phenomena, such as learning andmemory (Alvarez-Castelao and Schuman, 2015; Bramham, 2008;
Bramham and Wells, 2007; Cajigas et al., 2010). Interestingly, thetime scales associated with sleep regulation span from milliseconds in the faster dynamics of neural activity to hours/days in theslower dynamics of sleep pressure. Therefore, connecting the wide
gap of time scales in sleep regulation is a challenge for sleep studies
from both a biochemical (Krueger, 2008; Krueger et al., 2008) andtheoretical viewpoint (Olbrich et al., 2011) (Fig. 1). In this review,
we introduce the recently proposed “Ca2+ -dependent hyperpolarization hypothesis,” which proposes roles for neuronal Ca2+ inregulating membrane potential during sleep, and discuss how thishypothesis contributes to our understanding of the feedback mechanism underlying the homeostatic regulation of mammalian sleep.
2. Slow dynamics of NREM sleep: process S andsleep-induced substances
Sleep pressure has a long time scale (hours to days), which
provides an intuitive clue to understanding sleep-wake dynamics. In fact, the pioneering study of the molecular substances of
sleepiness focused on the relatively slow dynamics of the sleepwake cycle and the recovery process from sleep deprivation. In
1909, Kuniomi Ishimori found that brain extracts from sleepdeprived dogs dramatically induced sleep when injected into
dogs without sleep deprivation (Ishimori, 1909), implying thatsleep-promoting substances exist that accumulate during periods of (prolonged) wakefulness and promote sleep. The expected
dynamics of such sleep-inducing substances (SISs; also called
sleep-regulatory substances, SRSs) was designated “process S” in alandmark two-process model describing the relationship between
sleep pressure and circadian clocks (Borbely, 1982) (Fig. 1): process
S presumably increases during awake time and decreases during
sleep time. Increasing process S will prolong sleep duration.
Several candidate SIS molecules have been found since Ishimori’s discovery in 1909, including adenosine, nitric oxide,
prostaglandin D, tumor-necrosis factor, interleukin 1, and growthhormone-releasing hormone (Clinton et al., 2011; Krueger et al.,2008; Obal and Krueger, 2003). There is growing cellular and molecular evidence of adenosine’s role in promoting sleep. For example,
the extracellular adenosine concentration in the basal forebrain
was increased during spontaneous wakefulness compared to dur-

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Fig. 2. The averaged-neuron (AN) model predicts that a Ca2+ -dependent hyperpolarization pathway plays a pivotal role in non-rapid-eye-movement (NREM) sleep.
(A) Diagram showing the AN model. Light red: extrinsic ion channels thought to work mainly at synapses. Light blue: intrinsic ion channels and Ca2+ -pumps/exchangers.
Compared to the neuron-network (NN) model, which explicitly describes the synaptic connections of excitatory and inhibitory neurons, the AN model assumes that all
excitatory and inhibitory neurons are connected to each other. Thus, the model can be described and simulated as if one neuron connected to itself with both excitatory andinhibitory input.
(B) Representative ﬁring patterns (slow-wave oscillation) during NREM sleep and the intracellular Ca2+ concentration generated from the AN model. Important channels/pumps for inducing or maintaining the silent hyperpolarized state can be searched by changing the parameter corresponding to the activity of each channel/pump. In
this ﬁgure, reducing gKCa (i.e. reducing the activity of Ca2+ -dependent K+ channels) resulted in the loss of hyperpolarization.

ing NREM sleep in cats (Porkka-Heiskanen et al., 1997). Adenosine’s
effect on sleep became evident in mice with a conditional knockout of the adenosine A1 receptor in the forebrain and hindbrain;
these mice showed impaired enhancement of slow-wave activity
upon sleep deprivation (Bjorness et al., 2009). Another adenosine
receptor (A2) was shown to be responsible for the effect of caffeine,
which is used to promote wakefulness in daily life (Huang et al.,2005). A series of studies by Haydon and colleagues demonstrated
that adenosine induces sleep in animals through gliotransmission(Florian et al., 2011; Halassa et al., 2009; Marpegan et al., 2011;
Nadjar et al., 2013; Schmitt et al., 2012). These studies suggestthat the adenosine signaling pathway functions as a sleep feedback
pathway that responds to sleep loss.
However, adenosine may be not involved in the mechanismthat determines the set point of amount of sleep; rather, it induces
sleep in response to an abnormal sleep status resulting from inﬂammation or forced sleep deprivation. The basal sleep duration (i.e.,under natural conditions) does not differ signiﬁcantly between
adenosine-receptor knockout and wildtype mice (Bjorness et al.,2009; Huang et al., 2005; Stenberg et al., 2003). A similar rationale may be applicable to other SISs: in many cases, knocking out
their synthesis, receptors, or signal-transduction pathways has anonsigniﬁcant or only marginally signiﬁcant effect on the wholeday basal sleep duration, at least for NREM duration (Chen et al.,

2003; Eguchi et al., 2002; Fang et al., 1997; Kaushal et al., 2012;
Obal and Krueger, 2003). Collectively, these results suggest that
although candidate SISs engage the response to sleep loss, othermechanisms may be responsible for ensuring the required sleep
amount.
3. Fast dynamics of NREM sleep: electrophysiologicalneuronal activity
Studies focusing on neuronal activity during sleep, which occurs
in the order of milliseconds, provide insight on the electrophysiological regulation of sleep. The collective activity of cortical neurons
during NREM sleep is characterized by the EEG pattern (Walter,1937). During NREM sleep, an EEG displays high-amplitude, lowfrequency ﬂuctuations (slow-wave oscillation). This pattern isgenerated by synchronized slow oscillations of the cortical neuron’s membrane potential, in which a depolarized spiking phaseand hyperpolarized silent phase occur repetitively with a frequency
that corresponds well with that in the EEG pattern (Steriade andMcCarley, 2005). Hereafter, we refer to this ﬁring pattern of cortical neurons as slow-wave oscillation (see Fig. 2B). The EEG delta
power (0.5–4 Hz), which closely matches the major frequency ofslow-wave oscillation, is a useful metric to indicate sleep pressure(Borbely et al., 1981; Nakazawa et al., 1978; Webb and Agnew,

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1971) and is conserved across different phyla of animals (SheinIdelson et al., 2016), indicating that the molecular mechanisms
underlying the slow-wave oscillation of cortical neurons engage
directly in the homeostatic regulation of NREM sleep, and that thisoscillation is not simply the trivial output of resting neurons.
There is growing evidence that slow-wave oscillation canemerge from the cortical neuronal assembly: the slow-wave
ﬁring pattern was observed in cortical brain slices cultured
in vitro without electrical/chemical stimulation (Sanchez-Vives andMcCormick, 2000). This slow-wave pattern was also observed in
an isolated cortical slab maintained in the intact brain of an animal
kept under anesthesia (Timofeev et al., 2000). These studies indicatethat the cortical region of the brain contains all of the components
necessary to elicit the slow-wave ﬁring pattern, and that synaptic inputs from other regions of the brain are not a prerequisite. In
addition, the local cortical region can elicit sleep-related neuronal
activity in vivo, given that the EEG delta power (0.5–4 Hz), whichclosely matches the major frequency of slow-wave oscillation, canbe heterogeneously distributed in the cortex. This distribution,
which appears to be determined in an activity-dependent mannerin each cortical area and is thus called local sleep, has been observedin various organisms from rodents (Funk et al., 2016; Vyazovskiy
et al., 2011) to humans (Huber et al., 2006; Muto et al., 2016). A relevant phenomenon was recently observed in ﬂies: a prolonged waketime inhomogeneously reduces neuronal activity in the Kenyoncell population in the ﬂy mushroom body, and the cells respondinconsistently to repeated exposure to the same stimulus (Bushey
et al., 2015). These results indicate that small and local neuronal
circuits may be responsible for inducing the slow-wave oscillation.
Sleep pressure might also be stored locally in the cortical neurons.
It should be noted that the actual architecture of sleep requires
complex neural circuits. The thalamocortical circuit has been implicated as an underlying mechanism for gamma oscillation and thesleep spindle (Traub et al., 2005; Zagha and McCormick, 2014), andcortical-circuit structures are important for synchrony between
neurons in different areas of the brain (Amzica and Steriade, 1995;
Chen et al., 2012; Hill and Tononi, 2005). Nonetheless, it is useful toinvestigate the mechanism of slow-wave oscillation by focusing ona small system that is comparable to local cortical-neuronal assemblies and for which the process required for slow-wave oscillation
has minimum complexity.
Computational models are a powerful tool for ﬁnding explanations for various neuronal electrophysiological behaviors based on
the fast/microscopic properties of ion channels/pumps that havebeen characterized in experiments. Various computational models have attempted to recapitulate slow-wave oscillation based oninteractive populations of several types of neurons (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), here referred toas neuron-network models (NN models). Regarding the appearanceof the hyperpolarized silent phase, a remarkable property of slowwave oscillation, three types of mechanisms have been proposed
for the transition from ﬁring phase to silent phase. The ﬁrst is anenhancement of Ca2+ - and Na+ -dependent K+ channels in pyramidal neurons (Bazhenov et al., 2002; Compte et al., 2003; Hill andTononi, 2005; Sanchez-Vives et al., 2010; Timofeev et al., 2000).
This mechanism does not directly involve synaptic inhibition, butrather depends on the activation cascade of cell-intrinsic channels.
This prediction is consistent with experimental observations of a
relationship between the induction of the silent phase and a seriesof K+ currents, most probably Ca2+ -dependent K+ currents (Steriade
and McCarley, 2005; Timofeev et al., 2001). The second mechanism
is a passive inhibition of the excitatory synaptic current at pyramidal neurons due to a limited source of synaptic transmission
and the auto-inhibition of ion channels (Bazhenov et al., 2002; Hill
and Tononi, 2005). The third mechanism is an active repression of51

pyramidal neurons through inhibitory synaptic input by interneurons (Timofeev et al., 2000). The role of GABAergic neurons in
inducing the hyperpolarized silent phase is questionable, because
slow-wave oscillation is resilient to the inhibition of GABAA receptor activity both in vivo (Steriade et al., 2001) and in vitro (Chen et al.,2012; Sanchez-Vives et al., 2010). Instead, interneuron-mediatedneuronal circuits may be important for the synchronous activity among cortical cells during slow-wave oscillation (Chen et al.,2012). Thus, the question is which mechanism is most inﬂuential
in inducing slow-wave oscillation and has the greatest impact on
the organism’s sleep state.
NN models with varying complexities and settings produce
different predictions. Although complex and simple models cancomplement each other, the approaches differ as to purpose andwhat we hope to learn. The complex model has the advantage whentrying to precisely reproduce behaviors observed in experiments.
For example, a model with a cortical multilayer structure and thalamocortical circuit has the potential to reproduce not only theslow-wave oscillation of each neuron, but also the collective propagation of neuronal activity that can be compared to EEG patterns(Hill and Tononi, 2005). However, the drawback of complex models is that the huge number of possible combinations of parameters
obscures how each component contributes to the system behavior
of interest. Thus, it is difﬁcult to comprehensively identify the number and type of parameter sets (and the common critical pathway)that can generate slow-wave oscillation. Such a comprehensive
parameter survey requires a reduction in computational complexity of NN models, which means deciding which components should
be kept and which should be omitted or simpliﬁed. To investigate
the molecular details of slow-wave oscillation, equations describing the molecular behavior of neurons should be preserved (i.e.,
parameters that account for various ion channels and pumps). On
the other hand, although an in vitro brain slab requires a minimum
number of interconnected neurons to exhibit slow-wave oscillation(Timofeev et al., 2000), previous modeling studies suggest that a
precise circuit structure may not be necessary for slow-wave oscillation; a randomly connected neuronal assembly can also produce
this ﬁring pattern (Compte et al., 2003). Therefore, it is not necessary to explicitly simulate the behavior of thousands of neurons.

4. Ca2+ -dependent hyperpolarization as a basis for the fastelectrophysiological dynamics of cortical neurons in NREM
sleep
We created a simpliﬁed NN model, called an averaged-neuronmodel (AN model) (Tatsuki et al., 2016), in which we applied meanﬁeld approximation to an NN model of a population of neurons(Fig. 2A). In other words, the effect of all other neurons on any given
neuron is approximated by a single averaged neuron, thus reducing a population of neurons to one averaged neuron. The averaged
neuron can interact with itself directly or indirectly through excitatory (Na+ currents mediated by AMPA receptors, and Ca2+ currents
mediated by NMDA receptors) or inhibitory (Cl− currents mediatedby GABAA receptors) synaptic currents. The intrinsic currents incorporate depolarizing Na+ and Ca2+ currents and hyperpolarizing
K+ currents. The depolarizing Na+ and Ca2+ currents are mediated by voltage-gated or persistent Na+ channels and voltage-gated
Ca2+ channels, respectively. On the other hand, K+ currents are
mediated by several types of K+ channels, including voltage-gated,
leak, fast A-type, inwardly rectifying, slowly inactivating, and Ca2+ dependent K+ channels (Fig. 2A). In addition, intracellular Ca2+ istransported by Ca2+ pumps/exchangers. Although much simpler
than current NN models, the AN model recapitulates the slow-wave
oscillation with alternating ﬁring and silent phases (Fig. 2B).

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Since this model has been simpliﬁed by mean-ﬁeld approximation, it requires much less computational power for simulations
compared to existing NN models. Over 10,000,000 random parameter sets were generated under each condition. Through a broad andcomprehensive parameter search, the AN model provides the following three predictions: 1) Downregulating the Ca2+ -dependent
hyperpolarization pathway weakens the hyperpolarized silent
phase. 2) Voltage-gated Ca2+ channels and NMDA receptors play
redundant roles in the inﬂux of Ca2+ into the neuron to generate slow-wave oscillation. 3) Impaired GABAA receptor function
only marginally affects the appearance of slow-wave oscillation.
These predictions are consistent with several NN models as wellas previous computational studies of the electric bursting of various types of cells (Arbib, 2003; Izhikevich, 2007). Collectively, theAN model predicts that slow-wave oscillation is established by thecell-intrinsic induction of the silent phase by a Ca2+ -dependent
hyperpolarization pathway rather than by synaptic inhibition via
the GABAA receptor.
The validity of this prediction is supported by the sleep
phenotype of various gene-knockout mice. Interestingly, several
studies show that knocking out genes that activate the Ca2+ dependent hyperpolarization pathway shortens sleep duration. A
short-sleeper phenotype was reported for mice with a knockoutof the NMDA receptor Nr3a (Sunagawa et al., 2016) or of thevoltage-gated Ca2+ channel Cacna1g (Anderson et al., 2005; Leeet al., 2004). These channels mediate the inﬂow of Ca2+ , and theCa2+ then activates a Ca2+ -dependent K+ channel. Knocking out
Kcnn2 (a small-conductance Ca2+ -dependent K+ channel) tended
to decrease sleep duration, but the decrease was not signiﬁcant(Cueni et al., 2008). Taken collectively, these phenotypes and ANmodel predictions suggest a straightforward relationship between
animals’ sleep phenotypes and the cortical capacity to elicit slowwave oscillation: the more efﬁciently cortical neurons induce theCa2+ -dependent hyperpolarization pathway, the longer the animal
sleeps.
To evaluate this relationship comprehensively, we created 21lines of knockout mice to cover all genes in the mouse genome that
encode Ca2+ -dependent K+ channels, the ␣1 subunit of voltagedependent Ca2+ channels, and plasma-membrane Ca2+ ATPases(PMCAs) (Tatsuki et al., 2016), and further investigated the sleep
phenotype of mice treated with an NMDA receptor antagonist toassess the sleep-related function of the main NMDA receptor subunits, the knockout of which results in embryonic lethality (Forrest
et al., 1994; Li et al., 1994). The phenotypes of these mice wereconsistent with the predictions based on the AN model: sleep
duration was signiﬁcantly reduced by impairing Ca2+ -dependent
K+ channels, the ␣1 subunit of voltage-dependent Ca2+ channels, or the NMDA receptor, —any of which would reduce theability to induce Ca2+ -dependent hyperpolarization. On the other
hand, sleep duration increased signiﬁcantly in mice with impaired
PMCA; thus, in PMCA-mutant mice, the Ca2+ efﬂux rate would
decrease to maintain Ca2+ -dependent hyperpolarization. Thus,
genetic and pharmacological perturbations of the Ca2+ -dependent
hyperpolarization pathway, which describes the fast dynamics ofelectrophysiological properties of cortical neurons during sleep,
affect sleep duration in mice. Importantly, repressing or activating the Ca2+ -dependent hyperpolarization pathway decreases orincreases sleep duration, respectively. This bidirectional control ofsleep duration indicates that it is possible for the Ca2+ -dependent
hyperpolarization pathway to be targeted by the slow dynamics of process S. Therefore, the Ca2+ -dependent hyperpolarization
hypothesis may connect fast and slow time scales between electrophysiological neuronal states and sleep pressure (“process S”). The
direct test of the Ca2+ -dependent hyperpolarization hypothesis willlie in recording the slow-wave oscillation in cortical-membranepotential in vitro or in vivo, and testing for its dependency on Ca2+
(Cox et al., 2016).

5. Post-translational modiﬁcation of the Ca2+ -dependent
hyperpolarization pathway can connect the fast and slow
dynamics of sleep regulation
If the Ca2+ -dependent hyperpolarization pathway regulates
mammalian sleep, it should be homeostatically regulated.
In other words, the excitation of (cortical) neurons should
directly or indirectly enhance Ca2+ -dependent hyperpolarization.
Calcium/calmodulin-dependent protein kinase type II (CaMKII) isa strong candidate for sensing neuronal activity and transmitting
the information to regulate the efﬁciency of Ca2+ -dependent hyperpolarization. CaMKII, which is activated by the Ca2+ signal evokedby neuronal excitation, plays a central role in regulating slower
neuron/brain physiologies such as learning and memory (Lisman
et al., 2012; Mayford et al., 1996) and circadian rhythm (Kon et al.,2014). Notably, CaMKII is also a well-known protein family thatcan bind several channels involved in the Ca2+ -dependent hyperpolarization pathway in response to Ca2+ inﬂux, such as NMDA
receptors or L-type Ca2+ channels (Leonard et al., 1999; Rose et al.,2009). Indeed, knocking out either the alpha or beta Camk2 isoform in mice signiﬁcantly decreased sleep duration (Tatsuki et al.,2016), indicating that CaMKII is, in principle, a sleep-promoting
kinase. Thus, intracellular Ca2+ signaling may play central roles not
only in regulating membrane potential during slow-wave oscillation, but also in the homeostatic regulations connecting neuronal
excitability and sleep pressure. The Ca2+ -dependent hyperpolarization pathway may integrate SIS signals: for example, growth
hormone-releasing hormone (GHRH) stimulation increased intracellular Ca2+ levels and delta-wave oscillation during sleep (Liaoet al., 2010).
Recent studies have identiﬁed other sleep-promoting kinases.
A forward genetics study revealed that a gain-of-function mousemutant of salt-inducible kinase 3 (Sik3) has a remarkably long sleep
duration (Funato et al., 2016). In addition, a focused screening using
an in vitro neuron culture mimicking the expression patterns ofgenes associated with the sleep-wake cycle (Hinard et al., 2012)
found that mitogen-activated protein kinases (MAPK), ERK1/2 are
sleep-inducing kinases (Mikhail et al., 2017). Although it is notclear how these kinases are regulated during the sleep-wake cycle,
they appear to be involved in homeostatic processes. Sleep deprivation upregulates kinase-activated phosphorylation in the case ofboth SIK3 and ERK1/2 (Funato et al., 2016; Hinard et al., 2012).
Several SISs are also plausible factors for activating these sleeppromoting kinases upon prolonged sleep, since the downstream
pathways of SIS stimulation are often coupled with the activation of
the MAPK signaling cascade—MAPK is involved in the TNF signalingcascade, for instance (Brenner et al., 2015)—or with the activationof G protein-coupled receptors (GPCRs) that regulate SIK3 function through the cAMP-PKA signaling pathway (Funato et al., 2016;
Katoh et al., 2006).
In general, having a kinase cascade in a signal-transduction
pathway can create a time delay between the signal input andoutput (Goldbeter, 1991; Kholodenko, 2006). Thus, it would
be interesting to investigate whether the above-mentioned
sleep-promoting kinases interact with each other to form a sleeppromoting kinase cascade that can bridge the fast dynamics ofelectrophysiological neuronal activity and the slower dynamics of
sleep homeostasis. Furthermore, identifying the kinases and potential substrates that affect animals’ sleep duration may provide clues
to how different animals adjusted their sleep duration through thecourse of evolution. In circadian clocks, part of the natural varianceon the time scale of the circadian period (nearly 24 h) is caused

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53

NREM sleep

Wake

(mV)

Membrane potential

30-70
5 (sec)0

Ca2+ inflow

5 (sec)0

Ca2+ outflow

Ca2+ inflow

Ca2+ outflow

Ca2+-dependent
K+ channel
Voltage-gated
Ca2+ channel

NMDA receptor

K+

Ca2+-dependent
K+ channel

Extracellular

CaMKIIα/β?
SIK3?
ERK1/2?

Intracellular?

Ca2+-pump/exchanger

Ca2+

Voltage-gated
Ca2+ channel

NMDA receptor

K+

Ca2+-pump/exchanger
Extracellular

Intracellular

Ca2+

Fig. 3. Schematic diagram of the Ca2+ -dependent hyperpolarization pathway.
The efﬂux of K+ by Ca2+ -dependent K+ channels generates hyperpolarized states that characterize slow-wave oscillation. Genetic perturbations of factors corresponding toCa2+ inﬂow into the neurons reduce the sleep duration per day. On the other hand, genetic perturbations of factors corresponding to Ca2+ outﬂow from the neurons increase
the sleep duration per day. This Ca2+ -dependent hyperpolarization pathway may be the target of sleep-promoting kinases, which are plausible factors for connecting fast,
microscopic membrane potential to the slower dynamics of the sleep-wake cycle. Extracellular conditions, including ion concentrations and SIS levels, may be regulated byboth neurons and non-neuronal cells.

by single amino-acid substitutions in kinases and their direct substrates (Lowrey et al., 2000; Toh et al., 2001; Xu et al., 2005) that
do not change the basic architecture of the circadian molecular network. A similar story might explain the genetic variance in required
sleep time between animal species (Siegel, 2005, 2009), where thedaily sleep duration can be markedly different in phylogeneticallyrelated animal species, while the basic components that regulate
sleep duration would be conserved.
An obvious next step is to identify the exact downstream
pathway of sleep-promoting kinases, including the substrate thatinduces sleep. We consider it likely that sleep-promoting kinases
target ion channels/pumps. Among these possible targets, it isimportant to focus on phosphorylation sites that attenuate PMCA
activity or potentiate the activity of NMDA receptors, voltagedependent Ca2+ channels, and Ca2+ -dependent K+ channels. In
addition, the Ca2+ -dependent hyperpolarization pathway may
be indirectly regulated by the transcriptional regulation of ionchannels/pumps, by the metabolic regulation of neuronal assembly, and by extracellular ion conditions that are important for
regulating sleep (Ding et al., 2016). The importance of kinase
pathways immediately suggests the involvement of phosphataseactivity. Genetic studies using fruit ﬂies found that the NMDA
receptor interacts with calcineurin, a Ca2+ -dependent protein
phosphatase (Tomita et al., 2011; Tomita et al., 2015). In summary, kinase-phosphatase cascades have the potential to connect
the time-scale gap between fast/microscopic electrophysiologicaldynamics and slow/macroscopic sleep homeostasis. Therefore, theCa2+ -dependent hyperpolarization pathway may be the foundation
for integrating the intra- and inter-cellular signals that regulate
mammalian sleep (Fig. 3).
6. Psychiatric disorders and the Ca2+ -dependent
hyperpolarization hypothesis
Any new model for regulating physiological processes provides
new potential drug targets, and our model for sleep regulation

provides possible therapeutic applications for psychiatric diseases
that are often associated with sleep abnormalities (Wulff et al.,2010). Interestingly, several factors that regulate the proposed
Ca2+ -dependent hyperpolarization pathway in sleep/wake cycles
have been implicated in the molecular bases of major psychiatricdiseases. Schizophrenia-like positive and negative symptoms canbe induced by the pharmacological or immunological impairment
of NMDA receptors (Dalmau et al., 2008; Jentsch and Roth, 1999);
conversely, the pharmacological impairment of NMDA receptors
rapidly cures depression (Autry et al., 2011; Berman et al., 2000).
Although the underlying molecular and cellular mechanisms ofschizophrenia and depression are still under discussion (Coyle,2012), the Ca2+ -dependent hyperpolarization hypothesis may offer
clues to the molecular and cellular bases of these diseases. Thishypothesis explains how inhibiting Ca2+ inﬂux via NMDA receptors
by downregulating neuronal hyperpolarization leads to elevatedneuronal excitability. This elevation is thought to be due to a
direct effect of the attenuated hyperpolarization of cortical neurons, and not to inhibited synaptic repression; this idea has been
partly conﬁrmed by whole-brain imaging (Tatsuki et al., 2016).
If altered neuronal excitability in cortical pyramidal neurons is
causative for the symptoms of schizophrenia and depression, theion channels/pumps shown in Fig. 3 might be direct drug targets.
For example, if the Ca2+ -dependent hyperpolarization pathway
is impaired in patients with schizophrenia, antagonists against
PMCAs and agonists for Ca2+ -dependent K+ channels, voltagegated Ca2+ channels, and NMDA receptors would be potentiallytherapeutic. Similarly, new compounds developed to inhibit theCa2+ -dependent hyperpolarization pathway might be potential
quick-acting drugs to treat depression.
CaMKII␣/␤, possible regulators of the Ca2+ -dependent hyperpolarization pathway, have been implicated in several mental
disorders, including depression, schizophrenia, and bipolar disorder (Hagihara et al., 2016; Purcell et al., 2014; Robison, 2014).
Although many studies focus on CaMKII␣/␤’s function in regulating
synaptic plasticity, the causative relationship between CaMKII␣/␤54

F. Tatsuki et al. / Neuroscience Research 118 (2017) 48–55

and mental disorders is still unclear. Given that an abnormal sleep
pattern is frequently associated with mental disorders (Wulff et al.,2010), impaired Ca2+ -dependent hyperpolarization and abnormal
sleep structure due to CaMKII␣/␤ dysfunction might be causative
for mental disorders. The sleep-promoting kinases SIK3 and ERK1/2
are also potential drug targets, since impairments of these kinases
can also lead to psychiatric disorders.
7. Conclusion
The recent identiﬁcation of sleep-promoting kinases and thephenotypes of various channel-knockout mice provide a possible connection between electrophysiology and sleep homeostasis
via the Ca2+ -dependent hyperpolarization pathway. Because several factors that regulate synaptic plasticity (e.g. NMDA receptors
and CaMKII␣/␤) also regulate the Ca2+ -dependent hyperpolarization pathway, further investigation of hyperpolarization in sleep
regulation will lead to a quantitative understanding of the physiological signiﬁcance of sleep for brain information processing andstorage through the control of synaptic strength (Krueger et al.,2016; Tononi and Cirelli, 2006). A relationship between sleep pressure and cytosolic Ca2+ level via synaptic strength was recently
revealed in Drosophila (Liu et al., 2016), suggesting that sleep regulation and neuronal plasticity is coupled through Ca2+ -dependent
signaling across phyla. Therefore, cortical Ca2+ -dependent hyperpolarization may be a fundamental mechanism that connects fast,
microscopic neuronal activity to the slow, macroscopic dynamics
of sleep pressure.
Author contributions
H.R.U., K.L.O., and F.T. wrote the manuscript.
Acknowledgments
This work was supported by AMED-CREST, CREST, Brain/MINDS,
Basic Science and Platform Technology Program for Innovative Biological Medicine, JSPS (25221004, 23115006, 25830146,
16K14653), RIKEN, and Takeda Science Foundation.
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