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date: 20 November 2018

Suprachiasmatic Nucleus Anatomy, Physiology, and Neurochemistry

Summary and Keywords

We live in an approximately 24-hour world and circadian rhythms have evolved to adapt organisms to the opportunities presented by Earth’s 24-hour cycle of light and dark. A “master clock” located in the suprachiasmatic nucleus (SCN) of the brain orchestrates daily rhythms in all manner of behavioral, endocrine, metabolic, autonomic, and homeostatic systems in our bodies. The SCN is comprised of about 20,000 neurons and about one third as many astroglia. How can so few neurons and astroglia guide so many rhythms? How do neurons time out an interval as long as a day? The answers are a case study in understanding how genes within cells, and cells within circuits, function together to perform complex activities and optimize bodily functions. While individual clock cells are found in virtually all bodily tissues, the unique connectome of the SCN, its specialized afferent inputs from the retinohypothalamic tract, and its neural and humoral outputs enable its “babel” of neuronal types to synchronize their activity and signal time to the rest of the body.

At the molecular-cellular level, circadian rhythms are regulated by a 24-hour transcriptional–translational feedback loop. At the SCN tissue level, individual SCN neurons coordinate their gene expression and electrical activity, working together in circuits that sustain coherent rhythms. The SCN has many distinct cell types based on their neurotransmitters, neuropeptides, and afferent and efferent connections. There has been much progress in unraveling the dynamic network organization that underlies the SCN network’s communications. Though the precise anatomical connections underlying interneuronal communication in the SCN are not completely understood, key signaling mechanisms that sustain the SCN’s intrinsic rhythmicity have been tackled using intersectional genomic tools. Transgenic animals that permit the visualization of clock gene–protein expression have enabled analysis of SCN network activity over time. Availability of animals bearing mutations in clock genes or proteins enable the determination of changes within neurons, among neurons in networks, and their impact on behavior. The use of continuous readouts of circadian activity that track behavior, or clock gene expression, or electrical activity changes over time, within an SCN or a single neuron, leads the way to unraveling mechanisms sustaining the circadian timing system. Because the results of circadian studies generate huge amounts of data, the entry of mathematical modelers and statisticians into the field has begun to yield useful and testable predictions on how these multiplexed systems work to adapt to our 24-hour world.

Keywords: clock cells, oscillators, circadian and diurnal rhythms, networks, core and shell suprachiasmatic nucleus

Introduction

Organisms live in environments that change predictably over the course of the day and night. We are aware of some of these changes and we know of some of our adaptations. Of course, we are aware that we sleep and wake each day. We are less aware that hormone levels in our bodies fluctuate on a daily basis and reach peak plasma levels at specified times; for example, cortisol peaks in the early morning just before we awaken. These and many other physiological and behavioral daily rhythms depend on an internal circadian clock, located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN brain clock, by sending signals to other intrinsic timekeeping mechanisms in cells throughout our bodies, allows the anticipation of predictable daily events. These brain and bodily timers serve as biological clocks that have an intrinsic, self-sustaining period of approximately 1 day (hence circadian).

Circadian timing is a fundamental property of life. The ubiquitous expression of circadian rhythms provides many ways for studying these clocks. As a result, circadian timing now stands out as a paradigm for the explanation of how apparently complex patterns of behavior and physiology can arise from the properties of a relatively small number of genes and gene products, operating in single cells. An appreciation of the anatomy, physiology, and neurochemistry of this master clock is piqued by the realization of the myriad functions it serves (Antle & Silver, 2016). The SCN sets the phase of rhythms of cellular activity within tissues and organs throughout the body; it determines peak and trough times of hormone secretion; and it ensures that these activities are all correctly synchronized relative to each other and to local solar time. Also, it enables anticipation of events that occur at predictable and regularly recurring times of the day. Our bodies are able to anticipate upcoming needs so that we are physiologically ready to use time-limited resources as soon as they become available. As one example, the “hunger” hormone ghrelin is secreted from oxyntic cells of gastric glands, thereby increasing appetite in advance of a regularly timed meal. In summary, the SCN clock keeps us synchronized to local time, signals the correct phasing of clocks within cells throughout the body, and enables anticipation of, and preparation for, upcoming events. When these rhythms are correctly phased, we experience well-being and good health. When they are disrupted, as in jet lag and other states such as sleep deprivation and illness, we feel poorly.

Landmarks in Discovery of the SCN as a Circadian Clock

The analysis of circadian rhythms and the development of key concepts in the field had a long and slow trajectory (for review, see Daan, 2010). In fact, in recognition of the importance of this system, the 2017 Nobel Prize in Physiology or Medicine was awarded to Drs. Hall, Rosbash, and Young for research that elucidated the molecular mechanisms controlling circadian rhythms. Their pioneering work was in Drosophila and it pointed to the internal oscillators found in virtually all organisms, which synchronize cellular metabolism and organismal behavior to the light-dark cycle, with a 24-hour periodicity.

Early research was focused on determining whether there existed any internally organized (circadian) rhythms, as opposed to daily rhythms driven by exposure to environmental signals like light-dark cycles. Eventually, it became clear that 24-hour rhythms continued in the absence of any external cues and were endogenously organized—within the body. Today, the evidence for a brain clock in the SCN is very robust and is based on decades of work and many different types of experimental methods. First, it was known that daily light-dark cycles synchronize activity to the local environment. This information was an important clue in the search for the clock’s location, as photic information can only reach the mammalian brain through the retina. Autoradiographic tract tracing from the retina pointed to the termination of a retinohypothalamic tract (RHT) in the SCN, the small nucleus overlying the optic chiasm. Very shortly thereafter, lesion studies showed that behavioral and hormonal rhythms were abolished when the SCN was destroyed. Subsequent work using markers of circadian change such as rhythms in glucose utilization and in electrical activity, both in vitro and in vivo, indicated that these oscillations were intrinsic to the SCN tissue and were independent of information from the rest of the brain.

While this work proved that the SCN tissue behaved as a circadian clock and produced self-sustained rhythms even when isolated from the rest of the brain and body, it remained to be proven that it provided timing information to the body. This step was first shown with transplants of fetal SCN into the third ventricle of SCN-lesioned rats or hamsters that themselves were arrhythmic. Such transplants were highly effective in restoring rhythmicity to arrhythmic animals, as long as the transplanted tissue contained the SCN, but not with transplants of other tissue types (Lehman et al., 1987). Subsequent work showed that the period of the restored rhythm in SCN-lesioned animals was determined by the period of the grafted donor tissue (Ralph, Foster, Davis, & Menaker, 1990). These findings proved that the SCN is necessary and sufficient to sustain circadian activity rhythms (for further reading, see Weaver, 1998 and Silver & Kriegsfeld, 2014).

SCN Anatomy, Afferents, and Efferents

A first step in understanding how an SCN brain clock might work is the delineation of information it receives via afferent inputs to the nucleus and what output it sends via efferent signals. There are three monosynaptic inputs to the SCN. The primary afferent input to the SCN is the dense retinohypothalamic innervation from the RHT (Morin & Allen, 2006). The remaining two are the inputs from the intergeniculate leaflet (IGL) and afferents from the median raphe nucleus. Each of these three monosynaptic inputs is most dense in the core of the nucleus, and less dense in the shell (Figure 1) (Abrahamson & Moore, 2001; Lokshin, LeSauter, & Silver, 2015). As discussed in a thorough examination of the extended circadian system, if multisynaptic afferents to the SCN are considered the numbers of brain areas providing input to the nucleus expands to 85 (Morin, 2013). This indicates that feedback from other components to the brain clock can derive from many sources and can contribute to its circadian timekeeping mechanisms.

Evidence of functional specialization of core and shell can be seen in studies of simulated travel across time zones. Many studies show that following a phase shift, the changes in gene expression in the SCN core precede those in the shell (Silver, LeSauter, Tresco, & Lehman, 1996; Dardente, Klosen, Caldelas, Pevet, & Masson-Pevet, 2002; Yan & Okamura, 2002; Yan & Silver, 2002; Kuhlman, Silver, LeSauter, Bult-Ito, & McMahon, 2003; Nagano, Adachi, Nakahama, Nakamura, & Tamada, 2003). Restated, the phases of the clock genes shift rapidly in the photoreceptive core region of the SCN, and more slowly in the shell region. For example, after a 10-hour delay in the light-dark cycle, resynchronization of clock neurons required approximately 6 days, and behavioral analysis indicated that this was accompanied by the parallel reemergence of normal rest–activity patterns. The slow resynchronization of SCN shell oscillators points to a mechanism that underlies the symptoms of jet lag (Nagano et al., 2003).

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Figure 1. Retinal input to SCN seen in sagittal section.

Consistent with the idea that clock neurons are specialized, light exposure during the night typically increases SCN electrical activity and gene expression (e.g., c-fos), but does so only in a subset of neurons. Of those SCN neurons affected directly by retinal stimulation, some respond with sustained increases in electrical activity, some respond with phasic responses, and others are inhibited. A few studies have tried to identify the electrical activity of specific peptidergic phenotypes of neurons that respond to retinal stimulation. For example, SCN neurons containing gastrin-releasing peptide (GRP), lying in the SCN core, receive direct retinal input via the RHT. In a study using whole-cell patch clamping, exposure to light induced a short-term increase in GRP neuron excitability, followed by a long-term decrease in excitability that lasted about 4 hours. None of these effects was observed in immediately adjacent non-GRP neurons whether exposed to light or not. Thus, exposure to light appears to induce cell-type specific responses (LeSauter, Silver, Cloues, & Witkovsky, 2011).

With regard to SCN efferents, classical work determined that most monosynaptic efferents of SCN neurons terminate in nearby hypothalamic nuclei (see reviews in Klein, Moore, & Reppert, 1991; Kriegsfeld, Leak, Yackulic, LeSauter, & Silver, 2004). From these nearby nuclei, signals are transmitted to the rest of the brain. Analysis of clock gene expression in brain regions outside the SCN indicates that the expression of key clock genes follows a diurnal rhythm in all regions studied. The phase and amplitude of the rhythms of each gene, however, vary across regions, pointing to local differences in temporal dynamics underlying daily rhythm generation in the mammalian forebrain. The findings raise interesting questions about the dynamic connections of these neuronal oscillators with those of the master SCN clock (Harbour, Weigl, Robinson, & Amir, 2014).

An insight into the origin of efferent SCN signals and when they send timing information to their monosynaptic targets comes from work on hamsters. When they are placed in constant light, approximately half the animals held in constant light will divide each 24-hour day into two 12-hour days. In lovely work, it was shown that in behaviorally split hamsters that live in 12-hour days, the right and left SCN oscillate 12 hours out of phase with each other, and the twice-daily locomotor bouts alternately correspond to one or the other SCN (de la Iglesia, Meyer, Carpino Jr., & Schwartz, 2000). Further work showed that the split hamsters’ SCN exhibited 24-hour rhythms of FOS protein (the product of c-Fos gene) that cycled in antiphase between left and right sides, and between core and shell subregions (Yan, Foley, Bobula, Kriegsfeld, & Silver, 2005; Figure 2).

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Figure 2. Illustration depicting split SCN.

The unique configuration of the split SCN, with a 4-way split between left and right and core and shell, is useful for investigation of SCN communication to efferent targets. This can be done by asking whether daily activity is associated with any specific configuration of the core and shell components of the SCN in split versus unsplit animals. To track phase and period in the SCN, wheel-running and FOS protein expression in the brains were studied in three groups of hamsters: control animals housed in a light-dark cycle, or split and unsplit animals housed in constant light (Butler, Rainbow, Rodriguez, Lyon, & Silver, 2012). Constant light induced a robust rhythm in the SCN core, regardless of splitting. In contrast, shell differed according to the behavior and photic condition of the animals. Importantly, in all animals, regardless of lighting conditions, split or unsplit, the onset of behavior occurred at a single, common phase reference point of the SCN oscillation. This point occurred just after the peak expression of FOS in the shell (Figure 3), which suggests that each SCN shell signals daily activity onset at the day to night transition daily, just after the time of peak activity in shell neurons (Yan et al., 2005; Butler et al., 2012).

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Figure 3. Time of activity onset in split and unsplit SCN in animals housed in light–dark (LD) cycles or constant light (LL).

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Figure 4. Hierarchy of clocks.

In summary, today we know that the SCN is the chief orchestrator for rhythms generated in the body. We also know that almost all cells in the body have clocks. Correspondingly, all major organs have their own circadian clocks and they constitute a hierarchical multioscillator network in which the SCN serves as the chief orchestrator dominating the rhythms in the peripheral clocks (Figure 4). The cloning of mammalian Clock genes (King et al., 1997) and Period genes (Sun et al., 1997; Tei et al., 1997) made possible the analysis of the hierarchical organization of the circadian timing system as well as molecular and biochemical mechanisms underlying circadian oscillations. For example, the dominance of the SCN clock over peripheral oscillators was demonstrated using grafts from animals with genetically altered period lengths by Pando, Morse, Cermakian, and Sassone-Corsi (2002). Mouse embryo fibroblasts from Per1(-/-) mice have a much shorter period in culture than do tissues in the intact animal. Mouse embryo fibroblasts were surgically implanted in mice of different genotypes. When implanted back into wild-type mice with approximately 24-hour rhythms, the Per1(-/-) mouse embryo fibroblasts took on the rhythmic characteristics of the host.

We also know that the SCN is comprised of about 10,000 neurons per nucleus, and that individual neurons bear circadian oscillators. Several “clock genes” have been identified in interacting transcriptional–translational feedback loops that comprise this clock within individual neurons. When these SCN neurons are appropriately synchronized, they produce an output that regulates the phase of clock cells throughout the body and ultimately sets the timing of overt rhythms. At this point, several decades after its discovery, a great deal is known about these single-cell oscillators, the network organization of these oscillators, how the SCN sets its phase, and how it signals phase information to the rest of the body. The topic to be considered next is the nature of the genes, neurons, and networks that make up this brain clock, and how they can be understood in the context of clock neurons in the rest of the brain and the throughout the body.

Molecular Basis of Circadian Oscillation Within Individual Neurons and Cells

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Figure 5. Molecular oscillator.

Very importantly, cell autonomous oscillators occur not only in the SCN but also in most cells of the body. In fact, circadian oscillators in SCN neurons and in other neuronal and peripheral cell types are similar. In all loci, individual cells bear molecular oscillators consisting of two coupled feedback loops (Figure 5). In the first loop, generally termed the “canonical feedback loop,” protein complexes containing cryptochromes (CRY1 and CRY2), period (PER1 and PER2) proteins, and many additional polypeptides, bind to and repress activator complexes of the transcription factors CLOCK and BMAL1. CLOCK and BMAL stimulate transcription of Per and Cry genes, and when the PER–CRY protein complexes reach a critical concentration in the cytoplasm, they enter the nucleus and shut down their own expression. A secondary feedback loop, orchestrated by nuclear orphan receptors of the REV–ERB and ROR families, drives the rhythmic transcription of Bmal1 and Clock genes. Here, REV–ERBa and REV–ERBb repressors compete with RORa and RORb, for the binding to RORE elements in the Bmal1 and Clock promoters. Rev-erba and Rev-erbb are direct targets of CLOCK–BMAL1 complexes, so that the canonical and secondary feedback loops are tightly coupled. Both feedback loops occur in neurons and in peripheral cells, though there are some differences between these individual cell-based clocks.

Differences Between Clocks in SCN Neurons Versus Peripheral Cells

The vast majority of peripheral oscillators depend on SCN-derived signals to maintain sustained rhythms. This is due to a lack of strong coupling among cells in peripheral tissues compared with the tightly coupled neural network of the SCN. Another important difference between oscillators in SCN neurons and other neurons of the brain and in cells in the rest of the body is the pathways whereby they become synchronized to each other. In mammals, the retina is the only source of photic input and information traveling via the RHT provides information about light-dark cycles to the SCN. This photic signal is the major synchronizing cue for the SCN. The SCN, in turn, relays information via monosynaptic pathways to nearby hypothalamic regions and thence to the rest of the brain and via the autonomic nervous system to the adrenal glands. In contrast to inputs to the SCN, signals derived from feeding–fasting rhythms provide the major phase-setting cues for most peripheral cells and organs. Also, glucocorticoids from the adrenal glands, and body temperature rhythms can influence the synchronization of peripheral clocks. Unlike peripheral cells, the SCN is not very sensitive to feeding–fasting rhythms, temperature cycles, or glucocorticoid signaling cues that affect peripheral oscillators (Balsalobre et al., 2000; Damiola et al., 2000; Buhr, Yoo, & Takahashi, 2010). These differences in synchronizing signals are one component of SCN uniqueness.

SCN Anatomy and Neurochemistry

Classically, the SCN has been divided into two major compartments, termed core and shell, or ventral and dorsal, respectively. In general, it is thought that core neurons communicate phase-setting information from photic cues to the neurons of the shell (reviewed in Moore & Silver, 1998; Abrahamson & Moore, 2001; Antle & Silver, 2005). These topological distinctions are based on differences in peptidergic cell types between these regions, and differences in their afferent inputs and efferent outputs. Many of the major peptidergic cell types are not scattered throughout the nucleus, but are found in clusters (Figure 6). Among the best understood peptides of the SCN are vasoactive intestinal polypeptide (VIP) and gastrin-releasing peptide (GRP) occupying the core and arginine vasopressin (AVP) in the shell. Interestingly, the expression and location of AVP and VIP-containing neurons of the core and shell are consistent across many mammalian species, suggesting a stable and necessary feature of the network. In contrast, many other chemoarchitectural neuronal types, generally lying in the region between core and shell or scattered among the VIP and AVP neurons, differ among species, presumably serving species-specific functions (Cassone, Speh, Card, & Moore, 1988; Smale, Blanchard, Moore, & Morin, 1991; Morin, 2007). While species variations in the SCN presumably reflects unique aspects of responses to the environment, this feature has not been much studied at the mechanistic level (but see van der Veen et al., 2006; Bloch, Barnes, Gerkema, & Helm, 2013). However, much is known of the functions of the major core and shell peptides.

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Figure 6. Peptidergic diversity of neurons in the adult SCN.

VIP and GRP are both core peptides; VIP increases over the course of the dark period, whereas GRP immunoreactivity increases during the light period (Shinohara, Tominaga, Isobe, & Inouye, 1993). GRP neurons are directly retinorecipient. The effects of light were studied to characterize mechanisms controlling GRP (Francl, Kaur, & Glass, 2010). In hamsters housed under a 14:10-hour light-dark cycle, GRP exhibited daily fluctuations of release, with levels increasing during the morning and peaking around midday. Neuronal release of the peptides was confirmed by their suppression with reverse-microdialysis perfusion of calcium blockers and stimulation with depolarizing agents. Perfusion with the glutamate agonist NMDA, or exposure to light at night, increased GRP but did not affect AVP. These analyses reveal distinct daily rhythms of SCN peptidergic activity, with GRP activated by glutamatergic and photic stimulation that mediate phase resetting.

A great deal of work has shown that VIP, acting via its receptor VPAC2, is a key signaling pathway in the SCN, controlling daily rhythms. VIP neurons in the core are important for light entrainment and internal synchronization of the SCN. Disruption of VIP signaling in VIP knockout mice (KO) or in those lacking, VPAC2, reduces coherence of overt circadian rhythms, presumably because of reduced coupling among neurons (reviewed in Harmar et al., 2002; Colwell et al., 2003; Aton, Colwell, Harmar, Waschek, & Herzog, 2005; Maywood et al., 2006; Maywood et al., 2007; Vosko, Schroeder, Loh, & Colwell, 2007). Although the evidence systematically points to an important role for VIP and its receptor, it is unknown how this interneuronal coupling is achieved by VIP.

Another aspect of the work on VIP is the close relationship between behavior and the quality of rhythmic expression in the SCN within individual animals (Brown, Colwell, Waschek, & Piggins, 2007; Ciarleglio et al., 2009). Most mice lacking VPAC2 receptors do not sustain behavioral rhythmicity and lack detectable SCN electrical rhythms in vitro. Adult mice that do not produce VIP (VIP/PHI−/−) exhibit less severe disruptions of locomotor activity. SCN neurons from VIP KO mice exhibit a broad range in the period and phasing of electrical rhythms, concordant with the alterations in their behavioral rhythmicity. Using ex vivo real-time gene expression, imaging of the SCN rhythms and behavioral analysis of locomotor activity indicate that the degree of disruption of SCN neuronal organization correlates with the extent of behavioral disruption within individual animals. Taken together, the results indicate that VIP is important for normal period and phase of SCN neuronal rhythms.

The functional significance of AVP neurons in the shell has also been examined, and there is substantial interest in understanding which axonal projections from the central clock drive various behavioral rhythms. In one example, a neurocircuit from SCN AVP neurons to paraventricular nucleus (PVN) oxytocin (Oxt) neurons relays photic information inhibiting daytime feeding in rats (Santoso, Nakata, Ueta, & Yada, 2017). In this study, during the night, light exposure acutely suppressed food intake and elevated c-Fos expression in the AVP neurons of the SCN shell and the Oxt neurons of the PVT. Furthermore, light-induced suppression of food intake was abolished by blockade of the Oxt receptor. This circuit from SCN AVP neurons to the PVN serves as a pathway for circadian feeding rhythms, ensuring that eating behavior occurs at an appropriate circadian time.

Mice increase their water intake prior to sleep, a response signaled by AVP neurons of the SCN and not necessarily by physiological need (Gizowski, Zaelzer, & Bourque, 2016). This behavior ensures that they are hydrated through their sleep time. The SCN AVP neurons project to thirst neurons in the organum vasculosum lamina terminalis (OVLT). The AVP neurons become electrically active in anticipation of sleep and excite OVLT neurons through the activation of postsynaptic V1a receptors and downstream nonselective cation channels. Induction of AVP release before the anticipatory period prompts a surge in water intake, while inhibition of AVP release eliminates the increase in water intake. In summary, anticipatory thirst behavior is driven by timed excitatory peptidergic neurotransmission via AVP release from SCN neurons.

Though the work on AVP and VIP points to important effects of these two peptidergic cell types, it would be useful to know the functions of other peptidergic SCN neurons and whether all cell types in the SCN are known. A detailed quantitative analysis of SCN neuronal types in rat indicates that approximately 37% are AVP-positive, 24% are VIP-positive, calretinin and GRP-containing neurons are each about 14% of the total, while the other peptides studied (NT, ENK, SS SP) each make up less than 5% of the population (Moore, Speh, & Leak, 2002). Many SCN neurons express more than one peptide, providing further evidence of the heterogeneity of the nucleus. For example, in mouse and rat, a subset of SCN neurons are VIP&GRP-positive in mouse some neurons are VIP&NMS-positive, others are AVP&NMS-positive, and in rat angiotensin II neurons are all AVP-positive (Okamura et al., 1986; Albers, Liou, Ferris, Stopa, & Zoeller, 1991; Moore et al., 2002; Lee et al., 2015). While VIP, AVP, and to some extent GRP-containing neurons are the most intensely investigated, many additional peptides have been isolated from SCN tissue (Hatcher et al., 2008) and the functions of a very few of them have been studied. For example, neuromedin (NMS) is contained in a subset of SCN neurons; blocking vesicular transmission from NMS neurons disrupts circadian timing, suggesting action through intercellular synaptic transmission (Lee et al., 2015). Another peptide, called Little SAAS, is expressed in approximately 16% of SCN neurons (Atkins et al., 2010). Little SAAS is localized within the densely retinorecipient SCN region in both rat and mouse and sometimes colocalizes with neurons containing VIP or GRP, but not AVP. About 50% of little SAAS neurons express c-FOS following exposure to light in early night.

Immunochemical staining methods permit the identification and localization of preselected peptidergic cell types and their changes in expression levels. However, studies using high-throughput peptidomic-based quantification reveal a large number of previously unknown endogenous SCN peptides that exhibit different expression levels at various times of day (Southey et al., 2014). For example, one study revealed 102 endogenous peptides, of which 33 were previously unidentified (Lee et al., 2010).

In addition to these peptides, a vast majority, or perhaps all SCN neurons synthesize the inhibitory neurotransmitter, γ‎-aminobutyric acid (GABA) and there is an extensive GABAergic plexus throughout the SCN. Also, GABA receptors are ubiquitous throughout the nucleus, enabling neurons to respond to changing levels of extracellular GABA. Although research on the role of GABA in the SCN is many decades long, much remains poorly understood. The known uncertainties have been carefully documents (see Albers, Walton, Gamble, McNeill, & Hummer, 2017). The impression one has is that a key element of GABAergic actions is missing from our information as the cellular effects of GABA have been inconsistent, even when researchers have used very similar methods. There is some suggestion that one of the problems lies with the heterogeneity of SCN neurons (often the cell type being studied is unknown) along with their changing responses as a function of time of day. As an example of the complexity, it has been suggested that GABA may be inhibitory throughout the SCN during the day, but at night, it is inhibitory in the ventral core while excitatory in the dorsal shell (for details, see Albers et al., 2017). Needless to say, the inability to identify the phenotype of the neurons under study, and their unknown connections with other SCN neurons and glia, constrains the interpretation of the GABAergic effects.

Electron microscopic studies of rat SCN in the 1980s (Guldner, 1983) indicated that astrocytes are about one third as numerous as neurons in the nucleus. Astrocytes are historically known for playing a supporting role for neurons but now they are also recognized for their role in “gliotransmission” (Halassa, Fellin, & Haydon, 2007) and for being important participants in circadian timing. There are marked circadian variations in glial morphology in the SCN, revealed by immunolabeling for glial fibrillary acidic protein (Lavialle & Serviere, 1993). It seems that astrocytes, like nearly all mammalian cells, exhibit circadian rhythmicity (Prolo, Takahashi, & Herzog, 2005).

There has been substantial progress in figuring out how astrocytes contribute to circadian timing. In one study, elimination of Bmal1 expression specifically in SCN astrocytes result in a lengthened circadian period of the SCN studied ex vivo in a slice preparation and in locomotor behavior of the mouse. Similarly, excision of the CK1ε‎ tau mutation from SCN astrocytes results in lengthening of the period of SCN rhythmicity and locomotor behavior. These results point to the importance of astrocyte–neuron communication on the period of the SCN and the consequences of SCN oscillation on output signals that reach the body (Tso et al., 2017). In another study of the contribution of astrocytes, imaging of SCN in brain slices was used to simultaneously detect circadian rhythms in SCN neurons and astrocytes (Brancaccio, Patton, Chesham, Maywood, & Hastings, 2017). The results confirm the well-established finding that neuronal activity is high during the circadian day and reveal the novel discovery that astrocytic activity is high during circadian night. In the night, astrocytes suppress the activity of SCN neurons by regulating extracellular glutamate levels. Importantly, these findings suggest that extracellular glutamate secreted by astrocytes serves as a synchronizing agent and that astrocytes are part of the circadian circuit that regulate daily behaviors. The discovery of an important role for astrocytes marks a new direction in understanding signaling in SCN circuit-level timekeeping.

Connectome of the SCN

The analysis of the interneuronal network of the SCN has proved elusive. SCN neurons are very small, densely packed, and of many different peptidergic phenotypes; this makes it difficult to explore the connections among neurons. As noted, the most commonly used model of SCN organization highlights functional distinctions between core and shell compartments. But in order to understand how neurons in these different compartments communicate, it is essential to know how they are interconnected. Some work has been done in mouse. VIP and GRP neurons of the core region send fibers to the dorsal SCN region, and AVP neurons of the shell region send fibers to most of the SCN (Figure 7, Drouyer, LeSauter, Hernandez, & Silver, 2010; Enoki et al., 2017a). In the rat SCN, analysis by confocal microscopy of the main neuronal types indicates dense, reciprocal innervation between AVP-, somatostatin-, VIP/PHI-, GRP-, and VIP/PHI/GRP-immunoreactive neurons (Romijn, Sluiter, Pool, Wortel, & Buijs, 1997). A similar map of connections among the peptidergic cell types of the mouse would be useful given the greater availability of genetic tools available for studies in the mouse.

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Figure 7. SCN connectome.

Work in hamsters and rats add a temporal dimension to this interneuronal communication. In hamsters, calbindin-D28K (CalB)-containing neurons form a distinct cluster within the core of the SCN, and the CalB neurons receive monosynaptic input from the RHT (LeSauter & Silver, 1999). Speaking to the temporal domain, there is a circadian rhythm of CalB-immunoreactivity in fibers of these neurons. There are many more appositions between CalB fibers and VP cells during the night (approximately 64%) than during the day (approximately 0), suggesting that these cells relay photic information to AVP oscillator cells of the SCN shell in a temporally gated manner in hamster (LeSauter, Bhuiyan, Shimazoe, & Silver, 2009). Similarly, a rhythm in another calcium-binding protein, calretinin (CAR), has been reported in rats, with prominent labeling of dendrites at night and fewer in the day. Here too, it seems that CAR translocation occurs from cell body to dendrites (Moore, 2016). The changes in the location of cytoplasmic calcium-binding proteins suggest a mechanism for modulating the timing of local cellular calcium function in synaptic transmission.

Dynamic Network Organization: Cells, Circuits, and Spatial Location

The availability of new genetic tools enables detection of changes in time in specific cells, groups of cells, and networks in a way never before possible. More specifically, markers are available for tracking gene or protein expression using Ca+2 flux. Such tools confirm that individual SCN neurons are self-sufficient cellular clocks. When dispersed, individual SCN neurons display different period lengths. In contrast, when SCN neurons communicate within a network, period synchrony is sustained by intercellular signaling.

Normally, SCN clock neurons coordinate with each other in time and space to form a cohesive population. Surprisingly, coordination does not mean that neurons are all synchronous in their activity, but instead individual neurons adopt specific stable phase relations to each other (Yamaguchi et al., 2003). SCN neurons from distinct regions or peptidergic subclasses can exhibit different period length (Shinohara & Inouye, 1995; Nakamura, Honma, Shirakawa, & Honma, 2001; Noguchi & Watanabe, 2008; Myung et al., 2012). Furthermore, rhythms of electrical activity can differ markedly among neuronal subgroups (Jobst & Allen, 2002; Saeb-Parsy & Dyball, 2003; Belle, Diekman, Forger, & Piggins, 2009).

To fully understand when various SCN neurons are when at rest, or while sending output signals to their efferent targets, we need a great deal of information at several levels of analysis and at various temporal scales. Analysis can be done at the behavioral and physiological level of the whole organism and at single cell resolution and on the scale of the SCN as a whole. We would also like to follow minute-to-minute, hourly, and daily time scales, as well as to follow the changes that occur over several days. This challenge is being confronted using mathematical analysis and modeling tools (Pauls, Honma, Honma, & Silver, 2016) in conjunction with molecular biological manipulations. Also, advances in imaging allow the resolution of low-intensity signals and permit the recording of neuronal activity changes using reporters for Ca2+ ions, clock gene or protein expression, and measurement of membrane potential in the SCN. Initial marvelous work using these tools was applied one method at a time and derived one measure of the state of SCN neurons. For example, changes in clock properties were analyzed using real-time fluorescent or bioluminescent monitoring of SCN slices from transgenic rodents bearing Per1-luc, Bmal1-luc or PER2:LUC (for discussion of methods, see Herzog, Kiss, & Mazuski, 2015). In brain slices taken from these animals studied in vitro, reporter gene expression reflecting endogenous clock properties could be tracked for days. Generally, the circadian period length revealed in SCN slices correlates tightly with behavioral period length (Liu, Weaver, Strogatz, & Reppert, 1997; Yoo et al., 2004; Myung et al., 2012). The topology of SCN networks examined with these tools points to substantial plasticity and reveals important network dynamics that point to the basis of period and phase changes in the clock. For example, the molecular clocks of SCN neurons change their phase in a region-specific manner after changes in the light-dark cycle (Nagano et al., 2003; Nakamura, Yamazaki, Takasu, Mishima, & Block, 2005; Sellix et al., 2012; Evans, Leise, Castanon-Cervantes, & Davidson, 2013). Network analysis also enables understanding of the effects of genetic mutations. Thus, in SCN slices harvested from PER2::LUC wild-type (WT) mice, stable spatially organized clusters of cells, identified by their synchronous activity, are seen repeatedly over a 24-hour period (Foley et al., 2011). In movies of these clusters, waves of luciferase expression from identifiable clusters of cells appear to travel through the SCN in a stable and orderly repeating fashion, indicating that a complex phase relationship exists among clusters of neuronal oscillators, when functionally coupled (Figure 8; Foley et al., 2011; Evans et al., 2013). These results suggest that tracking the spatial location of neuronal activity in the nucleus, and being able to characterize these change over time, is necessary for understanding the networks that drive oscillation within the SCN (Park et al., 2016).

In contrast to slices taken from wild-type conspecifics, those harvested from VIP receptor knockout animals in which individual SCN neurons are rhythmic, the tissue is disorganized spatially but is nevertheless rhythmic. However, in slices from adult CRY knockout mice where individual cells lack rhythmicity the tissue is spatially organized but lacks circadian rhythmicity. Taken together, the evidence from the two types of mutant mice shows that spatial and temporal organization can be dissociated, and that both are necessary for sustained and robust rhythmicity. These results highlight the power of linking spatial and temporal organization for understanding oscillation in networks. The historical contributions and future potential of mathematical models and statistical analyses for understanding the circadian oscillation has been described. The potential of using large data sets provided by analysis of oscillation measured in several different domains simultaneously has been demonstrated repeatedly in the history of this field (reviewed in Pauls et al., 2016).

Suprachiasmatic Nucleus Anatomy, Physiology, and NeurochemistryClick to view larger

Figure 8. Oscillation in SCN from wild-type and mutant mice.

Substantial interest exists in understanding the relationship between activity of individual neurons and the network as a whole using genetic tools. Importantly, there has been much progress in using multiple analytic tools simultaneously for studying oscillation. An example of this research direction is lovely work describing the relationship of voltage rhythms to Ca2+ rhythms and their different responses in core and shell compartments (Enoki et al., 2017a). Like all other neurons, those in the SCN have voltage changes that are controlled by the release of Ca2+ ions. Using a voltage sensor encoded into the DNA of mouse neurons, it is possible to measure the voltage changes in VIP and in AVP neurons over many days. In the same preparation, it is possible to track changes in Ca2+. Most surprisingly, in this brain slice preparation, the voltage rhythms are synchronized across the entire SCN, although simultaneously recorded Ca2+ rhythms are unique to the core and shell regions. Specifically, the Ca2+ rhythms phase-lead the voltage rhythms in shell AVP neurons, and in contrast, Ca2+ and voltage rhythms are nearly in phase in core VIP neurons. Taken together, these results indicate the surprising conclusion that SCN networks with asynchronous Ca2+ rhythms in VIP core and SCN shell neurons produce coherent voltage rhythms in the network as a whole. This work is immensely useful in delineating core and shell functions and properties, but is not a full explanation. The role of the remaining cell types and their peptides and transmitters lying within core and shell has not yet been worked out.

Linking Electrical Activity of Neurons to Molecular Cellular Rhythms

We know that signaling among neurons is based on electrochemical communication and thus the relationship of molecular rhythms to electrical activity within individual cells is key to understanding the nature of self-sustained oscillation in SCN tissue. Some spectacular work has been done using very-high-resolution imaging to simultaneously explore various electrical and clock gene changes.

Calcium ions are important for intracellular signaling. Once Ca2+ enters the cytosol, either from the extracellular space or from the intracellular Ca2+ stores, it exerts a variety of effects on various cell functions. Increases in intracellular calcium levels have been implicated in the link between electrical and molecular changes because it opens voltage-gated calcium channels and results in further release of calcium from intracellular stores (Allen, Nitabach, & Colwell, 2017).

The relation of Ca2+ changes to electrical and PER expression was tracked simultaneously in a single slice by monitoring intracellular Ca2+ levels using a genetically encoded Ca2+ sensor (GCaMP6s17), for PER2 expression by using a bioluminescent reporter, and spontaneous electrical firing was recorded by a multielectrode array dish (Enoki et al., 2017b). In the wild-type SCN, robust circadian rhythms of Ca2+, PER2, and firing were detected in all regions of the SCN examined within single SCN slices. The circadian peak of the Ca2+ rhythm was phase-advanced by approximately 6 hours relative to that of the PER2 rhythm. The circadian peak of the firing was phase-advanced by about 2.2 hours relative to the PER2 rhythm and it occurred between Ca2+and PER2.

The peak time of Per mRNA and of high electrical firing rates occurs at six hours after lights on (CT06) (Gillette et al., 1995; Field et al., 2000). Use of genetically encoded reporters indicates simultaneous peaks of cytosolic Ca2+ and electrical activity. The cytosolic Ca2+ rhythm is abrogated when action potentials are blocked by TTX (Enoki et al., 2012; Brancaccio, Maywood, Chesham, Loudon, & Hastings, 2013). Similarly, TTX application eliminates synchronized Per rhythms in SCN tissue. Finally, the regulatory elements of the Per genes have calcium-response elements (CREs) that may mediate the effect of the Ca2+ rhythm on Per transcription (Travnickova-Bendova, Cermakian, Reppert, & Sassone-Corsi, 2002). CRE-dependent gene expression peaks after the daily surge in Ca2+ and prior to peak Per1 expression. These observations show that Ca2+ signaling plays a critical role in maintaining the molecular clockwork.

To summarize, the relationships between rhythms in gene expression, calcium, and membrane conductance are a current topic of research in circadian biology. The unraveling may be complex as there may be feedback between changes associated with clock gene expression and other responses such as calcium ions. Finally, the problem of understanding the sequence of events is compounded by the different time course of change in membranes (milliseconds), calcium ions (minutes), and clock genes (hours).

Conclusion

This article focuses on the anatomy and neurochemistry of the SCN as a window into understanding the ways in which the brain clock regulates physiology and behavior on a daily basis. The story to date shows how an apparently simple function—namely, adjustment to daily changes in light-dark cycles—is orchestrated by a small bilateral hypothalamic nucleus comprised of about 20,000 neurons and about one third as many astrocytes. The story to date also shows how the path to discovery is thrown back and forth between behavioral analyses, cellular–molecular work, and mathematical analyses.

Why is this work important? Where does it lead? The implications of circadian rhythms point to an optimal time for each of our many biological processes. Good alignment of various circadian rhythms in our bodies is necessary for optimal health. Disruptions, such as those seen in jet lag and various states of sleep deprivation, are associated with suboptimal function and poor health (for further reading on this topic, see Karatsoreos & Silver, 2016). Also, it has been documented that many of the most useful drugs available have targets in the circadian system. RNA-seq and DNA arrays were used to quantify the transcriptomes of 12 mouse organs over time. A total of 43% of all protein-coding genes showed circadian rhythms in transcription somewhere in the body (Zhang, Lahens, Balance, Hughes, & Hogenesch, 2014). In most organs, oscillating genes peaked around dawn and dusk. Importantly, the majority of best-selling drugs and World Health Organization essential medicines directly target the products of rhythmic genes, many of which have short half-lives and might benefit from timed dosage. In fact, an entire field of chronotherapeutics is devoted to optimal timing for the administration of various drugs (Ballesta, Innominato, Dallmann, Rand, & Levi, 2017). Timing is everything, and optimal circadian timing in our various bodily tissues is a key aspect of well-adapted physiology and behavior.

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