The Hypocretin Arousal Network
Summary and Keywords
The hypocretins (also known as orexins) are selectively expressed in a subset of lateral hypothalamic neurons. Since the reports of their discovery in 1998, they have been intensely investigated in relation to their role in sleep/wake transitions, feeding, reward, drug abuse, and motivated behavior. This research has cemented their role as a subcortical relay optimized to tune arousal in response to various salient stimuli. This article reviews their discovery, physiological modulation, circuitry, and integrative functionality contributing to vigilance state transitions and stability. Specific emphasis is placed on humoral and neural inputs regulating hcrt neural function and new evidence for an autoimmune basis of the sleep disorder narcolepsy. Future directions for this field involve dissection of the heterogeneity of this neural population using single-cell transcriptomics, optogenetic, and chemogenetics, as well as monitoring population and single cell activity. Computational models of the hypocretin network, using the “flip-flop” or “integrator neuron” frameworks, provide a fundamental understanding of how this neural population influences brain-wide activity and behavior.
Lateral Hypothalamus as an Arousal Node
The lateral hypothalamic area (LHA) has long been recognized as a primary neural center mediating wakefulness and arousal. During the 1918 influenza pandemic, the Viennese neurologist Baron Constantin von Economo meticulously analyzed and documented the symptoms of a new condition he termed “encephalitis lethargica” (von Economo, 1918). The etiology of this condition (presumed to be autoimmune or viral) remains unknown, but patients showed prolonged periods of sleep (>20 hours/day), and they could only be awakened with strong and extended stimulation (Dickman, 2001). Von Economo documented that some of these patients had significant damage to the posterior hypothalamus (containing the LHA) and rostral midbrain, and patients showing the opposite problem (prolonged insomnia) had damage to the preoptic area and basal forebrain (von Economo, 1931). Based on these observations, he predicted that neurons in the anterior hypothalamus promote sleep while those in the posterior region promote wakefulness. In the following years, his predictions were largely substantiated by studies in multiple species, including monkeys (Ranson, 1939), rats (Nauta, 1946), and cats (Swett & Hobson, 1968).
During this time, the first paper on the use of the electroencephalogram (EEG) to dissect sleep-wake circuitry was published by Giuseppe Moruzzi and Horace Magoun (Moruzzi & Magoun, 1949). In their seminal study, cats were anesthetized or prepared using the encéphale isolé (isolating the brain from the body), a preparation first described by Frederick Bremer. EEG biopotentials were recorded from several cortical sites, and, in both cases high-voltage, low-frequency oscillations were observed (major components of non-rapid eye movement [NREM] sleep). This pattern could be stimulated to transition to low-voltage, high-frequency oscillations consistent with cortical arousal. The researchers aimed to stimulate the superior peduncle, a pathway that extends from the cerebellum through the rostral pons and mesencephalon, and examine its effects on the motor cortex through the thalamus. In essence, they were stimulating the reticular formation. Electrical stimulation of this area caused a rapid switch from high-voltage, low-frequency EEG to low-voltage, high-frequency oscillations across the entire cortex. They had discovered what is known as “the ascending reticular activating system.”
Differential analysis of EEG and electromyogram (EMG) signals allow for the objective classification of vigilance states in mammals and several non-mammalian vertebrates. The EEG reflects large-scale changes in electrical activity in the cerebral cortex, with cortical firing rates steadily declining during NREM sleep in comparison to rapid eye movement (REM) sleep and wakefulness (Loomis, Harvey, & Hobart, 1935; Siegel, 2009; Szymusiak, Gvilia, & McGinty, 2007). EEG signals can be parsed into conventional bandwidths that reflect cortical firing rates at approximate frequencies: delta (0.5–4 Hz), theta (5–9 Hz), alpha (9–12 Hz), beta (12–30 Hz), and low (30–60 Hz) and fast gamma (60–100 Hz). Sigma or “spindle” frequencies are also described within the range of 10 to 16 Hz, largely driven by the thalamic reticular formation. Synchronization (reflected in high-voltage, low-frequency EEG) of cortical firing rates during NREM sleep depends on a cortico-thalamo-cortical loop, which is modulated distally by subcortical systems (including the hypocretin system; Crunelli & Hughes, 2010). During NREM sleep, delta waves dominate the EEG and EMG activity (postural tone) is low or absent. During wakefulness, EMG activity is high, reflecting muscle tone, and the EEG shows task-dependent spectral properties, with low theta frequencies building over time indicating sleep propensity (Vyazovskiy & Tobler, 2005). REM sleep, also known as “paradoxical” sleep, is characterized by a “wake-like” EEG, where theta waves dominate, but (paradoxically) EMG activity is low or absent (Jouvet, Michel, & Courjon, 1959).
Discovery and Initial Characterization of the Hypocretins/Orexins
The hypocretins were discovered by two groups at essentially the same time using disparate methods, reported in 1998. The first group (lead by J. Gregor Sutcliffe at the Scripps Research Institute in La Jolla, California) was interested in surveying hypothalamic-enriched messenger ribonucleic acids (mRNAs) to determine if specific genes expressed in these nuclei contributed to their functions (de Lecea et al., 1998). They discovered a cDNA clone that encoded a predicted preproprotein containing two putative peptides exclusively expressed in a group of neurons in the lateral/paraventricular hypothalamus. Because of their anatomical location and sequence similarity with the gut hormone secretin, they were named the hypocretins (1 and 2).
Another group (lead by Masashi Yanagisawa at the University of Texas in Dallas, Texas) screened for novel ligands of orphan G-protein coupled receptors (GPCRs) using a cell-based reporter system. Because GPCRs are powerful targets for drugs, it was thought that identifying their natural ligands would be fundamentally important in the development of treatments for a variety of diseases. The researchers discovered two peptides, and because injecting them into the brain promoted feeding, they named them the orexins (A and B) without realizing they were the same as the hypocretins (Sakurai et al., 1998). Since the hypocretins were discovered first, and because their main function seems to be in wake stability and not feeding, we refer to them as the hypocretins (hcrt) throughout this article. Following their discovery, an antibody was generated against hcrt to map its distribution in the rodent brain. This effort revealed that these cells send expansive projections throughout virtually the entire brain, with dense fibers observed in the locus coeruleus, midbrain, and paraventricular thalamus, among others (Peyron et al., 1998). This implicated these cells in functions ranging from arousal and motivation to neuroendocrine and body temperature control.
Initial microdialysis and c-fos immunoreactivity studies demonstrated that hcrt neurons showed a circadian activity pattern, where firing rates peaked during the active (dark) phase in rodents (Estabrooke et al., 2001; Yoshida et al., 2001). Juxtacellular recordings in head-fixed or freely moving animals revealed that hcrt neural activity is phasic and firing rates peak approximately 10 to 20 seconds prior to a sleep-to-wake transition (Lee, Hassani, & Jones, 2005; Mileykovskiy, Kiyashchenko, & Siegel, 2005). The advent of optogenetics allowed for millisecond-timescale control of distinct neural populations (Boyden, Zhang, Bamberg, Nagel, & Deisseroth, 2005), with the first in vivo use of this technology applied to the hcrt neurons to causally test their role in vigilance state stability and transitions (Adamantidis, Zhang, Aravanis, Deisseroth, & de Lecea, 2007). This study demonstrated that light-evoked stimulation of hcrt neurons powerfully promotes wakefulness in mice, an effect dependent on hcrt release (and not other co-released neurotransmitters). The effect on arousal was frequency dependent, as stimulations below 5 Hz failed to induce sleep-to-wake transitions. Subsequent studies using designer receptors exclusively activated by designer drugs (DREADDs) confirmed these findings (Sasaki et al., 2011). Hcrt neurons expressing the excitatory Gq-coupled DREADD increased cFos expression upon clozapine-N-oxide (CNO) administration, which was associated with increased wakefulness. Alternatively, when hcrt neurons were made to express the Gi-coupled inhibitory DREADD, CNO injection promoted sleep (Sasaki et al., 2011). Further optogenetic silencing experiments demonstrated that long-term silencing of hcrt neurons promotes slow-wave sleep in mice, highlighting their essential role in wake maintenance (Tsunematsu et al., 2013). A more recent study investigated exactly which components of wakefulness hcrts are responsible for. Vassali and Franken (2017) observed that mice deficient in hcrt had significant reductions in theta-dominated wakefulness, which is an EEG oscillatory pattern associated with goal-directed and exploratory behavior. In sleep-deprivation experiments, however, these mice showed normal amounts of theta activity during wakefulness, suggesting that hcrt is necessary for spontaneous, but not enforced, wakefulness.
These lines of research have led to the development of novel drugs for insomnia. Because insomnia is thought to be a disorder of overactive arousal-promoting neural populations (Riemann et al., 2015), hcrt receptor antagonism is an attractive approach given the nonredundant role this neural population plays in the stability of wakefulness. Suvorexant (Belsomra) was the first dual-hcrt receptor antagonist approved for use by the Food and Drug Administration (in 2014) and is prescribed widely for insomnia in the United States and Japan.
Narcolepsy is a debilitating disorder of sleep-wake states, characterized by aberrant transitions from wakefulness into REM sleep with bouts of cataplexy, affecting approximately 25 to 50 per 100,000 people (Longstreth, Koepsell, Ton, Hendrickson, & van Belle, 2007). Mice lacking hcrt or hcrt receptors were reported to show a narcolepsy-like phenotype, including cataplexy and disrupted sleep-wake cycles (Chemelli et al., 1999; Hara et al., 2001; Willie, Chemelli, Sinton, & Yanagisawa, 2003). Further canine data identified a mutation in hcrt receptor 2 (hcrtR2) linked to the development of narcolepsy in dogs (Lin et al., 1999). Additionally, several studies have demonstrated that degenerative loss of hcrt neurons is responsible for human narcolepsy (Nishino, Ripley, Overeem, Lammers, & Mignot, 2000; Peyron et al., 2000; Ripley et al., 2001). Loss of hcrt neurons, rather than reduced hcrt gene expression, is the likely cause, as other markers that co-localize within hcrt neurons are also reduced in narcoleptic patients (Crocker et al., 2005).
Thought to be an autoimmune disorder (reviewed in Mahlios, De la Herrán-Arita, & Mignot, 2013), narcolepsy is associated with specific genotypes in the antigen presentation complex HLA-DQB1 (HLA-DQB1*06:02) (present in 98.4% of patients vs. 17.7% of the general European population; European Narcolepsy Network, 2014; Mignot, Hayduk, Black, Grumet, & Guilleminault, 1997). Five epitopes from the hcrt precursors are predicted to bind HLA-DQB1*06:02; however, a recent study failed to find evidence of autoreactive CD4+ T-cell targeting of hcrt precursors (Kornum et al., 2017). Additionally, a study claiming to find CD4+ T-cell mediated autoimmunity targeting hcrt and cross-reactivity to an epitope present on the 2009 H1N1 influenza virus was retracted when the authors failed to replicate their own findings (de la Herran-Arita et al., 2014). The 2009 H1N1 “swine flu” epidemic reinvigorated research into the autoimmune basis of narcolepsy, as cases of childhood narcolepsy rose dramatically in relation to administration of the adjuvant present in the vaccine (Partinen et al., 2012). In a mouse model of H1N1 infection, the virus entered sleep-wake regulatory regions of the brain, producing a narcolepsy-like phenotype (Tesoriero et al., 2016). This suggests that, independent of an autoimmune response, the H1N1 virus can directly infect and kill neurons that control sleep-wake states. Additionally, artificially expressing the neo-self-antigen hemagglutinin (HA) in hcrt neurons and subsequent transfer of HA-reactive CD8+ T cells causes destruction of hcrt neurons and produces a narcolepsy-like phenotype (Bernard-Valnet et al., 2016).
Recently, a study from Frederica Sallusto and colleagues demonstrated that CD4+ autoreactive T cells do indeed target amino acid residues present in the hypocretins (Latorre et al., 2018). Furthermore, they confirmed CD8+ T cell responses to hypocretin peptides from narcoleptic patients, but not controls. Importantly, they provided evidence that autoreactive cells were stimulated by another protein enriched in hypocretin neurons: TRIB2. Finally, they failed to find evidence of ‘molecular mimicry’ between the hypocretins and the influenza vaccine, as T-cells from patients with narcolepsy failed to proliferate in response to an influenza vaccine containing A/California/7/2009 H1N1 strains or to one containing CA09 H1 haemagglutinin (Latorre et al., 2018). Therefore, the association between vaccination and the development of narcolepsy still remains a mystery. These recent experiments strongly support the notion that narcolepsy has an autoimmune origin.
Conservation of Hcrt Peptides Across the Phylogenetic Tree
Highlighting their essential role in vigilance state stability is the remarkable conservation of the hcrt across the phylogenetic tree. Hypothalamic hcrt expression can be detected in the zebrafish embryo 22 hours post-fertilization (Faraco et al., 2006), and genetic ablation of hcrt neurons in zebrafish results in a behavioral phenotype markedly similar to their mammalian counterparts lacking these neurons (Elbaz, Yelin-Bekerman, Nicenboim, Vatine, & Appelbaum, 2012). Hcrt neurons send expansive projections across the zebrafish brain to participate in the consolidation of wakefulness, and, just as in mammals, hcrt-evoked wakefulness is reliant on norepinephrine signaling (Elbaz et al., 2017; Singh, Oikonomou, & Prober, 2015). Recent research has demonstrated that hcrt is differentially expressed in closely related Mexican cave versus surface fish, where it is associated with differing amounts of wakefulness and may play a role in the evolution of sleep loss (Jaggard et al., 2018). Hcrts are also expressed in the avian brain, where they are localized to a single population of neurons spanning the paraventricular and lateral hypothalamic areas (Ohkubo, Boswell, & Lumineau, 2002; Singletary, Deviche, Strand, & Delville, 2007). Comparative approaches using multiple species will undoubtedly provide valuable data on the evolution of this (and other) arousal systems.
Primary Afferents to Hcrt Neurons
Afferent inputs to hcrt neurons were mapped using a combination of tract tracing methods, uncovering major projections from the lateral septal nucleus, bed nucleus of the stria terminalis, preoptic area, multiple hypothalamic nuclei, substantia nigra, ventral tegmental area (VTA), and dorsal raphe (DRN; Yoshida, McCormack, España, Crocker, & Scammell, 2006). Genetic tracing studies revealed cell-type specific inputs arriving from cholinergic neurons in the laterodorsal tegmentum, preoptic gamma aminobutyric acid (GABA)-ergic neurons, and serotonin (5-HT)-expressing neurons in the median/paramedian raphe (Sakurai et al., 2005). Additionally, inputs from monoaminergic and peptidergic systems may use “volume transmission,” which is comprised of short- (but larger than a synaptic cleft) and long-distance diffusion of chemical signals via the extracellular space or cerebrospinal fluid (Agnati, Zoli, Strömberg, & Fuxe, 1995). Further, as new tools are developed, important input pathways are being described. For example, GABAergic neurons in the zona incerta expressing the transcription factor Lhx6 were recently shown to directly inhibit hcrt neurons in the LHA (Liu et al., 2017). Although too numerous to describe in detail here, we highlight a few important afferents likely involved in vigilance state dynamics.
Ventrolateral Preoptic Area
Sleep-active neurons in the ventrolateral preoptic area (vlPOA) send inhibitory projections to hcrt neurons in the LHA, as well as has other monoaminergic groups in the brainstem. Incoming GABAergic fibers from this region are thought to inhibit hcrt neurons via actions at the GABAB receptor (Xie et al., 2006). A cross-talk among the vlPOA, hcrt, and downstream monoaminergic cells groups in the tuberomammillary nucleus, locus coeruleus, and dorsal raphe (discussed in subsequent sections) is thought to regulate vigilance state switching in a “flip-flop” fashion where hcrt acts to stabilize the circuit (Saper, Chou, & Scammell, 2001; Saper, Fuller, Pedersen, Lu, & Scammell, 2010). For example, during wakefulness, excitatory signaling from hcrt neurons to monoaminergic groups promotes downstream cortical activation. These cell groups send inhibitory projections back to hcrt and vlPOA neurons. A subsequent decrease in hcrt activity results in their own disinhibition and promotes excitatory drive to the monoaminergic cell groups to maintain wakefulness. During sleep, vlPOA neurons inhibit hcrt activity to keep the organism in the sleep state. In the case of narcolepsy, the intervening hcrt population is lost, and rapid transitions between states can occur with the smallest perturbation (Sakurai, 2007).
The heterogeneous basal forebrain (BF) sends reciprocal inputs to hcrt neurons (Henny & Jones, 2006). Hcrt neurons receive differential input from cholinergic, GABAergic, and glutamatergic afferents arriving from the basal forebrain, with GABAergic inputs being the most prominent. These inputs suggest a complex interaction between different cell groups within the BF regulating hcrt neural activity both during wakefulness and sleep (Jones, 2008).
The dorsomedial hypothalamus (DMH) and supraventricular zone send abundant inputs to hcrt neurons (Sakurai et al., 2005). This nucleus may act as a relay signaling circadian components from the suprachiasmatic nuclei to the LHA, as hcrt neurons receive only sparse input from the suprachiasmatic nuclei. The DMH seems to be important for food-anticipatory activity, where daily restricted feeding entrains a locomotor activity rhythm that precedes the presentation of food (Mieda, Williams, Richardson, Tanaka, & Yanagisawa, 2006). Mice deficient in hcrt show aberrant food-anticipatory activity and altered clock gene expression in the forebrain (Akiyama et al., 2004). Therefore, signaling via the DMH to hcrt neurons may play a key role in linking the food-entrainable oscillator with arousal (Yamanaka et al., 2003).
Amygdala and the Bed Nucleus of the Stria Terminalis
The amygdala (AMY) and bed nucleus of the stria terminalis (BNST; also known as the extended AMY) provide abundant and cell-type specific inputs to hcrt neurons (Sakurai et al., 2005). A large amount of afferents were observed to arrive at hcrt neurons from multiple regions in the AMY, including the central, medial, basomedial, and anterior cortical AMY, as well as basolateral AMY. Heavy staining indicating synaptic connection with hcrt neurons was observed in the BNST, especially in the antero-medial and ventro-lateral (Sakurai et al., 2005). Because of the heterogeneity among AMY and BNST neurons, dissecting their functional inputs to hcrt neurons has been challenging. In 2018, Giardino and colleagues tackled this challenge, demonstrating that subtypes of GABAergic neurons within the BNST bidirectionally contribute to opposing emotional states, partially dependent on their input to hcrt neurons in the lateral hypothalamus (LH). Specifically, corticotropin release factor (CRF)-positive neurons occupied the lateral BNST and sent more inputs to hcrt neurons to promote behaviors associated with negative valence (i.e., were aversive). Reciprocally, cholecystokinin-positive neurons in the medial BNST promote behavior associated with a positive emotional valence (i.e., were pleasurable) likely via their sparse projections to hcrt neurons and modulation of neighboring leptin receptor (lepRb)-expressing neurons. These neurons received direct input from upstream cells in the central AMY. This study highlights a novel pathway by which limbic structures (e.g., AMY and BNST) can bidirectionally modulate hcrt neurons, linking emotional state directly to sleep-wake circuity.
Primary Efferents Controlling Cortical Arousal
In the years following the discovery of hcrts, multiple groups have investigated their actions at anatomical projection sites and subsequent effects on arousal and other behaviors. One hypothesis on how these peptides regulate arousal is via general distributed effects throughout the brain. Alternatively, specific projections to powerful wake-promoting regions could be primarily responsible for the effects hcrt has on sleep-to-wake transitions. Indeed, evidence has accumulated supporting the latter hypothesis, which we discuss in subsequent sections.
The noradrenergic locus coeruleus (LC) has been long recognized as a critical hub for the promotion of wakefulness and arousal (Aston-Jones & Cohen, 2005; Berridge & Waterhouse, 2003). It receives dense inputs from hcrt neurons (Peyron et al., 1998), generating the hypothesis that hcrt neurons promote arousal via direct actions on LC neurons. Indeed, direct administration of hcrt to the LC depolarizes neurons in this area, increases LC firing rates, and promotes arousal (Gompf & Aston-Jones, 2008; Hagan et al., 1999; Horvath et al., 1999). Direct silencing of LC neurons in tandem with optogenetic excitation of hcrt neurons prevented hcrt-evoked sleep-to-wake transitions, suggesting that LHAhcrt → LC is a critical arousal pathway (Carter et al., 2012). In a separate set of experiments, increasing the excitability of LC neurons using step function opsins facilitated hcrt-evoked wakefulness, further supporting this idea.
The BF is a heterogeneous structure made up of both cholinergic and noncholinergic (primarily GABAergic) neurons with disparate effects on wakefulness, spanning the substantia innominata, the vertical and horizontal limbs of the diagonal band, the extended AMY, the ventral pallidum, and the medial septum (Jones, 2004). These areas receive moderate to dense input from hcrt neurons (Peyron et al., 1998). Using cell-type specific tools, optogenetic stimulation of cholinergic neurons within the BF was shown to be sufficient for activation of the cortex and transitions out of NREM sleep (Han et al., 2014; Irmak & de Lecea, 2014). Additional targeting of GABAergic (i.e., noncholinergic) neurons in this region demonstrated their role in wakefulness (Anaclet et al., 2015). Cholinergic neurons within the BF were shown to fire predominantly during cortical activation, rather than NREM sleep, and are activated by hcrt, an effect dependent on hcrtR2 (Eggermann et al., 2001). Direct injection of hcrt into the BF stimulates cortical activation and wakefulness (España, Baldo, Kelley, & Berridge, 2001), and hcrt1 released in the BF during wakefulness has differential actions on cholinergic and noncholinergic neurons, leading to cortical release of acetylcholine and arousal (Arrigoni, Mochizuki, & Scammell, 2010). Dynorphin, a co-transmitter released from hcrt synaptic vesicles, is also able to modulate the activity of BF cholinergic neurons, which adds another layer of complexity onto this circuit and its role in sleep-wake transitions (Ferrari et al., 2016).
Histaminergic neurons in the tuberomammillary nucleus (TMN) are silent during sleep and begin to fire after wake onset, where histamine release promotes arousal (Haas & Panula, 2003; Takahashi, Lin, & Sakai, 2006). Acute optogenetic silencing of TMN histamine neurons inhibits wakefulness and rapidly induces NREM sleep (Fujita et al., 2017). Hcrt was shown to directly activate histamine neurons in the TMN and modulate GABAergic inputs to this region (Bayer et al., 2001; Eriksson, Sergeeva, Brown, & Haas, 2001; Eriksson, Sergeeva, Selbach, & Haas, 2004). The LHAhcrt → TMN circuit may not be necessary for hcrt-evoked arousal, as mice lacking histidine decarboxylase (the rate-limiting enzyme in histamine synthesis) show normal sleep-to-wake transitions upon hcrt neural stimulation (Carter, Adamantidis, Ohtsu, Deisseroth, & de Lecea, 2009). Direct stimulation of hcrt+ axonal fibers in the TMN and assessment of vigilance states will help determine the exact effect hcrt has in the brain region on arousal.
The DRN shows vigilance state-specific changes in firing rates and has long been implicated in arousal (McGinty & Harper, 1976). Composed of primarily 5-HT and dopaminergic (DA) neurons, the DRN is excited by many arousal-related peptides, including hcrt (Brown, Sergeeva, Eriksson, & Haas, 2002; Kohlmeier, Inoue, & Leonard, 2004; Kohlmeier, Watanabe, Tyler, Burlet, & Leonard, 2008). Optogenetic stimulation of 5-HT or DA neurons in the DRN causes rapid sleep-to-wake transitions (Cho et al., 2017; Moriya et al., 2017). Despite their demonstrated role in vigilance state dynamics, the natural activity of hcrt axon terminals or direct stimulation of hcrt fibers in this region has not been causally investigated.
Laterodorsal Tegmental Nucleus
The laterodorsal tegmental nucleus (LDT) is a pontine site critical for the regulation of wakefulness, composed of cholinergic and noncholinergic neurons (Satoh & Fibiger, 1986). Hcrt was demonstrated to promote wakefulness in cats when directly injected into the LDT (Takahashi, Koyama, Kayama, & Yamamoto, 2002; Xi, Morales, & Chase, 2001). The effect of hcrt on LDT neurons is dependent on its actions at both pre- and postsynaptic sites. Presynaptically, hcrt increases the amplitude and frequency of spontaneous excitatory post-synaptic currents (EPSCs) via triggering action potentials and enhancing synaptic transmission in glutamatergic axon terminals. Postsynaptically, hcrt promotes an inward current coincident with elevated current noise in both cholinergic and noncholinergic LDT neurons (Burlet, Tyler, & Leonard, 2002). Because many LDT neurons are only active during REM sleep (Sakai, 2015), hcrt may suppress this vigilance state by transiently inhibiting the output of LDT cells. Similar to other arousal nodes, application of optogenetic/chemogenetic tools in tandem with fiber photometry will provide a clearer understanding of how hcrt operates in this brain region to regulate arousal state transitions.
The VTA has long been implicated in arousal and motivated behavior (Boutrel & Koob, 2004; Miller, Farber, Gatz, Roffwarg, & German, 1983). It is composed of primarily DA and GABAergic (with a small subset of glutamatergic) neurons, which have each been demonstrated to show vigilance-state specific changes in firing rates, suggesting they may play a role in sleep-wake transitions (Dahan et al., 2007; Lee, Steffensen, & Henriksen, 2001). Only recently, however, have VTA-DA neurons been directly implicated in arousal, as cell-type specific activation of this neural population powerfully promotes wakefulness (Eban-Rothschild, Rothschild, Giardino, Jones, & de Lecea, 2016; Oishi et al., 2017; Taylor et al., 2016). Hcrt neurons send dense projections to the VTA, where they are able to activate both DA and non-DA neurons, a property not shared by the co-distributed melanin concentrating hormone (MCH) neural population (Korotkova, Sergeeva, Eriksson, Haas, & Brown, 2003). Infusion of hcrt into the lateral ventricles or directly into the VTA is able to increase DA efflux in the prefrontal cortex, suggesting that the VTA contributes to hcrt’s actions on vigilance (Vittoz & Berridge, 2006). Whether a direct circuit from LHAhcrt → VTA DA or GABAergic neurons causally promotes wakefulness remains to be empirically demonstrated. In addition to these data on arousal, significant research has been conducted on how hcrt (and the co-transmitter dynorphin) in the VTA contributes to drug seeking and abuse, as well as reward (Tyree & de Lecea, 2017).
Integration of Physiological Signals
Hcrt neurons are sensitive to signals arriving from the periphery including satiety, stress, and immune factors. They integrate these signals (through unknown mechanisms) and modulate their excitability or firing rates to adjust behavioral state accordingly. In the next sections, we discuss several salient physiological signals that modulate hcrt neural activity.
Leptin is an adipokine hormone (primarily secreted by white adipocytes) that positively correlates with satiety (Martí et al., 1999). Hcrt neurons express receptors for, and are inhibited by, leptin either directly (Håkansson, de Lecea, Sutcliffe, Yanagisawa, & Meister, 1999; Yamanaka et al., 2003) or through local GABAergic neurons that express the lepRb (Bonnavion, Jackson, Carter, & de Lecea, 2015). Reductions in circulating leptin levels during fasting activates hcrt neurons (Diano, Horvath, Urbanski, Sotonyi, & Horvath, 2003; Leinninger et al., 2011). Indeed, leptin administration can block optogenetically evoked cFos activity in hcrt neurons, which is associated with reduced hypothalamic–pituitary–adrenal (HPA) axis activation coincident with induction of phosphorylated (activated) signal transducer and activator of transcription 3 (pSTAT3) (Bonnavion et al., 2015). Leptin was shown to further inhibit hcrt neurons via local neurotensin-expressing neurons in the LHA via GABA-independent mechanisms (Goforth, Leinninger, Patterson, Satin, & Myers, 2014).
Ghrelin is a gut hormone upregulated during extended periods of fasting and is a powerful modulator of feeding behavior (Nakazato et al., 2001). Two isoforms of ghrelin exist, des-acyl and acyl-ghrelin, generally thought of as “inactive” and “active” ghrelin, respectively. Ghrelin-O-acyl-transferase is the enzyme that enables ghrelin to bind and activate its receptor (GHS-R). Hcrt neurons express GHS-Rs, central administration of ghrelin increases cFos expression in hcrt neurons (Lawrence, Snape, Baudoin, & Luckman, 2002; Olszewski et al., 2003), and ghrelin-induced feeding can be blocked via attenuation of hcrt signaling (So et al., 2018; Toshinai et al., 2003). More recent research demonstrated that ghrelin signaling from the ventral hippocampus to lateral hypothalamic hcrt neurons promotes hyperphagia, providing an additional top-down circuit that regulates feeding behavior (Hsu et al., 2015). A proximate explanation for these findings is that, during periods of fasting, elevations in circulating ghrelin concentrations activate hcrt neurons to promote a behavioral food-seeking program to restore metabolic homeostasis.
Glucose and Hypoglycemia
Evidence has accumulated suggesting a role for hcrt in the detection and regulation of glucose metabolism (Burdakov, Luckman, & Verkhratsky, 2005; Yamanaka et al., 2003; Yi et al., 2009). Physiological increases in extracellular glucose inhibit hcrt neurons via a tandem-pore potassium channel (Burdakov et al., 2006), and insulin-induced hypoglycemia activates hcrt neurons in a reciprocal fashion (Moriguchi, Sakurai, Nambu, Yanagisawa, & Goto, 1999). These data position hcrt neurons as energy/nutrient sensors that can then influence whole-organism metabolism via specific actions in the periphery or through behavioral adjustment of the organism. For example, disinhibition of LHA hcrt neurons increases hepatic glucose production (putatively through an autonomic pathway; Yi et al., 2009), and it has been suggested that hypoglycemia promotes wakefulness and food-seeking to restore metabolic homeostasis (Willie et al., 2001). Interestingly, recent fiber photometry data demonstrates that hcrt neurons are rapidly inactivated upon eating, even when the food is “calorie-free” (González et al., 2016). This suggests that the act of eating, rather than the nutritional value of the food, contributes to hcrt activity in relation to metabolism.
Dietary Amino Acids
In addition to detecting humoral signals associated with satiety (leptin, ghrelin, glucose), research has demonstrated that amino acids derived from food are able to modulate hcrt neural activity (Li, Hu, & de Lecea, 2013). Hcrt neurons were shown to be stimulated by nutritionally relevant mixtures of amino acids (AAs) through slice patch-clamp experiments as well as cFos mapping studies (Karnani et al., 2011). This excitatory property of AAs was demonstrated to require inhibition of adenosine triphosphate–sensitive potassium channels and activation of system-A AA transporters, and nonessential AAs were more stimulatory than essential AAs. Additionally, AAs suppressed hcrt responses to glucose (discussed previously), suggesting that these cells sense macronutrient balance rather than net energy value in the extracellular space.
pH and Carbon Dioxide
Extracellular levels of acid and carbon dioxide (CO2) are fundamental signals regulating arousal and breathing. Initial anatomical research suggested that hcrt may play a role in the regulation of breathing (Peyron et al., 1998), which was confirmed via selective injection of hcrt into brainstem breathing control centers expressing hcrt receptors (Young et al., 2005). Subsequent research demonstrated that hcrt neurons are directly sensitive to CO2 and changes in pH (Sunanaga, Deng, Zhang, Kanmura, & Kuwaki, 2009; Williams, Jensen, Verkhratsky, Fugger, & Burdakov, 2007). In slice electrophysiological experiments, hcrt neurons were excited by elevations in bath CO2 and reductions (acidification) in extracellular pH. As hcrt neurons expressed green fluorescent protein (GFP) in these experiments, their responses to changes in pH and CO2 could be compared to neighboring non-hcrt neurons. In this case, these signals either did not have any effect on firing rates or cells showed the opposite response (increased firing in response to alkalization), suggesting that detection of these breathing-relevant stimuli was specific to LHA hcrt neurons (Williams et al., 2007). Another study demonstrated that inhaled CO2, rather than an in vitro manipulation, also activated hcrt neurons (although it is unclear whether this effect was direct; Sunanaga et al., 2009). Other circuits sensitive to CO2 include calcitonin gene-related peptide neurons in the parabrachial area (Kaur et al., 2017), which also may affect the activity of LH neurons through direct and indirect synaptic connections (Lutz, 2013). Additionally, dorsal raphe 5-HT+ neurons mediate CO2-induced arousal independent of increases in breathing rate (Smith et al., 2018). These actions may be governed by inputs arriving from hcrt neurons in the LH (Liu, Van Den Pol, & Aghajanian, 2002), but this has not yet been tested.
Stress and Glucocorticoids
Stress and arousal are fundamentally coupled. It is unsurprising that stress powerfully activates hcrt neurons, which then exert modulatory properties on the HPA axis (Winsky-Sommerer, Boutrel, & de Lecea, 2005). The CRF peptidergic system directly innervates and depolarizes hcrt neurons, an effect that can be blocked via CRF-R1 antagonism (Winsky-Sommerer et al., 2004). It was hypothesized that hcrt neurons integrate stressful stimuli (via the CRF system) to adjust arousal accordingly. Hcrt may not be equally involved in all forms of stress, and hcrt may be engaged in response to stress specifically when arousal requires increased attention to environmental cues (Furlong, Vianna, Liu, & Carrive, 2009). Subsequent research then demonstrated that there is feedback between the HPA axis and the hcrt system, where activation of hcrt neurons results in subsequent elevations in circulating glucocorticoids (Bonnavion et al., 2015).
An essential component of “sickness behavior” is a reduction in general arousal, increased sleep, and reduced locomotor behavior (Dantzer & Kelley, 2007). This response is largely driven by cytokines and chemokines communicating with the brain upon immune challenge. Using cFos as a marker of neural activation, Grossberg and colleagues (2011) demonstrated that peripheral administration of lipopolysaccharides reduced hcrt neural activity coincident with reductions in locomotor activity indicative of “lethargy.” Subsequent central administration of hcrt prevented lipopolysaccharides-induced lethargy, suggesting a causal role for hcrt in inflammation-induced reductions in locomotor activity. In a cytotoxic chemotherapy-induced model of fatigue using doxorubicin, cyclophosphamide, and 5-fluorouracil, drug administration resulted in a similar reduction in hcrt Fos expression coincident with hypothalamic inflammation and reduced locomotor activity (Weymann, Wood, Zhu, & Marks, 2014). Co-administration of central hcrt rescued cytotoxic chemotherapy-induced “fatigue.” Although there is some evidence that hcrt neurons express specific cytokine receptors (Schéle et al., 2012), direct actions (e.g., electrophysiological) of specific cytokines on these neurons is lacking. Interleukin 6 (IL-6) is a likely mediator of inflammation-induced changes to hcrt neural activity given its shared signal transduction pathway with leptin (both IL-6 and lepRBs are coupled to gp130/STAT3).
A fundamental question that remains to be answered is: What is the functional heterogeneity of these neurons and their connections? Do all hcrt neurons participate equally in the various functions of this cell group, or are there specific transcriptional and circuit components that dictate their contributions to arousal? With the introduction of optogenetic/chemogenetic tools delivered in cre-dependent adeno-associated viral vectors along with single-cell transcriptional profiling techniques, the functional properties of different hcrt-expressing neurons can be causally investigated.
Heterogeneity within a neural population can be due to (a) the diversity in gene/protein expression within the cells themselves, (b) diversity among inputs, (c) differential outputs, and (d) temporal multiplexing of circuit function. Anatomical position within a system can provide additional insight into the proposed function of specific cells. Indeed, an initial idea that gained some support was that hcrt neurons were functionally heterogeneous based on their topographical position along the mediolateral axis of the hypothalamus, where dorsomedial populations were activated in relation to wakefulness and aversive stimuli, while lateral populations were activated during conditioned food or drug reward seeking (Harris & Aston-Jones, 2006; Mahler, Moorman, Smith, James, & Aston-Jones, 2014). Additionally, hypothalamic inputs to hcrt neurons were shown to target primarily medial and perifornical regions, while projections arriving from the brainstem seemed to target mostly lateral components of the hcrt field (Sakurai, 2007). Other anatomical and electrophysiological data failed to support this idea (González, Jensen, Fugger, & Burdakov, 2012), suggesting a rather homogenous efferent projection pattern among the medio-lateral axis.
One group investigated transcriptional profiles of hcrt and co-distributed MCH neurons (Mickelsen et al., 2017). They found that these neurons can be further discriminated based on their expression of key transcription factors (among others) (Lhx9; 100% of hcrt neurons, 3.4% of MCH neurons; and Nkx2.1: 60.1% of MCH neurons and 0% of hcrt neurons). Further efforts using retro-labeling of hcrt neurons from specific output nuclei (e.g., LC, TMN, BF) and subsequent single-cell RNA-seq will provide another layer of information regarding exactly how these neurons functionally integrate and regulate so many processes simultaneously.
The hcrt system is an attractive starting point with which to develop quantitative models of the sleep-wake cycle (Sakurai & Mieda, 2011; Sorooshyari, Huerta, & de Lecea, 2015). As discussed in prior sections, the primary function of the hcrt system is to control and maintain boundaries between mutually exclusive vigilance states. Quantitative models of the hcrt arousal network can be used to explain experimentally observed biological variables, approximate or propose limits on a biological system, and drive the design of experiments and technologies. As sleep-to-wake transitions are a behavioral output of the hcrt system, any model will have to consider three primary components: the neural, neural circuit, and behavioral levels. As an added layer of complexity, any complete model must take into account the multiple timescales across which hcrt neurons function (e.g., fast amino acid neurotransmission, slow neuromodulator activity, volume transmission, and circadian rhythms in plasticity/excitability).
Initial work positioned hcrt as a master regulator in a flip-flop model (Saper et al., 2010), where it works to stabilize wake-promoting circuitry preventing aberrant “flips” between vigilance states. However, this model cannot take into account intermediate or disassociated arousal states, which occur in disorders like narcolepsy or REM sleep behavior disorder, or phenomena like unihemispheric or local sleep, which cannot be explained by a simple binary decision (Konadhode, Pelluru, & Shiromani, 2016; Rattenborg, Amlaner, & Lima, 2000). Furthermore, awakening is frequently associated with “sleep inertia,” a prolonged state of impaired cognition following wakefulness, which cannot be adequately explained using the flip-flop model (Vyazovskiy et al., 2014).
An alternative to the flip-flop model is an integrator function (Figure 1), where weighted inputs to an “integrator” neuron (e.g., hcrt) are computed and the neuron makes a decision on whether to fire and wake the animal or remain silent and promote sleep. By comparing latencies to wakefulness upon optogenetic stimulation of discrete neural populations, the “weights” of the inputs can be approximated based on their ability to promote wakefulness (Carter & de Lecea, 2011; Eban-Rothschild, Appelbaum, & de Lecea, 2018). Using this approach, a “codebook” can be generated that can be used to predict the response of the integrator neuron upon receiving each weighted input (Sorooshyari et al., 2015). A more realistic version of this approach would rely on a continuous activation function that reflects the probabilities of wakefulness, integrating both neural and humoral inputs, as well as “hidden layers.” This model also allows for adaptation to both short and long integration times, as the time it takes the neuron to “make a decision” based on the weighted inputs can be adjusted to reflect real-world data.
The hcrts serve a remarkably nonredundant role in the stabilization of wakefulness. Since their discovery in 1998, thousands of papers have been published investigating their contributions to arousal, reward, motivation, drug abuse, sexual behavior, metabolism, and neuroendocrine function. Additionally, they have been causally implicated in the etiology of narcolepsy, and specific receptor antagonists have been developed as novel treatments for insomnia disorders. Dual receptor antagonists or those specifically targeting hcrtR2 (but not hcrtR1) seem to be the most efficacious at promoting sleep (Li, Nevárez, Giardino, & de Lecea, 2018). Despite this tremendous body of work, little is still known about the functional heterogeneity of these cells, either in their transcriptional profiles or in their wiring. Finally, integration of biological data into computational models of the sleep-wake cycle is still in its infancy. How does a single hcrt neuron integrate, compute, and distribute an “arousal” signal? How is this functionally integrated into the hcrt circuit and downstream effector centers? What is a “minimal sleep circuit,” and how can we use quantitative modeling to push the boundaries of what a system is capable of? Using cutting-edge tools such as cre-dependent adeno-associated viral vectors and single-cell transcriptomics in tandem with CRISPR/Cas9, fiber photometry, and optogenetics, the time is ripe for addressing these longstanding questions in the field.
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