CENTRAL AND PERIPHERAL CIRCADIAN CLOCKS IN MAMMALS

Suprachiasmatic nucleus

The SCN itself is composed of ~20,000 neurons, each of which is thought to contain a cell autonomous circadian oscillator. The SCN functions as a network in which the population of SCN cells are coupled together and oscillate in a coherent manner (Herzog 2007). The dynamics of the spatial and temporal coordination of rhythms in the SCN have been studied recently with the advent of single-cell circadian reporter technology, which has revealed unexpected complexity in the temporal architecture of the nucleus (Evans et al 2011; Foley et al 2011; Yamaguchi et al 2003). At the single-cell level, SCN neurons exhibit a wide range in cell autonomous circadian periods that vary from 22 to 30 hours (Ko et al 2010; Liu et al 1997; Welsh et al 1995). Intercellular coupling among SCN neurons acts to mutually couple the entire population to a much narrower range that corresponds to the circadian period of the locomotor activity rhythm which is extremely precise (a standard deviation in period that is ~0.2 hrs or 12 min in mice) (Herzog et al 2004). The heterogeneity in intrinsic period of the SCN cells confers at least two important functions: phase lability and phase plasticity. The phases of the rhythms of individual SCN neurons are highly stereotyped anatomically and appear as a wave that spreads across the nucleus over time (Evans et al 2011; Foley et al 2011; Yamaguchi et al 2003). Intrinsically shorter period cells have earlier phases and intrinsically longer period cells have later phases within the SCN – this is a reflection of phase lability (Yamaguchi et al 2003). Under different photoperiods (e.g., long vs. short photoperiod light cycles), the waveform of the SCN population rhythm is modulated such that in short photoperiods, the SCN waveform is narrow and high amplitude; whereas, in long photoperiods, the SCN waveform broad and low amplitude – this is a reflection of phase plasticity (Inagaki et al 2007; VanderLeest et al 2007). In addition to the heterogeneity of SCN oscillator period and phase, it has been proposed that the cell autonomous SCN oscillators are not uniformly robust intrinsically (Webb et al 2009). Rather, the intercellular coupling of SCN neurons appears critical to the robustness of the SCN network oscillatory system.

With the discovery of peripheral oscillators (Balsalobre et al 1998; Yamazaki et al 2000; Yoo et al 2004) and the apparent ubiquity of clock mechanisms (Yagita et al 2001), a critical question concerns the similarity and differences in the SCN pacemaker as compared to peripheral oscillators. To address this question, Liu et al. (Liu et al 2007a) examined whether canonical clock mutations previously assessed in vivo affected the SCN and peripheral oscillators in a similar manner. Using Per2::luciferase reporter mice, they found that the effects of the Period and Cryptochrome loss-of-function mutations were the same in SCN explants as that seen previously at the behavioral level. By contrast, in peripheral tissues, single loss-of-function mutations that are subtle at the behavioral level, such as Per1 or Cry1 knockouts, produced very strong loss-of-rhythm phenotypes. Interestingly, the effects of these mutations are cell autonomous in both fibroblasts and in isolated SCN neurons supporting the idea that the cell autonomous clock is similar in these two cell types. However, when the SCN population is coupled, the effects of these mutations are non-cell autonomous. This occurs as a consequence of the intercellular coupling in the SCN network, which is capable of rescuing a cell autonomous defect in the individual cells (Fig. 2). This transformation of the oscillatory capability of SCN neurons from damped to self-sustained is an important illustration of the robustness of the SCN network. Indeed, Ko et al. (Ko et al 2010) have found that the SCN network is capable of generating oscillations in the circadian domain in the complete absence of cell autonomous oscillatory potential. In Bmal1 knockout mice, which are arrhythmic at the behavioral level, SCN explants unexpectedly express stochastic oscillations in the circadian range that are highly variable. When the individual cells are no longer rhythmic, the coupling pathways within the SCN network can propagate stochastic rhythms that are a reflection of both feed-forward coupling mechanisms and intracellular noise. Thus, in a manner analogous to central pattern generators in neural circuits, rhythmicity can arise as an emergent property of the network in the absence of component pacemaker or oscillator cells.

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Figure 2

Network and autonomous properties of SCN neurons. Network properties of the SCN can compensate for genetic defects affecting rhythmicity at the cell autonomous level. A) Bioluminescence images of a Cry1^−/− SCN in organotypic slice culture. Note the stable, synchronized oscillations. Numbers indicate hours after start of imaging; 3V indicates the 3^rd ventricle. B) Bioluminescence images of dissociated individual Cry1^−/− SCN neurons showing cell-autonomous, largely arrhythmic patterns of high bioluminescence intensity. C and D) Heatmap representations of bioluminescence intensity of individual Cry1^−/− neurons in SCN slice (A) and dispersed culture (B). Values above and below the mean are shown in red and green, respectively, for 40 SCN neurons in each condition. E and F) Ten single SCN neuron rhythms from wild-type (E) and Cry1^−/− (F) mice. Imaging began immediately following a media change at day 0. Dissociated Cry1^−/− SCN neurons are largely arrhythmic, whereas dissociated wild-type cells are rhythmic. By contrast, in organotypic slice cultures, both wild-type and Cry1^−/− SCN cells are robustly rhythmic and tightly synchronized. Figure and legend adapted and reprinted from (Liu et al 2007a), with permission from Elsevier.

In addition to the generation of sustained oscillations by the SCN network, the SCN is also robust to perturbations from environmental inputs. In wild type mice, the phase resetting curve to light pulses is characteristic of Type 1 or weak resetting (low amplitude) (Vitaterna et al 2006). This is a reflection of the robustness of the SCN pacemaker because inputs such as light can only perturb the phase of the oscillation to a limited extent. In contrast, genetic mutations that lower the amplitude of the molecular oscillation in the SCN lead to increases in the sensitivity to light-induced phase shifts (Type 0 resetting) without changing the strength of the light signals impinging on the SCN (Vitaterna et al 2006). Similar effects are seen with temperature cycles. Peripheral oscillators are exquisitely sensitive to the phase shifting effects of temperature and can be entrained strongly by low amplitude temperature cycles that are equivalent to the circadian fluctuation in core body temperature (~2.5°C oscillation in mice) (Brown et al 2002; Buhr et al 2010). Interestingly, the SCN is resistant to entrainment by low amplitude temperature cycles and this resistance depends on the intercellular coupling of SCN neurons (Abraham et al 2010; Buhr et al 2010). As was the case for genetic mutations on the generation of rhythms, the effects of temperature perturbations on the SCN are cell autonomous when intercellular coupling is eliminated. Thus, both SCN and peripheral oscillators are sensitive to temperature cycles at the cell autonomous level; however, coupling within the SCN network confers robustness and makes the SCN network resistant to temperature perturbations. As discussed below, the temperature resistance of the SCN makes functional sense since the SCN drives the body temperature rhythm and this temperature signal, which can serve as an entraining signal for peripheral clocks, might then feedback and interfere with the SCN (Buhr et al 2010).

Behavioral and Homeostatic Regulation: Local cues feed back into the clock

In addition to controlling hormone secretion and body temperature directly, the SCN coordinates rhythms in behavioral processes, such as locomotor activity and feeding, which can influence endocrine function and body temperature. These behaviors, feeding in particular, can regulate peripheral clocks at the local level, modulating local signaling pathways and metabolic processes. Homeostatic signaling pathways also affect peripheral clocks and their function, allowing for extra-SCN control of circadian processes. Studies in which mealtime has been experimentally manipulated to occur antiphase to the normal SCN-driven feeding rhythm have attempted to elucidate local mechanisms for controlling clocks in peripheral tissues.

The liver clock, unlike the SCN, is particularly sensitive to resetting by feeding. Hepatic rhythms of clock gene and protein expression rapidly shift their phase to follow the timing of a scheduled meal (Damiola et al 2000; Stokkan et al 2001). Similarly, livers of Cry1/Cry2 null mice display rhythms in many transcripts (including a number of transcripts involved in metabolic processes) when fed in regular 24h intervals (Vollmers et al 2009). Feeding appears to result in cues that bypass the core circadian feedback loop to drive these rhythms. These cues may include feeding-induced changes in temperature and HSF1 activity (Kornmann et al 2007) or activation of other metabolically-sensitive pathways.

A number of local mediators of both core clock components and clock-controlled rhythmic transcripts have been identified, which can respond to SCN-driven inputs as well as local signals related to homeostasis and metabolic state. These include members of the nuclear receptor family of transcription factors, many of which exhibit circadian rhythms of transcription within the liver and other metabolically-relevant tissues (Yang et al 2006). These rhythmic nuclear receptors regulate transcription of downstream metabolic pathways. Among the rhythmic nuclear receptors are PPARs (peroxisome proliferator-activated receptors) and members of the REV-ERB and ROR families. As described above, RORα and REV-ERBα participate directly in the clock mechanism by regulating Bmal1 transcription (Preitner et al 2002; Sato et al 2004), but are also important for many aspects of metabolic regulation. Like REV-ERBs and RORs, many of the other rhythmic nuclear receptors are also regulators of clock function, providing a mechanism by which signals of metabolic status can influence rhythmicity. Glucocorticoid receptors, as discussed above, induce transcription of Per and potentially a number of other clock and clock controlled genes (Reddy et al 2007; So et al 2009; Yamamoto et al 2005a). PPARα, which responds to lipid status and glucocorticoids, may also regulate Bmal1 transcription (Canaple et al 2006).

PPARγ coactivator-1α (PGC-1α, a transcriptional coactivator) provides a link between changes in metabolic status and the clock. PGC-1α is critical for adaptive responses to nutritional and metabolic state, particularly following fasting [reviewed in (Lin et al 2005)]. PGC-1α is itself rhythmic, and activates expression of Bmal1 and Rev-erbα through coactivation of RORs (Liu et al 2007b). PGC-1α null mice display disruptions in a number of circadian outputs including locomotor activity, oxygen consumption rate, and expression of both clock and metabolic genes (Liu et al 2007b). PGC-1α also interacts with SIRTUIN 1 (SIRT1), a nicotinamide adenine dinucleotide (NAD)-dependent histone deacetylase (Rodgers et al 2005).

Another mechanism by which metabolic signals can feed into the clock is through the adenosine monophosphate-activated protein kinase (AMPK) (Bass & Takahashi 2010). This kinase is a central mediator of metabolic signals and its activity is robustly rhythmic in mouse liver and regulated by nutrient status, as reflected in the ratio of AMP to ATP. Active AMPK directly regulates the central clock mechanism by phosphorylating and destabilizing the clock component CRY1 (Lamia et al 2009).

Cellular redox state can also serve as a mechanism by which the metabolic status of the cell can impact the circadian system. NAD levels exhibit circadian oscillations in the liver, likely due to transcriptional regulation of nicotinamide phosphoribosyltransferase (Nampt, encoding the rate-limiting enzyme in the NAD⁺ salvage pathway) by CLOCK:BMAL1 (Nakahata et al 2009; Ramsey et al 2009). NAD levels also vary with cellular redox state as a consequence of metabolic changes and this, in turn, can directly impact clock function. The ratio of NAD⁺ to NADH influences binding of the NPAS2:BMAL1 and CLOCK:BMAL1 to DNA in vitro suggesting one way in which NAD could interact with clock components (Rutter et al 2001).

NAD⁺-dependent SIRT1 also displays daily oscillations and feeds back onto the circadian clock. SIRT1 forms a complex with CLOCK:BMAL1, leading to the deacetylation of PER2 (Asher et al 2008) and BMAL1 (Nakahata et al 2008). SIRT1 also suppresses CLOCK:BMAL1-mediated transcription, resulting in decreased expression of Per2 (Nakahata et al 2008; Ramsey et al 2009) and the clock-controlled gene Dbp (Nakahata et al 2008).

Another molecule with NAD⁺-sensitive activity, poly(ADP-ribose) polymerase 1 (PARP-1), was recently shown to interact with the circadian clock (Asher et al 2010). PARP-1 activity is rhythmic in the liver, and this rhythm persists even in the absence of a functional hepatic circadian clock. The rhythm of PARP-1 activity can be entrained by scheduled meals, however, suggesting that the circadian activity of PARP-1 is driven by feeding-related cues. PARP-1 interacts with CLOCK:BMAL1 in a rhythmic fashion and inhibits DNA binding by the CLOCK:BMAL1 complex. PARP-1 also polyADP-ribosylates CLOCK, and appears to temporally-regulate interaction of CLOCK:BMAL1 with PER2 and CRYs. The circadian regulation of PARP-1 by feeding, and subsequent consequences for the circadian clock, are likely not entirely mediated through NAD⁺. Regardless of the mechanism, PARP-1 provides another way for metabolic signals to influence timekeeping by the core molecular clock.

The Methamphetamine-Sensitive Circadian Oscillator (MASCO)

Chronic or scheduled methamphetamine treatment affects circadian outputs in a manner similar to food restriction (Honma & Honma 2009). Methamphetamine, provided in the drinking water of rats and mice, is capable of driving circadian rhythms of locomotor behavior in the absence of the SCN (Honma et al 1987; Tataroglu et al 2006). In SCN-intact animals, this appears as a lengthening of the free-running period of locomotor activity and in some cases with two activity components that are relatively coordinated with each other. Much like the FEO, these rhythms persist when the stimulus (in this case, methamphetamine) is withdrawn (Tataroglu et al 2006), as well as in the absence of a functional molecular clock (Honma et al 2008; Mohawk et al 2009). The MASCO is capable of functioning as a pacemaker driving rhythms in locomotor activity, body temperature, endocrine function, and the oscillators of peripheral tissues (Honma et al 1988; Pezuk et al 2010). In the presence of the SCN, methamphetamine results in desynchrony among internal oscillators, as some follow the SCN and some follow the presumed phase of the MASCO (Pezuk et al 2010). When the SCN is ablated, however, the MASCO organizes oscillators in tissues throughout the organism, resulting in a coordinated system (Pezuk et al 2010).

The site of the MASCO is also unknown. It is possible that the FEO and MASCO share an anatomical and mechanistic basis, or, indeed, that they represent a single oscillator. Research focused on understanding how the MASCO and FEO relay circadian information to peripheral tissues will likely uncover novel (or underappreciated) mechanisms of control of rhythms. The role of these oscillators in the absence of food restriction and methamphetamine must also be determined. It is unlikely that these oscillators are dormant under normal conditions; instead, it seems probable that the FEO, MASCO, and SCN cooperate in a hierarchically organized, perhaps necessarily redundant, timing network.