Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
Review
The trouble with circadian clock dysfunction: Multiple deleterious
effects on the brain and body
Erin L. Zelinski ∗ , Scott H. Deibel, Robert J. McDonald
Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
a r t i c l e
i n f o
Article history:
Received 7 July 2013
Received in revised form 7 January 2014
Accepted 16 January 2014
Keywords:
Circadian disruption
Circadian rhythms
Cognitive function
Disease
Health
Brain
a b s t r a c t
This review consolidates research employing human correlational and experimental work across brain
and body with experimental animal models to provide a more complete representation of how circadian
rhythms influence almost all aspects of life. In doing so, we will cover the morphological and biochemical
pathways responsible for rhythm generation as well as interactions between these systems and others
(e.g., stress, feeding, reproduction). The effects of circadian disruption on the health of humans, including
time of day effects, cognitive sequelae, dementia, Alzheimer’s disease, diet, obesity, food preferences,
mood disorders, and cancer will also be discussed. Subsequently, experimental support for these largely
correlational human studies conducted in non-human animal models will be described.
© 2014 Elsevier Ltd. All rights reserved.
Contents
1.
2.
3.
4.
5.
6.
Anatomy and gross morphology of the suprachiasmatic nucleus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mechanisms underlying circadian rhythmicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.
Molecular oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.
Neurotransmitters, peptides, and hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1.
Classic neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2.
Small molecule signaling and neuropeptides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3.
Hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.
Glucocorticoids and stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Peripheral and slave oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Perturbing the circadian system: consequences for health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.
Shift work (Fig. 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.
Aging and associated diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.
Obesity and diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.
The influence of chronotype and affective disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.
Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6.
Cardiovascular consequences and clock genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lessons from animal models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.
Manipulations of SCN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.
Constant illumination paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.
Shifting the LD cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effects on learning and memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.
Mechanisms for hippocampal effects (Fig. 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.
Models of diabetes, obesity and food preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3.
Reproductive effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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∗ Corresponding author at: Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, 4401 University Drive, Lethbridge, AB
T1K 6W4, Canada. Tel.: +1 403 394 3985; fax: +1 403 329 2775.
E-mail address: [email protected] (E.L. Zelinski).
0149-7634/$ – see front matter © 2014 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.neubiorev.2014.01.007
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
7.
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Most English speakers are familiar with the phase: the early
bird gets the worm. One strategy for getting the worm is to predict
when it will be available. Thus, the possession of a clock, particularly an alarm clock, would be highly advantageous. Conveniently,
endogenous timekeepers are present in virtually all living things
(Young and Kay, 2001). Relatively simple, single cell organisms
exhibit circadian rhythmicity, but individual oscillators in multicellular organisms must be coordinated. This master clock is the
suprachiasmatic nucleus (SCN) of the anterior hypothalamus in animal species that possess complex central nervous systems (CNS,
Reppert and Weaver, 2001). The SCN is capable of maintaining
rhythmicity in the absence of environmental inputs, but cues like
sunrise, sunset, and fluctuations in temperature increase its temporal fidelity (Dibner et al., 2010).
The first section of this review provides a description of the
machinery that underlies circadian rhythms. The expression of various genes and associated protein products vary throughout the day
in the SCN and these variations are associated with changes in the
synthesis, sensitivity, efficacy, and concentrations of various neurotransmitters, neuropeptides, and hormones throughout the brain
and body (Hirota and Fukada, 2004). Many of these same agents
regulate gene expression and activity within the SCN (Antle et al.,
2005; Choi et al., 2008; Liu et al., 1997; Morin, 1999). Further, the
dependence of the SCN on exogenous cues for anchoring its activity to real world events means that the presence or absence of cues
such as light at particular phases of circadian cycle elicit different
effects in the activity of the circadian network (Antle et al., 2009).
The second section describes the relationship between circadian rhythms and health. If the presence of circadian rhythmicity
is advantageous, the absence of circadian rhythmicity should be
detrimental. Shift work, jetlag, obesity, diabetes, affective disorders, dementia, and cardiovascular diseases have all been linked to
circadian rhythmicity or the disruption thereof in epidemiological
studies and in experimental animal models (Dallmann et al., 2012;
Pan et al., 2011; Parkes, 2002; Tranah et al., 2011). Regardless of
whether a particular study focuses on the effects of circadian disruption or correlates of poor health, the association between the
two is routinely identified. Further, many treatment outcomes for
disease states also elicit partial reparation of circadian rhythmicity
and vice versa providing additional support for the links between
circadian rhythms and health.
The final section of this review describes experimental queries
of the impacts of circadian rhythm disruption in animal models. The
localization of the master clock to the SCN provides the opportunity
for experimental manipulations of circadian rhythmicity. Elimination of circadian rhythms has been elicited via techniques such as
physical ablation of the SCN (Lesauter and Silver, 1998), molecular
knockouts (KOs; Husse et al., 2011; Okamura et al., 1999) or mutant
rodent strains (Ralph and Menaker, 1988). The reliance of the
system on environmental inputs make it possible to manipulate circadian rhythms by altering the timing of relevant events (e.g., light
onset) without physical manipulation of the brain (Moore, 1996).
Various illumination paradigms have been developed for studying
circadian rhythmicity including constant illumination paradigms
(Yan et al., 2005), the administration of light pulses at conflicting
times of day (Antle et al., 2009), or altering light schedules to elicit
shifts in the light-dark (LD) cycle (i.e., LD shifts; Devan et al., 2001).
Each of these manipulations elicits differential responses, but they
provide the opportunity to examine the impacts of circadian disruption on different types of learning and memory (e.g., spatial
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memory, stimulus–response associations, time-place learning) as
well as the structures (e.g., hippocampus) that underlie these abilities.
1. Anatomy and gross morphology of the suprachiasmatic
nucleus
The CNS of all vertebrates contains a group of bilaterally distributed nuclei, the SCN. The SCN are located in the periventricular
zone of the anterior hypothalamus dorsal to the optic chiasm and,
as is true for so many nuclei located within the hypothalamus,
serve a homeostatic or physiological regulatory function. Each SCN
is comprised of two distinguishable regions, a core and a shell
(Abrahamson and Moore, 2001; Antle and Silver, 2005; Leak and
Moore, 2001) and while some arguments can be made against this
oversimplification, it will suffice for the purposes of this discussion
(Morin, 2007). Please see Morin and Allen (2006) for a complete
description of this debate and in depth discussion of neuronal phenotypes, anatomy, and connectivity within SCN.
The SCN exhibits cyclical patterns of electrical activity and
responsiveness to input over an approximate 24 h cycle (Inouye and
Kawamura, 1979). These rhythmic patterns of activity, which occur
with a frequency of an approximate solar day, are called circadian
oscillations (from Latin: “circa” = approximately and “diem” = day).
The SCN interprets information about time of day that it receives
from various sensory and homeostatic afferents including inputs
from the geniculohypothalamic tract and median raphe (Morin
and Allen, 2006). The geniculohypothalamic tract provides various
inputs to the SCN including GABAergic and neuropeptide Y (NPY),
although the molecular profiles differ by species (Morin and Allen,
2006). The serotonergic projections from raphe nucleus are thought
to affect the responsiveness of the SCN to other inputs (e.g., light)
(Abrahamson and Moore, 2001; Challet, 2007; Selim et al., 1993).
Of the inputs received by the SCN, light, arriving at the SCN via
the retinohypothalamic tract (RHT) from intrinsically photosensitive melanopsin expressing retinal ganglion cells (pRGCs) elicits the
most robust effects within the SCN (Ruby et al., 2002; Hattar et al.,
2002; Provencio et al., 1998). When combined, these multimodal
inputs inform daily cycles of SCN activity (Abrahamson and Moore,
2001; Leak and Moore, 2001) which then feed into the transcriptional and translational machinery of each neuron. Although the RHT
is the major photic input in mammals (Cassone et al., 1988), this
can differ by taxa. For example, Tuatara (Sphenodon) possess light
sensitive cells in the pineal gland (Tosini et al., 2001) which may
act to synchronize certain rhythmic processes, bypassing the eye
altogether.
The SCN also receives information about internal physiological
state (Leak and Moore, 2001). These inputs include humoral signals and thus involve various brainstem and medial hypothalamic
nuclei (Kalsbeek and Buijs, 2002), along with other components
of the autonomic nervous system (Krout et al., 2002). For example, melatonin, released from the pineal gland, affects transcription
rates in SCN in a multistep cascade that ultimately targets the
SCN shell (Reppert and Weaver, 2001). In addition to timing cues
derived from light, blood pressure, insulin, sex hormones and other
neuropeptides, various homeostatic processes exhibit their own
circadian oscillations that, in turn, may feed-back into the SCN to
tune the circadian clock. The loci responsible for generating these
rhythms have been categorized as peripheral oscillators due to their
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E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
downstream position relative to the SCN and light (Hirota and
Fukada, 2004).
The core of the SCN receives afferent input from both the CNS
and peripheral tissues (Abrahamson and Moore, 2001; Krout et al.,
2002; Leak and Moore, 2001). While canonical clock genes (CGs)
oscillate rhythmically throughout the extent of the SCN, the core
does not exhibit synchronized, endogenously rhythmic patterns
of electrical activity in the absence of external input (Butler and
Silver, 2009). Instead, it is hypothesized to rely upon environmental inputs from these various afferent projections to trigger bursts
of activity (Silver et al., 1996b). Photic stimulation is an example of an environmental input that targets the core region of the
SCN (Abrahamson and Moore, 2001). Thus, the neurons within the
core can be described as environmentally responsive, particularly
to photic inputs. This does not mean that the neurons of the core
do not oscillate. SCN core neurons do oscillate over the 24 h cycle,
but without input, the population becomes desynchronized (Antle
and Silver, 2005; Butler and Silver, 2009).
Unlike the core, the shell region exhibits highly regularized
endogenous oscillations of electrical activity and is not immediately responsive to photic stimulation (Abrahamson and Moore,
2001). The shell of the SCN projects to many hypothalamic nuclei
and some peripheral tissues that send information about their own
rhythms back to the SCN, forming an information feedback loop
that is hypothesized to provide stability to the ticking of the master circadian pacemaker. Unlike the core, the shell will maintain
an approximate 24 h pattern of electrical activity in the absence of
environmental cues (Nakamura et al., 2001; Welsh et al., 2010).
In general, the core interprets and integrates information
regarding time of day, entraining the oscillator present within the
shell, while the shell itself mediates the output of the master circadian pacemaker (Antle and Silver, 2005). Therefore, the circadian
system can be conceptualized as an open-closed system that operates through various feedback loops where several tissues act as
both afferent and efferent nodes in concert with numerous targets
within the CNS and across the body (Dibner et al., 2010; Leak and
Moore, 2001; Welsh et al., 2010).
2. Mechanisms underlying circadian rhythmicity
2.1. Molecular oscillators
Endogenous circadian rhythms observed in the SCN core, shell,
and peripheral organs and tissues are generated by oscillating gene
expression derived from an autoregulatory transcription/posttranscription/translation/post-translation-based feedback loop
(Dibner et al., 2010; Okamura, 2004). The earliest descriptions of
the genetic machinery responsible for circadian rhythm generation were made in the fruitfly (Drosophila melanogaster; Konopka
and Benzer, 1971) and subsequently, seven genes important for
circadian rhythm generation have been identified (for detailed
review see, Lowrey and Takahashi, 2000). Homologs of each, and
in some cases several iterations of the drosophilae genes, have
been identified in other species (Yan, 2009). The transcription,
translation, and translocation of these genes vary rhythmically
throughout the day.
The feedback loop is executed across the breadth of the neuron, including nuclear transport and cytoplasmic concentration
gradients (Hirota and Fukada, 2004; Reppert and Weaver, 2001).
The system is comprised of two limbs, categorized as either
positive (i.e., the presence of the factors increases the transcription/translation of some product) or negative (i.e., the presence of
the factors decreases the transcription/translation of some product)
(Lowrey and Takahashi, 2000; Shearman et al., 2000a,b). Though
gene expression is endogenously controlled, and hence intrinsically
rhythmic, there are several mechanisms that entrain the system
through various inputs or “Zeitgebers”(from German ‘zeit’ – time and
‘geber’ – giver). The use of mutant mouse strains and the manipulation of rhythms using various behavioral paradigms have greatly
enabled the investigation of the role of CGs in circadian function
(Allada et al., 1998; Albus et al., 2002).
The negative component of the feedback loop is dominated by
the CGs Cryptochrome (Cry) and Period (Per; Reppert and Weaver,
2001). In drosophilae Per and Cry have single isoforms, but in mammals (e.g., mice) Per has three iterations (Per1, Per2, and Per3) and
Cry has two (Cry1, Cry2; Reppert and Weaver, 2001). The presence of
PER and CRY protein products within the nucleus inhibits their own
transcription by down-regulating the transcription factors CLOCK
and BMAL1 (Zhang and Kay, 2010).
Light can also influence PER1 and PER2 expression in the SCN
in conjunction with endogenously rhythmic transcription factors
CLOCK and BMAL1 (Hannibal, 2002). Information traveling along
the RHT from the pRGCs arrives at the retinorecipient zone where
it activates neurons and subsequently engaged intracellular second messenger signaling cascades. The most important of which
involves cyclic AMP response binding element (CREB) whose transcriptional activity acts directly at the per promoter (Lee et al.,
2010; Obrietan et al., 1999; Travnickova-Bendova et al., 2002).
Early-night light exposure increases the expression of PER1 and
PER2, which delays the circadian oscillator, whereas late-night
light exposure only increases PER1 expression, resulting in a phase
advance (Challet et al., 2003). The magnitude of the change is
partially mediated by the intensity and timing of the light pulse
(Challet et al., 2003). Consequently, they are excellent indicators
of the inputs into the molecular clock and can be used to determine differential responsiveness to such inputs. Not surprisingly
given their role as intermediaries between inputs and the molecular clock itself, Per1 and Per2 are among the quickest CGs to adapt
to a new circadian period (Shearman et al., 1997). The role of Per3
is less clearly defined with only mild perturbations observed when
its protein product is absent (Shearman et al., 2000a,b). A length
polymorphism of this gene is associated with delayed sleep phase
syndrome (Archer et al., 2003), but it is still unclear whether this
is due to perturbations of the circadian clockwork or represents
some role for PER3 in sleep homeostasis. Adding further complexity to their regulation, PER protein levels are enhanced via
Casein Kinase 1E (CK1␧) phosphorylation, a critical agent for the
maintenance of normal 24 h rhythms (for review see Zhang and
Kay, 2010). The importance of CK1␧ is highlighted by a semidominant mutation found in Tau mutant hamsters whereby wild type
animals possess a circadian cycle of approximately 24 h, heterozygotes exhibit an approximate 22 h cycle, and homozygous hamsters
exhibit an approximate 20 h cycle (Ralph and Menaker, 1988).
Per genes are typically co-expressed with Cry genes (Welsh et al.,
2010). Cry1 and Cry2 expression may also be used to quantify circadian phase (Albrecht, 2002). Targeted deletion of the Cry genes
has revealed that Cry1 shortens the length of the circadian cycle
whereas Cry2 lengthens it (Okamura et al., 1999). In the absence
of both isoforms, circadian rhythmicity gradually disappears under
constant dark (DD) conditions (van der Horst et al., 1999), although
not as quickly as in BMAL1 or PER triple KO animals. Cry double
KO animals show so-called “aftereffects” with the destabilization
of behavioral rhythms taking up to a week in order to be fully
observed. This argues that CRYs may act to increase robustness
and provide a positive driving force to maintain circadian amplitudes, but may be less important for determining either the period
of the molecular clock itself or the coupling between many such
clocks as is required for the proper function of the master circadian
pacemaker.
Given that PER and CRY function as heterodimers it is important to consider not only the individual temporal dynamics of each
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
protein alone, but the timing of their coincident presence both in
the cytoplasm and the nucleus. The expression of CRY and PER peak
mid to late in the circadian day (Dibner et al., 2010; Lowrey and
Takahashi, 2004) and when CRY and PER levels reach their nadir of
expression, their transcription, driven by CLOCK and BMAL, is at its
peak (Dibner et al., 2010). Therefore, the concentrations of CRY and
PER in the cytosol and the nucleus have important implications for
the activity of the transcriptional machinery governing circadian
rhythm generation (Allada et al., 1998; Reppert and Weaver, 2001).
CLOCK and BMAL1 form the positive component of the feedback
loop. Though they are distinct entities, CLOCK:BMAL1 heterodimers
enhance transcription of Per and Cry through E box elements
(Duguay and Cermakian, 2009). CLOCK:BMAL1 expression also
oscillates over the 24 h cycle (Dibner et al., 2010). BMAL1 can be
used to categorize circadian phase because it exhibits a PER and
CRY anti-phase peak in the middle of the circadian night (Maywood
et al., 2003; Welsh et al., 2010). How CLOCK and BMAL1 regulate
the expression of Cry is largely undefined, but it is well documented
that PER is a positive regulator of BMAL1 transcription (Duguay and
Cermakian, 2009). Another potential mediator of CLOCK:BMAL1
transcription is an interaction between PER and CRY that facilitates
nuclear membrane transport, thus enabling BMAL transcription
(Dibner et al., 2010).
A third class of CGs, Timeless (Tim), has also been implicated
in the regulation of CLOCK:BMAL1 transcription (thus, Per and Cry
transcription) conclusively in drosophilae, but not mammals. In
drosophilae, Tim has a clearly defined role as a regulator of photic
input during clock resetting (Field et al., 2000; Hastings et al., 1999).
In mammals, Tim does not interact with other CGs directly nor
exhibit circadian oscillation in mRNA expression (Koike et al., 1998;
Zylka et al., 1998) and thus, does not appear to act as a CG in mammals (Field et al., 2000; Hastings et al., 1999).
The rate of CGs re-entrainment (or re-alignment) to a new circadian rhythm has important implications for memory and health
(Welsh et al., 2010). The abundance of mediating factors (e.g., photic
entrainment; Dibner et al., 2010) and environmental inputs targeting the SCN potentiate how quickly it is able to adapt to a
new circadian phase and CG expression within the SCN. However, due to the hierarchical nature of the circadian system, other
CG expressing regions (peripheral oscillators) are slower to adapt
because they do not possess abundant modulatory Zeitgeber inputs
(Albrecht, 2002). Except for a few regions (for review, see Guilding
and Piggins, 2007), the rhythmicity of many peripheral oscillators, such as those found in the neocortex (Rath et al., 2013) and
cerebellum (Rath et al., 2012), depend on the SCN. As a result,
these ‘slave’ oscillators are potentially much slower when adapting
to new schedules than the SCN. The desynchronization between
the central and slave oscillators, and the prolonged duration of
disruption in extra-SCN areas relative to SCN provides a mechanism by which circadian disruption may contribute to reduced
cognitive function and poor health outcomes following circadian
disruption. This was first demonstrated using ex vivo tissue from
transgenic rats which express luciferase driven by the PER2 promoter, allowing Yamazaki et al. (2000) to observe the phase of
entrainment of various tissues after a shift of the light:dark cycle.
A more recent follow up confirms and extends this finding in mice
(Davidson et al., 2009). The desynchrony between circadian phase
of the SCN and other oscillators likely account for the detrimental decline in metabolic efficiency observed during “jetlag”, be it
from international travel or from social factors contributing to living a schedule mismatched with our internal biological rhythms
(Roenneberg et al., 2012).
The molecular machinery responsible for the generation of circadian rhythms is far from simple. Multiple cascades taking place
in the nucleus and cytoplasm contribute to the expression of circadian oscillations and rhythmicity. In addition, Zeitgebers (e.g., light)
83
manipulate the expression of the cascades that can act to stabilize the rhythms, advance, or delay them. Much of what we know
about the molecular machinery of circadian rhythms has been generated through careful experimentation and the use of selective
KOs, but naturally occurring mutants (e.g., Tau mutations in Syrian
golden hamsters) have also contributed to our understanding of
how relatively few genes are responsible for the oscillatory activity
observed in the SCN and peripheral oscillators. However, CGs do not
represent the complete story because various neurotransmitters,
peptides, and hormones also modulate the molecular machinery of
the SCN.
2.2. Neurotransmitters, peptides, and hormones
Several neurotransmitters including serotonin (5HT), norepinephrine, glutamate, gamma-aminobutyric acid (GABA), and
acetylcholine (ACh) affect functional activity in the SCN (Challet,
2007). Molecular communication within the SCN, like many
other hypothalamic nuclei, is largely modulated by colocalization
of traditional neurotransmitters with various neuromodulators
including hormones (e.g., melatonin; steroids), neuropeptides (e.g.,
Vasoactive Intestinal Peptide, VIP; Arginine Vasopressin, AVP;
Gastrin Releasing Peptide, GRP; Substance P), and intracellular
mediators of cellular activity (e.g., Calbindin; Calretinin; Pituitary
adenyl cyclase-activating peptide, PACAP; NPY) (Abrahamson and
Moore, 2001). The colocalization of these numerous molecular
agents is a large contributor to the complexity of SCN regulation
(Goldman, 2001).
2.2.1. Classic neurotransmitters
2.2.1.1. Serotonin. 5HT is an important regulator in the SCN, though
how 5HT elicits its effects are poorly understood (Challet, 2007;
Morin, 1999). 5HT concentration oscillates across the 24 h cycle
and appears to be mediated, at least in part, by locomotor activity
and is under the control of the SCN via daily fluctuations in corticosteroid concentrations (Malek et al., 2007). Raphe projections
synapse with the core of the SCN, providing the majority of 5HT
that dampens the SCN’s response to retinal input (Abrahamson
and Moore, 2001; Pickard et al., 1999). 5HT not only regulates
the phase of the rat SCN in vitro (Medanic and Gillette, 1992), it
also has the ability to alter LD shifting in vivo (Edgar et al., 1993).
Infusion of the serotonergic agonist quipazine into SCN immediately before the administration of a late night light pulse in
hamsters attenuated the expression of glutamate and cFOS protein expression (Selim et al., 1993). That said, caution must be
taken when interpreting these results because there is a lack of
distinct pharmacological tools capable of delineating the various
serotonergic receptor subtypes expressed within the SCN. Paradoxically, both photic and non-photic effects of 5HT have been
reported depending on the animal model and the pharmacological agents used and there are conflicting reports of 5HT’s ability
to either potentiate or to suppress input induced electrical activity and subsequent changes in CG expression. Cuesta et al. (2009)
attempted to resolve these issues, at least in rats, through detailed
analysis of the variable contributions of 5HT receptor subtype
during light-induced advances vs. delays. More recently, genetic
tools have allowed for the circumvention of the pharmacological
limits imposed, allowing the function of at least one distinct receptor subtype to be investigated. For example, Smith et al. (2008)
report exaggerated phase response curves in 5HT-1A KO mice during circadian manipulation indicating that 5HT-1Ar signaling is
likely responsible for attenuating the phase shifting capacity of
light.
2.2.1.2. GABA. GABA is expressed in the majority of SCN neurons
(Kalsbeek and Buijs, 2002). GABA expression peaks in the middle
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E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
of the subjective day in mammalian species (Friedman and Piepho,
1978). The midday peak is sensible because a large proportion of
the GABA afferents arrive at the SCN via retinal fibers (Jiao and
Rusak, 2003). Although the effect of GABA on the SCN is primarily inhibitory, GABA can also have an excitatory effect on the SCN
(Choi et al., 2008; Irwin and Allen, 2009). The influence of GABA
on the SCN is regulated via the variable expression of specific chloride channels across the circadian cycle (Choi et al., 2008; Irwin
and Allen, 2009 Wagner et al., 2001; Wagner et al., 1997). The
nature of the effect of GABA on the SCN varies depending on the
time of day and there are conflicting reports concerning at what
times of day GABA is inhibitory or excitatory (Choi et al., 2008;
de Jeu and Pennartz, 2002; Irwin and Allen, 2009; Wagner et al.,
2001; Wagner et al., 1997). However, Choi et al. (2008) observed
that although GABA was more likely to have an excitatory effect
on SCN neurons during the night, for a small subset of SCN neurons
GABA was excitatory during both the day and night. The majority of
SCN GABAergic efferents terminate in many mediobasal hypothalamic regions (Hermes et al., 1996). For example, direct GABAergic
innervation of the paraventricular nucleus (PVN) is responsible for
mediating many autonomic and humoral processes (Hermes et al.,
1996), some of which feedback to the SCN (e.g., melatonin, cortisol/corticosterone).
2.2.1.3. Acetylcholine. Cholinergic projections to the SCN arise from
the basal forebrain, the lateral and medial septum, lateral geniculate nucleus, and zona incerta, though discrepancies have been
reported, especially across species (Bina et al., 1993; Pickard, 1982).
Further, reports of choline acetyltransferase (ChAT)-positive neurons in SCN vary across rodents with maximal expression observed
in rats, localized expression in anterior SCN of hamsters, and no
ChAT staining observed in mice (Hut and Van der Zee, 2010). ACh
is thought to alter the sensitivity of the SCN to other molecular signals or proteins and is largely constrained to the night phase of the
LD cycle in nocturnal rodents (Gillette and Mitchell, 2002; Hut and
Van der Zee, 2010). ACh is often referred to as a signal of wakefulness because its expression in cortex exhibits consistent circadian
cycle peaks during periods of activity and is lowest during sleep
regardless of whether a species is diurnal or nocturnal (Hut and
Van der Zee, 2010). ACh increases following the presentation of
a light pulse (Murakami et al., 1984). However, the expression of
ACh within the SCN does not exhibit endogenous circadian variation (Hut and Van der Zee, 2010). The decline in the function of
the cholinergic system that has been observed in advancing age
(Bartus et al., 1982) provides support for the cholinergic hypothesis of Alzheimer’s disease (Francis et al., 1998). This observation
also introduces a potential mechanism for circadian arrhythmia
(i.e., abnormal circadian rhythmicity) in aged individuals that may
contribute, at least in part, to the decline in SCN output and dampening of circadian rhythmicity observed in the aged SCN (Kalsbeek
et al., 2010; Nakamura et al., 2011).
2.2.1.4. Glutamate. Light cues travel from the retina to the SCN
along the RHT (Abrahamson and Moore, 2001). Optic nerve
stimulation releases glutamate and PACAP at the SCN and the
corresponding activation at glutamatergic NMDA receptors is an
excellent example of environmentally triggered activation of the
SCN (Beaule et al., 2009). Glutamate is the most highly expressed
neurotransmitter along the RHT (for review, see Brown and Piggins,
2007; Colwell, 2001). The loss of function following NMDA receptor blockade implies that glutamate is the primary mechanism
by which photic information is communicated within the SCN
(Hannibal and Fahrenkrug, 2002). The importance of glutamate in
RHT-SCN communication is highlighted by the observation that
NMDA administration early in the subjective night elicits larger,
prolonged increases in intracellular Ca2+ transients relative to
subjective daytime NMDA administration in rats measured via
whole-cell patch clamp electrophysiology and infrared differential
interference contrast videomicroscopy (Colwell, 2001).
Various neurotransmitters are involved in SCN signaling. 5HT
and ACh modulate intra-SCN activity. GABA is involved in the
majority of SCN efferent transmission whereas most photic
information is communicated to/within the SCN via glutamate.
Oscillating CG (i.e., time of day effects) modulate the sensitivity
of the SCN to various neurotransmitters resulting in fluctuations in
the sensitivity of the SCN to neurotransmitters and the effects they
elicit (e.g., phase advances vs. phase delays contingent on the timing
of the light pulse). However, there is more to the picture, including
several neuropeptides that are critical for the maintenance of SCN
function and circadian rhythmicity.
2.2.2. Small molecule signaling and neuropeptides
Various small molecule transmitters and neuropeptides are
crucial components of functionality in the SCN. Neuropeptides
modulate the affinity, affectivity, and sensitivity of the SCN, and
target tissues, to molecules like glutamate, GABA, and ACh (Moore,
1996). Neuropeptides within the SCN promote the expression of
particular genes or proteins (e.g., NPY; VIP; vasopressin, VP; Welsh
et al., 2010). For example, PACAP modulates glutamatergic excitation following RHT input to the SCN (Colwell, 2011).
In general, neurons comprising the shell portion of the SCN
express AVP (Abrahamson and Moore, 2001). AVP is thought to
potentiate neuronal firing within the SCN, but may also act as a
neuromodulator of SCN output (Kalsbeek et al., 2010). The shell
synthesizes calbindin (CalB), angiotensin II, and met-enkaphalin
(mENK) and the core synthesizes VIP, gastrin releasing peptide (GRP), CalB, calretinin, and neurotensin (Abrahamson and
Moore, 2001). Neurons expressing VIP and GRP (i.e., the core)
use afferent inputs from upstream structures (e.g., retina, hypothalamic/thalamic nuclei) to synchronize the individual oscillators
found within each cell (Stehle et al., 2003). There is also evidence in
mammals that GRP and VIP mimic the effects of photic input (Reed
et al., 2001).
Microinfusion of GRP into the SCN potentiates Per1 and Per2
expression within the SCN (Antle et al., 2005), though the exact
pattern of gene expression differs across species. VIP signaling also
supports the circadian regulation of glucocorticoid release (Loh
et al., 2008) although it is unlikely that this is through direct VIP
output to regions controlling release. There is evidence that AVP
output is directly responsible for circadian suppression of the HPA
axis and this is likely to be the intrinsic timekeeping mechanisms
of the SCN (Buijs et al., 1998; Kalsbeek et al., 1992).
Neuropeptides and small molecule neurotransmitters are critical for the proper function of the SCN. The absence of neuropeptides
profoundly affects CG, neurotransmitter and Zeitgeber functions
that are central to the SCN’s ability to adapt to new circadian times,
incorporate exogenous information, and oscillate in a normal fashion. Though they most often operate in a synergistic fashion with
specific neurotransmitters, neuropeptide transmitters are a necessary component of the clock system. Once again, the influences of
this class of neurotransmitters on the SCN occur among a community of other agents that require examination in concert.
2.2.3. Hormones
The SCN targets various hypothalamic nuclei responsible for
hormone release (Gan and Quinton, 2010; Leak and Moore,
2001). These hormones (e.g., corticotropin-releasing hormone,
CRH; gonadotropin releasing hormone, GnRH) also act to regulate
SCN function, providing a means of reciprocal control (Dickmeis,
2009). Variable concentrations of these hormones alter behavioral
state, which can also impact circadian state.
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
2.2.3.1. Melatonin. Melatonin expression corresponds to the LD
cycle and varies across the circadian cycle (Chattoraj et al., 2009).
Melatonin is not only involved in circadian modulation, it has also
been implicated in various metabolic disorders (Korkmaz et al.,
2009), the majority of which (e.g., obesity, diabetes) also have
strong ties with abnormal circadian function (Bechtold et al., 2010).
Though melatonin secretion is coupled to LD cycle, pinealectomy
(i.e., the removal of melatonin synthesis) does not eliminate circadian rhythmicity. Melatonin does not appear to be necessary
for gross circadian rhythm generation or maintenance, but melatonin seems to be responsible for modulating ultradian rhythms
(Cassone, 1992). Melatonin receptors are expressed in the SCN (Liu
et al., 1997) and the activity of the pineal gland is impacted by circadian time and periodicity, but pinealectomy does not appear to
impact the level of daily patterns of locomotor activity (Prendergast
et al., 2013).
Primary synthesis and release of melatonin occurs at the pineal
gland (Nathan et al., 1998), but melatonin synthesis also occurs
in a variety of tissues ranging from the retina to the gut (Korkmaz
et al., 2009). Melatonin synthesis and secretion are highest at night,
but this pattern is attenuated under constant light (light:light; LL)
conditions (Armstrong et al., 1986). Modern living conditions (i.e.,
light persisting past sunset) have drastically impacted 24 h melatonin profiles (Navara and Nelson, 2005). There are potential health
risks associated with this decline because melatonin is a purported
antioxidant that contributes to the elimination of free oxygen radicals and nitric oxide (Reiter et al., 2009). The attenuation of the
24 h melatonin profile (i.e., reduced antioxidants in the CNS) is one
way that modern living conditions could be detrimental to human
health.
At the SCN, melatonin modulates gene expression derived by
the transcription-translation feedback loop that supports the 24 h
circadian cycle. The influence of melatonin within the SCN takes
place at specific melatonin receptors (e.g., melatonin receptor 1 and
2, Mel1 and Mel2; Liu et al., 1997) and the expression of melatonin
receptors oscillates over the 24 h cycle (von Gall et al., 2002). So, not
only does the concentration of melatonin fluctuate across the cycle,
but the ability of melatonin to influence the SCN varies across circadian time (Korkmaz et al., 2009). Melatonin receptors are expressed
in numerous tissues outside the SCN including anterior pituitary,
median eminence, dorsomedial nucleus of the hypothalamus, lateral habenular nucleus, reuniens nucleus, and nucleus of the stria
medullaris (Weaver et al., 1989). This, in combination with the
observation that melatonin expression declines by two thirds in
healthy aged individuals and four fifths in preclinical AD populations relative to young controls, has resulted in a great deal of hype
regarding the role of melatonin in aging and health (Rosales-Corral
et al., 2012).
Contrary to the hype that surrounds melatonin and health, considerable ambivalence exists amongst chronobiologists regarding
the role/efficacy of melatonin in circadian rhythmogenesis. This
skepticism may be because responsiveness to LD shifts is preserved
following pinealectomy (i.e., the removal of melatonin from the circadian milieu; Prendergast et al., 2013) or because many inbred
mouse strains lack key enzymes in the melatonin synthesis pathway (Goto et al., 1989) which makes interpretation of data related
to melatonin effects difficult in some mouse models. Although a
scientist may generate hypotheses regarding negative results, she
must be careful to maintain the boundaries of empiricism and
not over-interpret the lack of a positive result as evidence for the
absence of an effect. In short, it is important to maintain criticality
when assessing the efficacy of melatonin as a medical treatment.
2.2.3.2. Steroids. Another class of hormones that have been implicated in the production and maintenance of circadian rhythms are
steroids (Dickmeis, 2009). Steroid hormones can be broken down
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into two broad classes: Gonadal steroids (e.g., testosterone, estrogen) and Glucocorticoids (e.g., corticosterone). These hormones are
ultimately released from organs located outside the brain (e.g.,
gonads, adrenal glands), but their release is triggered in multistep cascade that begins in the brain with precursor hormones and
includes several intermediary molecules (Evans, 1988).
Gonadal hormone expression is controlled via the
hypothalamic-pituitary-gonadal axis (HPG; for review see,
Handa et al., 1994). This circuit acts to regulate concentrations
of hormones most often associated with reproductive function
(Galea, 2008). Estrogen affects gene expression within the SCN in
females (Nakamura et al., 2005) and androgens are implicated in
the proper pacemaker function of male mouse SCN (Karatsoreos
et al., 2007). These estrogen-associated effects are not unique to
the SCN relative to other areas of the brain (e.g., cortex) and body
(e.g., kidney, uterus, etc.). In addition to the changes induced by
gonadal steroids on SCN, alterations in performance on various
cognitive tasks/dimensions have also been observed (e.g., spatial
navigation; Galea et al., 2008).
The scale at which the SCN influences the HPG ranges from
ultradian (i.e., within a day) to circannual (i.e., across seasons). In
adult mammals, the expression of gonadal steroids fluctuates consistently over the circadian cycle (Gan and Quinton, 2010) but the
highest concentrations are typically found in early morning and
decline over the course of the day (Bao et al., 2003). This cycle is
similar to that of gene and protein expression within the SCN over
the 24 h cycle. SCN estrogen receptors have implicated that gonadal
hormones play a functional role in circadian rhythm generation
(Kruijver and Swaab, 2002) and provide a potential mechanism for
sex-specific impairments following circadian disruption (Zelinski
et al., 2014a).
Gonadal hormone concentrations (e.g., testosterone, estrogen,
gonadotropin releasing hormone) exhibit daily oscillations across
the reproductive lifespan, though variability exists in hormonal
rhythms according to season, reproductive conditions, and other
factors (Moskovic et al., 2012). Gonadal hormones are important
during prenatal development, but are quite low following an early
perinatal peak until the onset of puberty in mammals (Perfito and
Bentley, 2009). Thus, circadian dependent fluctuations in gonadal
hormone concentrations are only a contributing factor to circadian
rhythmicity.
At the close of reproductive lifespans, menopause, and arguably
to some extent andropause, result in the decline of circulating levels of gonadal steroids. Though circadian rhythms do exist at these
time points, the robustness of circadian rhythms decline with age
(Antoniadis et al., 2000). The concomitant declines in circadian regulation and gonadal hormone expression implicates reproductive
senescence in the decline of circadian rhythmicity with advanced
age. However, there are a multitude of hypotheses addressing why
circadian rhythms decline with age and it is likely to be the result
of a change in several, interacting phenomena.
2.3. Glucocorticoids and stress
Glucocorticoid release is associated with physiological arousal
and stress and hormone release is closely tied to the magnitude of
arousal. As such, differential concentrations of stress hormones are
related to the metabolic needs of the sympathetic and parasympathetic nervous systems that by their nature demand a rapid
neural response (Sapolsky et al., 2000). Thus, some of the pathways
associated with glucocorticoids are fast-acting. Baseline levels of
glucocorticoids (e.g., corticosterone) also fluctuate across the circadian cycle in a predictable fashion, though environmental stressors
play an enormous role on the expression thereof (Chrousos, 1998;
Dickmeis, 2009).
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Glucocorticoid release is modulated by the hypothalamicpituitary-adrenal axis (HPA; Tsigos and Chrousos, 2002) and
consistent patterns of glucocorticoid expression can be observed
across days. Signals generated in the hypothalamus trigger the
release of intermediary hormones (e.g., ACTH) from the pituitary
that, in turn, trigger the release of glucocorticoids from the adrenal
glands (Chrousos, 1998). Circadian oscillators also exist within
peripheral tissues and these often act as a gating mechanism for
various intrinsic and extrinsic tissue functions. For example, the
molecular clocks found within cells of the adrenal gland act as a
gate for glucocorticoid signaling (Oster et al., 2006) and comparable mechanisms are also present in the liver and the pancreas
(Lamia and Evans, 2010; Lamia et al., 2008). This regulation, occurring downstream of the master pacemaker, results in increased
complexity and must be considered when attempting to understand the effects of circadian modulation (or perturbation) on
discrete tissue function or hormone secretion. Furthermore, the
abundance of peripheral oscillators make it extremely difficult to
isolate oscillating outputs, like hormone secretion, associated with
endogenous rhythmicity from those caused by environmentallyor systemically-induced fluctuations (e.g., any noxious event be
it internally or externally driven). The genetic tools necessary to
directly tease apart these two very separate inputs driving transcription and translation in peripheral tissues have only recently
become available (Hughes et al., 2012).
As is the case with the HPG axis, the HPA axis maintains several feedback mechanisms that allow for modulation of hormone
concentrations (Chrousos, 1998; Dickmeis, 2009), but there are
several instances where the system can go awry. Chronic activation of the HPA axis leads to a decline in its sensitivity to high
levels of glucocorticoids leading to elevated glucocorticoid concentrations. Prolonged exposure to glucocorticoids elicits robust,
persistent deficits in the functions of various systems including
brain (Sapolsky, 2000) and reproductive tissues (Thaker et al.,
2006). This is alarming given that circadian disruption is associated
with higher baseline glucocorticoid levels (Cho, 2001; Cho et al.,
2000).
3. Peripheral and slave oscillators
Core CGs are rhythmically expressed in virtually all peripheral
tissues (Yamamoto et al., 2004) including liver, heart, lung, kidney, spleen, stomach, skeletal muscle, cornea, thyroid and adrenal
glands (Dibner et al., 2010). Although the SCN is the master pacemaker in mammals, there is a complex network of oscillators
in other brain regions and peripheral tissues. Many physiological processes such as metabolism (lipids, carbohydrates, proteins,
xenobiotic substances), renal (renal plasma flow and urine production) and cardiovascular (blood pressure and heart beat) functions
exhibit circadian variation (Dibner et al., 2010; Gachon et al., 2004).
Circadian rhythms in body temperature, feeding, and activity can
also entrain peripheral oscillators indirectly (for reviews see Brown
and Azzi, 2013; Dibner et al., 2010; Mohawk et al., 2012). SCNperipheral oscillator communication occurs via direct neuronal
efferents, neuropeptide secretion, and humoral signals (e.g., glucocorticoids)(for reviews see Brown and Azzi, 2013; Dibner et al.,
2010; Mohawk et al., 2012).
The relationship between the SCN and peripheral oscillators was
initially unclear. The SCN was hypothesized to either generate or
synchronize circadian output in peripheral oscillators. Yoo and colleagues revealed that the SCN was necessary for synchronizing CG
expression between tissues, though CGs continue to oscillate in
peripheral tissues of SCN lesioned mice (Yoo et al., 2004). However, it should be noted that rhythmic expression of some clock
and clock control genes can be sustained by the SCN in the absence
of local clocks in peripheral tissues. For example, Kronmann et al.
(2007) demonstrated that Per2 and various clock controlled genes
continued to oscillate in the liver, even when the clocks of hepatocytes were nonoperational in vivo. Although CG oscillations in
individual cells of peripheral oscillators can be maintained without
input from the SCN, the SCN is much more resistant to perturbations
including CG mutations (Ko et al., 2011) and changes in temperature (Buhr et al., 2010) because there is more coupling between
SCN neurons than cells in peripheral oscillators (for review see
Mohawk et al., 2012). These SCN properties further strengthen the
notion that the SCN is the master pacemaker because there is a
high degree of synchronicity between SCN neurons due to this coupling, which means that rhythmicity is maintained in the SCN under
conditions that would abolish rhythmicity in peripheral oscillators
(Mohawk et al., 2012). For example, even when the clocks within
SCN neurons are rendered arrhythmic by knocking out BMAL1, due
to coupling and molecular noise within the SCN network, stochastic
circadian rhythms are still evident in SCN tissue explants (Ko et al.,
2011).
There are also oscillators in many other brain areas, including
but not limited to the olfactory bulb, hippocampus, cerebral cortex,
amygdala, pineal gland, cerebellum, and paraventricular and arcuate nuclei of the hypothalamus (for review see Guilding and Piggins,
2007). The majority of these regions (e.g., cerebellum, cerebral cortex) fail to generate circadian output in the absence of input from
the SCN and are thus referred to as slave oscillators (Rath et al., 2012,
2013). However, some brain areas such as the arcuate nucleus of the
hypothalamus, dorsal medial hypothalamus, and the lateral habenula can be categorized as semi-autonomous oscillators (Guilding
and Piggins, 2007) because neurons in these regions continue to
exhibit oscillatory properties in the absence of the SCN, though synchronicity of the circadian output of neurons within that tissue is
abolished (Guilding and Piggins, 2007). Unlike the aforementioned
regions, the olfactory bulb has all the characteristics of a master
pacemaker (e.g., the SCN) and appears to be a fully autonomous
oscillator (Guilding and Piggins, 2007).
In summary, circadian rhythms are elicited by the synergistic
interactions of many oscillators throughout the brain and body
though much of it occurs under the direction of the SCN. As
highlighted in the present review, the myriad of health problems
associated with chronic circadian disruption are likely a product
of desynchronization across peripheral and master oscillators for
extended periods of time (Hastings et al., 2003; Albrecht, 2012).
Although molecular oscillations in the SCN entrain to Zeitgebers
relatively quickly (Davidson et al., 2009; Yamazaki et al., 2000),
during chronic periods of circadian disruption the clocks in the
periphery might be constantly playing catch up and subsequently
remain out of phase with the SCN. Further, a lack of coherence in
the oscillation of individual cells within each system (e.g., liver) (for
review see, Morse and Sassone-Corsi, 2002) may further contribute
to the negative outcomes following circadian disruption.
4. Perturbing the circadian system: consequences for
health
Circadian rhythms influence metabolic cascades in numerous
domains and links occur between several diseases and circadian
rhythm disruption (Fig. 1). It is plausible that the changes to
metabolic function in many disease states are related to the molecular machinery that underlies the circadian system and there are
an abundance of observations linking abnormal circadian function
to diseases with definitive metabolic components. Disorders ranging from Multiple Sclerosis (Hedström et al., 2011) to Diabetes
(Delezie and Challet, 2011) have been associated with circadian disruption. Furthermore, circadian dysfunction can be elicited through
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
87
Fig. 1. Circadian disruption affects multiple organ systems. The diagram provides examples of how circadian disruption negatively impacts the brain and the digestive,
cardiovascular, and reproductive systems. Though the diagram displays unidirectional affects, there are various feedback loops that exist within the system and interactions
that occur between these systems.
environmental manipulations (e.g., jetlag), not just innate malfunctioning of the clock system.
4.1. Shift work (Fig. 2)
There are conditions where an individual is required to forego
the intrinsic schedule dictated by their endogenous circadian
rhythm. One such example is shift work. Individuals employed on
rotating scheduling practices have increased prevalence of various diseases such as cancer, diabetes, and coronary problems
(Davis et al., 2001; Lund et al., 2001; Pan et al., 2011). Cognitively,
they exhibit increased rates of irritability, affective disorders, and
memory impairments (for review, see Frank and Ovens, 2004).
Under such adverse conditions, sleep–wake cycles become disrupted and time-inappropriate cues such as nighttime eating and
light exposure create conflicting signals in the SCN and peripheral oscillators or altered cellular mechanics. Various lifestyle
changes coincide with shift work and, rather than a reflection
of poor decision-making, it may be that a maladapted circadian
system is triggering behavior and metabolic changes that culminate in the expression of fatigue or increased preference for fatty
foods, thereby exacerbating the development of disease states that
involve circadian components (e.g., diabetes). In addition, increased
cancer rates are observed in individuals employed on rotating
shift work schedules (Davis et al., 2001; Schernhammer et al.,
2001, 2003). Disrupted circadian rhythmicity could be affecting
Shiftwork
Night time light exposure
Night time eating
Heightened preference/consumption of
high fat/sugar diet.
Decreased melatonin
Decreased clearance
of reactive oxygen
species
Heart disease
Premature aging
Cancer
(Metabolism in “resting” state at night)
Altered 5HT
signalling leading
to mood disorders,
depression,
irritability.
SCN
Increased adiposity
Insulin insensitivity
Increased Body Mass Index (BMI)
Hypothalamic nuclei
Pancreas
Liver
Adipose tissue
Altered leptin/ghrelin signaling
Obesity
Stroke
Diabetes
Heart disease
Fig. 2. A detailed example of the pathway toward ill health featuring shift work.
Night-time shift work is associated with a reversal in the eating schedule and being
exposed to light at night. Night-time light exposure is associated with altered 5HT
processing in the SCN, which affects downstream structures related to cognition
as well as hypothalamic nuclei that influence metabolism and peripheral circadian
oscillators. In addition, light at night affects the secretion and receptor density of
melatonin, a potent antioxidant. It is possible that the decreased efficacy of melatonin is involved in the development of premature aging, heart disease, or cancer.
Working at night also results in night-time eating which is associated with a preference for high sugar and/or fat foods. In addition to the increased consumption
of fats/sugars, lipid and glucose metabolism is altered resulting in increased adiposity and insulin insensitivity resulting in increased BMI. Increased BMI leads to
altered leptin/ghrelin signaling that feeds back to the hypothalamus, further altering
metabolism. When combined, various disease states begin to emerge such as diabetes, obesity, or cardiovascular disease.
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the body through both altered metabolic function and subsequent
behavioral modification, producing a cycle characterized by altered
cellular machinery, subsequent changes in diet choices and exercise, and ill health.
4.2. Aging and associated diseases
Oscillations of various circadian parameters decline with age.
These attenuations are observed in circulating levels of plasma
cortisol (Van Cauter et al., 1996) and testosterone in aging
men (Bremner et al., 1983). Similarly, aged mice show degradation of circadian rhythms in activity, body temperature, and
corticosterone (Weinert, 2000). Further, circadian deficits have
long been a hallmark of demented states. Formerly referred
to as nocturnal delirium, sun-downing (i.e., increased levels of
agitation and disorientation at particular times of day) is one
of the earliest observed cognitive phenomena related to circadian function (Cameron, 1941). These changes are linked to
alterations in oscillating levels of melatonin, 5HT, SCN morphology, and synchronicity with extra-SCN CG activity in dementia
(Cermakian et al., 2011; Swaab et al., 1988; Witting et al.,
1990).
Age-associated decline in brain function is not restricted to the
SCN (Park and Reuter-Lorenz, 2009). Dementia (i.e., age-associated
cognitive decline) is a heterogeneous group of disorders that
can be associated with vascular failure (e.g., vascular dementia;
stroke), cell death (e.g., Parkinson’s disease), or the accumulation
of plaques (e.g., AD), among others (e.g., fronto-temporal dementia, mild cognitive impairment). In addition to such disease specific
morphological changes, frequent nighttime awakenings are characteristic of the sleep profiles of aged individuals. These frequent
awakenings or shortened bouts of sleep and wakefulness are exaggerated in individuals with Alzheimer’s disease (AD). Although
changes have been observed in the SCN of AD brains, an examination of pontine nuclei responsible for the component types
of sleep (e.g., rapid eye movement REM) has not been undertaken. This is important because though aspects of sleep (e.g.,
the timing of sleep on and offset) are related, the SCN is not
responsible for sleep architecture or maintenance. Considerable
evidence exists outlining the importance of sleep for normal cognitive function, so a detailed examination of the characteristics
of pontine nuclei controlling sleep architecture should be undertaken in conjunction with an examination of SCN function in
demented individuals. In a rodent model, examination of sleep
components revealed that an acute episode of circadian disruption induced via LD shifts does not impact the amount of sleep
or number of sleep epochs (Rota et al., 2014), but does influence
the distribution of slow wave and REM sleep lengths (Rota et al.,
2014).
Examination of CG activity in brain tissue has revealed that the
expression of Per and CGs in neural regions associated with various
cognitive functions (e.g., hippocampus), are expressed rhythmically
throughout the day. The volume of the SCN appears to shrink across
the lifespan, but the total number of AVP positive neurons does
not decline (Swaab et al., 1985). However, when individuals with
dementia of the AD type are examined, a decline in SCN AVP positive neurons has been documented (Swaab et al., 1985). Though this
observation links changes in SCN anatomy with functional changes
in circadian rhythmicity, whether the morphological changes are
consistent with alterations in CG expression within the SCN and
across other neural regions was not described until much more
recently. Cermakian et al. (2011) observed the maintenance of oscillating CG within various brain structures (e.g., pineal gland) in an
AD population, but CG expression in the bed nucleus of the stria
terminalis and the anterior cingulate differed between controls and
an AD population. However, though oscillatory expression patterns
were maintained in AD brains, the cross-regional synchronization
and acrophase (i.e., period of greatest activity) differed in controls and AD brains. This indicates that although CG regulation is
maintained, the master clock is not coordinating the activity across
areas and is consistent with the demonstration that aged mice had
either attenuated expression or lack of rhythmicity of CLOCK and
BMAL1 in many brain areas, except for the SCN (Wyse and Coogan,
2010). The decline of the central oscillator (i.e., the SCN) has been
documented previously (Swaab et al., 1988) and, when combined
with the results obtained by Cermakian et al. (2011) and Wyse
and Coogan (2010), it appears that communication with the SCN is
important for synchronizing CG expression across neural regions.
It is possible that the loss of interregional coherence is contributing
to cognitive impairment.
The underlying cause of these changes is still largely undefined.
Changes to the SCN in AD have been documented (Swaab et al.,
1988), but it is unclear whether the changes in CG expression in
other neural regions are a consequence of the loss of the central
oscillator or are an emergent property of neurological changes associated with AD (e.g., phosphorylated Tau protein accumulation).
The alterations that exist in functional neuroimaging studies in conjunction with knowledge of fiber tracts gained from diffusion tensor
imaging and postmortem tissue analysis implicate degradation of
interregional communication at a structural level and the differences in gene expression patterns seem to corroborate this failure
of effective communication (Bookheimer et al., 2000; Douaud et al.,
2011).
Tranah et al. (2011) examined circadian rhythmicity and the
subsequent development of dementia in a sample of women.
Results from this study indicate that women with decreased
circadian activity rhythms were more likely to exhibit subsequent mild cognitive impairment or dementia (Tranah et al.,
2011). A link between circadian disruption and cognitive health
has also been described whereby circadian therapies (e.g., bright
light therapy) reduce the severity of sun-downing (Khachiyants
et al., 2011). These observations, among others (for review,
see Khachiyants et al., 2011), have lead to the systematic and
controlled investigation into the cognitive effects of circadian disruption.
Despite considerable efforts toward understanding the etiology
of AD, pharmacological treatments for AD remain largely ineffective. A clear link has been established between circadian disruption
and dementia and evidence is beginning to accumulate indicating
melatonin administration in conjunction with exercise may be neuroprotective, at least in animal models (García-Mesa et al., 2012).
Although considerable evidence can be garnered via neuroimaging techniques and epidemiological approaches, the use of animal
models has been, and will continue to be, imperative in describing
the circadian-dementia association.
The link between circadian rhythm degradation and dementia is
well established. Functional impairments in circadian rhythmicity
in dementia patients have been documented since the middle of the
20th century. More recently, physical evidence has been generated
that describes changes in the SCN itself, functional connectivity
across brain regions, and the incorporation of signals from SCN
at peripheral oscillators. However, whether these changes emerge
from degradation of the SCN followed by peripheral CG expression or dementia related pathology alters inputs to SCN from other
brain regions is currently unresolved, but the state of circadian
health is an important predictor of subsequent cognitive decline
and dementia. The relationship between circadian rhythms and
aging is complicated and requires further examination. It is possible
that the solution to age related cognitive decline might be recapitulating the coherence of oscillators across multiple brain regions
resemblant of that observed when the function of the circadian
system is typical.
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
4.3. Obesity and diabetes
There is considerable epidemiological evidence for a relationship between abnormal circadian rhythms and metabolic changes
related to obesity and diabetes in humans (Lund et al., 2001; Pan
et al., 2011). The SCN maintains extensive connectivity with various hypothalamic nuclei associated with metabolism that regulate
the functions of the pancreas, liver, and adipose tissue (Dibner
et al., 2010). In particular, the SCN acts as both an efferent and an
afferent to the dorsal medial nucleus of the hypothalamus (DMH).
These anatomical relationships are corroborated by interactions at
the molecular level (Butler and Silver, 2009), providing metrics for
examining the influence of circadian disruption on metabolism and
vice versa.
Metabolic demand varies across the circadian cycle. As such,
organisms have evolved to regulate energy metabolism across the
circadian cycle. In accordance, lipid concentrations in plasma fluctuate across the circadian cycle (Dallmann et al., 2012). Further,
lipid concentrations continue to fluctuate under controlled feeding
conditions (Dallmann et al., 2012) indicating that the accessibility
of fat stores may vary across the circadian cycle. In addition to the
changes in adiposity that are, at least in part, related to the length
of time an individual is employed on a shift work schedule, women
employed on rotating shift work schedules are at a moderately
higher risk of developing type 2 diabetes/obesity (Pan et al., 2011).
Links between multiple organ systems have also been reported. For
example, patterns of incidence and severity of myocardial events is
altered in diabetic individuals relative to controls (Aronson, 2001).
Large similarities also exist in the altered circadian profile of the
sleep–wake cycle of the elderly and that of young individuals with
diabetes (Kreier et al., 2007).
In humans, Dallmann et al. (2012) revealed that, even when
food administration is controlled, the highest concentrations of
most lipid products peak around midday. This finding demonstrates
that the circadian component of this loop acts independently of
sleep, feeding, and fasting schedules (Dallmann et al., 2012). In
addition to the differences in lipid metabolism, Holmbäck et al.
(2003) revealed numerous time of day effects on glucagon, various
thyroid-related hormones, and cortisol when comparing diurnal
to nocturnal eating. In mouse models this is, at least partially,
due to circadian desynchrony between metabolically active organs
(Bray et al., 2013). Consistent with this idea, animal models have
defined a relationship between mealtime and obesity. Under normal conditions rats restrict feeding to the dark phase of the LD
cycle, but Zucker (fa/fa) rats that possess a leptin receptor mutation that decreases leptin sensitivity resulting in increased food
intake and adiposity begin feeding 2 h prior to the onset of the
dark phase (Mistlberger et al., 1998). When food is restricted to the
dark phase, Zucker rats gain less weight than their ad libitum fed
counterparts, though they still gain significantly more weight than
controls (Mistlberger et al., 1998). Further, mice (Arble et al., 2009;
Bray et al., 2013) fed during the day gain significantly more weight
without eating more food than do animals fed during the night.
Individuals employed on rotating shift work schedules have
higher body mass index (BMI) scores (Di Lorenzo et al., 2003;
Karlsson et al., 2001; Parkes, 2002). Surprisingly, the total number of calories consumed does not vary with the time of the shift
(Lennernas et al., 1995; Reinberg et al., 1979), nor does it differ
between shift and non-shift workers (de Assis et al., 2003a). These
BMI differences are exemplified by altered leptin and ghrelin profiles, a cogent indicator of altered metabolic processes.
In addition to these changes in leptin and ghrelin concentrations, sleep deprivation is also associated with alterations to
the propensity of men and women to overeat following sleep
deprivation (St-Onge et al., 2012). Similar alterations in diet preferences have been noted in shift workers who tend to eat more
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meals/snacks (de Assis et al., 2003b; Reinberg et al., 1979), or consume high carbohydrate (de Assis et al., 2003a,b; Reinberg et al.,
1979) and added fat foods (de Assis et al., 2003b). Further, differences in abdomen-to-hip ratio and total cholesterol were elevated
in a Japanese shift working population (Nakamura et al., 1997).
Although, animal models of circadian disruption have also indicated that there is an association between circadian disruption and
a preference for simple sugars (McDonald et al., 2013), McDonald
et al. (2013) failed to replicate the difference in BMI observed in
several human shift work studies (Di Lorenzo et al., 2003; Karlsson
et al., 2001; Parkes, 2002). Together, these findings indicate that circadian disruption influences food related behavior and metabolism.
Increased adiposity and altered glucose metabolism create a
positive feedback loop that results in more profound adiposity
(i.e., obesity) and poorer glucose metabolism that can culminate
in diabetes or untimely death. Impaired glucose tolerance, which
is thought to be a precursor to Type 2 Diabetes, was observed in
shift workers starting a new rotation (Lund et al., 2001) and in
subjects who experienced simulated shift work (Hampton et al.,
1996). Similarly, in addition to increased serum glucose, Scheer and
colleagues observed decreased leptin, increased insulin, decreased
sleep efficiency, increased blood pressure, and a change in the phase
of the cortisol rhythm in people that were subjected to a seven
day circadian disruption protocol meant to resemble shift work
(Scheer et al., 2009). These finding are consistent with the observations in animal models of alterations to food choice and glucose
metabolism in photically shifted subjects (McDonald et al., 2013;
Deibel et al., 2014). In addition to circadian disruption possibly
contributing to the development of diabetes, diabetes might also
disrupt circadian rhythms associated with metabolism (Albrecht,
2012; Bowden et al., 1999). For example, relatives of individuals
with type 2 diabetes, despite having normal glucose tolerance, had
a disrupted insulin secretion circadian rhythm and decreased glucose uptake when exposed to extended periods of hyperglycemia
(Bowden et al., 1999).
The extensive connectivity that exists between the SCN and
hypothalamic nuclei related to homeostatic functions such as
feeding or satiety provide a mechanistic link between circadian
disruption and disrupted metabolism. Unfortunately, positive feedback loops are created under conditions of obesity that result in
increased adiposity. The links between circadian disruption, adiposity, and altered glucose metabolism likely elicit synergistic
affects in the brain and body that culminate as diabetes, obesity, and
death. Thus, it is important to communicate the increased likelihood of developing metabolic disorders following circadian rhythm
disruption.
4.4. The influence of chronotype and affective disorders
In healthy controls, daily fluctuations in levels of arousal or
attention across the 24 h cycle have been noted that correlate
with alterations in the P300 component of electroencephalographic
event-related potentials (Higuchi et al., 2000). The timing of arousal
patterns can differ across individuals creating chronotype clusters
that exhibit increased activity early or later in the day. Initially, it
was hypothesized that chronotype was merely an effect of preference or experience, but it appears as though there are measurable
differences in the cognition and personality across these two groups
of individuals that cannot be attributed to choice (Goldstein et al.,
2007). In adolescents, an evening-chronotype increases the risk of
poor academic performance and behavioral problems such as drug
addiction (Goldstein et al., 2007).
Daily fluctuations in arousal patterns are a major characteristic of circadian rhythms and disturbances in this cycle tend to be
characteristic of affective disorders (e.g., major depressive disorder; for review see, Monteleone and Maj, 2008). At a genetic level,
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alterations in circadian CGs are associated with an increased likelihood of affective disorder diagnosis (for example, see Ciarleglio
et al., 2011). Originally, it was thought that the affective disorder
elicited circadian disruption, but realigning the circadian clock can
be an effective treatment for mood disorders (particularly Seasonal
Affective Disorder; Monteleone and Maj, 2008) and many pharmacological interventions for mood disorders produce changes to the
circadian system (e.g., lithium lengthens the free-running period
of circadian rhythms in diurnal primates; Welsh and Moore-Ede,
1990).
The bi-directionality of these effects indicate that affective and
circadian systems interact in a reciprocal fashion. Affective disorders exhibit discernable patterns across the 24 h period that differ
from those observed under euthymic conditions. Sleep bout duration is shortened in depressed individuals and abnormal sleep
architecture is often observed (Winokur et al., 2001). Furthermore,
the magnitude of the fluctuation in patterns of arousal is attenuated
in depressed individuals. Additional evidence is found in the strong,
positive correlation between the degree of circadian disruption and
affective disorder symptom severity.
There is also an association between elevated serum glucocorticoids and circadian disruption. For example, abrupt LD shifts
(Caufriez et al., 2002) or chronic transmeridian flight (Cho, 2001;
Cho et al., 2000) results in an increase of the nadir of the cortisol rhythm, or generally increased cortisol levels, respectively.
Increased glucocorticoids in the serum of some patients with
depression (Nestler et al., 2002) supports the hypothesis that
increased cortisol due to aberrant light cycles or circadian disruption may be a contributing factor in the ontogeny of depression.
A mouse model of depression found that an ultradian light dark
schedule (LD: 3.5:3.5) resulted in increased corticosterone and
depression-like behaviors (LeGates et al., 2012).
Bipolar disorder clusters within families and possesses a strong
genetic component (McGuffin et al., 2003). Examination of CGs in
individuals with Bipolar disorder has revealed single nucleotide
polymorphisms (SNPs) in Clock, ARNTL1, pPER3, and Cry2 (Mansour
et al., 2009; Nievergelt et al., 2006). Similarly, a familial gene association study for Cry2, Per1-3, and Timeless concluded that alterations
in all three genes likely increase Bipolar disorder susceptibility (Shi
et al., 2008). During periods when individuals with bipolar disorder are asymptomatic, circadian disturbances are still observed.
Furthermore, there is a link between mood stability and circadian
stability in individuals whereby affective symptoms increase when
circadian disruption is present.
Realigning circadian rhythms appears to reduce depressive
symptoms and medications that alleviate symptoms also reduce
circadian dysregulation. This dissociation strongly suggests that
affective and circadian functions are intertwined. The relationship
between affective disorders and circadian rhythm disruption is further supported by the observations of altered CGs in individuals
with bipolar disorder. The clear link between abnormal circadian
rhythms and affective disorders provides an avenue for the treatment of both conditions.
4.5. Cancer
The link between circadian function and cancer is complex.
Sleep disturbances and disrupted patterns of the underlying molecular oscillations are observed in cancer patients and are associated
with decreased survival rates (Mormont and Levi, 2003). The
concentrations of various molecules related to cell proliferation,
differentiation, and apoptosis cycle across the circadian period
(Blask et al., 2005), as do their ability to affect various biological systems (e.g., SCN), and this machinery appears to become misaligned
in various cancer states. In short, cancer’s molecular machinery acts
on both sides of the circadian system because it takes advantage of
disrupted circadian rhythmicity and then hijacks molecular cascades that result in further disruption the circadian system (Sahar
and Sassone-Corsi, 2009).
Epidemiological and experimental links between circadian
disruption and increased prevalence of cancer have both been
reported (Davis and Mirick, 2006). Circadian disruption is often
affected in disease states, but exogenous influences or lifestyle
choices such as frequent transmeridian travel (Cho, 2001; Cho
et al., 2000) can also affect the system. Shift work is one example of a circadian disrupted lifestyle. Considerable evidence linking
shift work to increased prevalence of cancer lead the International
Agency for Research on Cancer to classify circadian disrupting shift
work as a probable human carcinogen (IARC Report, 2010). The
degree of shift change between different shifts, the type of shift
work schedule employed and the number of years spent working a non-day shift are important mediators of the carcinogenicity
of the shift work schedule. The effects of shift work are similar
across countries and populations (e.g., similar rates observed for
shift working individuals in England and Japan) indicating that
the influence of environmentally induced circadian disruption is
universal to all human populations. This finding is not surprising
given that similar findings have been reported in human and animal
studies (Filipski et al., 2002, 2004; Yang et al., 2011).
A functional CLOCK protein has been implicated with breast carcinogenesis without circadian disruption (Zhu et al., 2011). When
Hoffman and colleagues examined methylation patterns of CLOCK,
hypermethylation of CLOCK promoter reduced breast cancer risk
(Hoffman et al., 2010). Alterations in tumorigenesis in prostate have
also been described and links to CG variants were also observed
(Chu et al., 2007).
The alterations in breast cancer may be the result of interactions
between circadian gene expression and androgens, but there are
likely other systems affected. Another potential mechanism is an
alteration in melatonin levels associated with night-time activity
in individuals employed on shift work schedules. Night-time light
exposure, hence activity, has been linked to alterations in circulating levels of melatonin (Zeitzer et al., 2000). Furthermore, subjects
who do not sleep during the peak hours of melatonin expression
have the highest increased incidence of breast cancer (Davis et al.,
2001). Light exposure during the dark phase of the circadian cycle
is associated with decreased melatonin expression. This may occur
because melatonin is reported to eliminate nitric oxide and other
reactive oxygen species (Reiter et al., 2009).
The association between cancer survival and prevalence rates
and circadian disruption are not limited to just reproductive tissues (Filipski et al., 2002; Gaddameedhi et al., 2011). Sleep–activity
cycles are associated with colorectal and prostate cancer tumorigenesis and survival (for review see Wood et al., 2010). In addition,
pancreatic cancer progresses more quickly in SCN-ablated mice
relative to those with intact circadian rhythms (Filipski et al.,
2002). Similarly, decreased natural killer (NK) cell cytolytic activity
(involved in tumor suppression) and concomitant increased lung
tumor growth were observed in rats exposed to environmental
circadian disruption (Logan et al., 2012). Furthermore, Yang et al.
(2011) report a decline of the circadian system in chronic myloid
leukemia that was partially recovered following cancer treatment.
These patterns also hold true for more prevalent cancers such as
squamous cell carcinoma wherein UV radiation exposure at 4 a.m.
produces a drastic effect on the proliferation of cancer cells relative
to exposure at 4 p.m. (Gaddameedhi et al., 2011). Though this may
seem intuitive at first glance (how often are individuals exposed to
high levels of UV radiation at night?), it has important implications
for individuals traveling to different time zones (i.e., LD shifts) to
spend a great deal of time out-of-doors.
The classification of circadian disrupting shift work as a probably carcinogen highlights the role of the circadian system on
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
cancer. The link between circadian disruption and cancer has been
noted for a range of tissue types (e.g., breast, prostrate, pancreas,
skin) and various time of day affects in oncogene activity have
been observed. These findings should highlight the importance of
considering when treatments should be administered to cancer
patients because time of day effects are likely to be observed.
4.6. Cardiovascular consequences and clock genes
The incidence and severity of cardiovascular events varies across
the circadian cycle (for review see, Manfredini et al., 2005). Cardiovascular events (e.g., a stroke) are more often fatal if they occur early
in the morning (for reviews, see Hastings et al., 2003; Manfredini
et al., 2005). The clock-dependent peripheral oscillator Krüppellike factor 15 (Klf15) mediates the insertion of ion channels in
cardiac tissue critical for myocardial repolarization (Jeyaraj et al.,
2012a). Interestingly, Klf15 is also important for maintaining nitrogen homeostasis, a process that is mediated by feeding (Jeyaraj
et al., 2012b). The link between ion-channel insertion, nitrogen
homeostasis, and feeding behavior provides a potential explanation
for the observed increases in cardiovascular disease in the circadian
disrupted populations.
The link between circadian rhythms and cardiovascular function
is further supported by the examination of heart and renal function
in Tau mutant hamsters. Martino et al. (2008) revealed that aged
Tau mutant hamsters exhibit increased cardiomyopathy and renal
apoptosis. Further, these affects were not noted for young subjects
or subjects that were not required to entrain on the normal, longer
circadian cycle. The observation of pathology in heart and kidney
tissue indicates that the effects of circadian disruption on organ
health are pervasive (Martino et al., 2008). Furthermore, lesions
of SCN ameliorate these changes indicating that it is not circadian
rhythm disruption per se that elicits the ill effects, but the desynchonization between central and peripheral oscillators that elicit
cardiovascular and renal effects (Martino et al., 2008).
The severity and incidence of cardiovascular infarcts vary across
time of day and a relationship between circadian disruption
and increased incidence of cardiovascular events has also been
reported. These effects do not appear to be mediated by circadian
dysregulation alone, but rather are the product of a lack of coherence between the individual’s innate circadian timing system and
the world around them.
5. Lessons from animal models
Whether or not there is a correlation between various diseases
and circadian disruption is equivocal. However, the relationship
between circadian disruption and health in humans is only correlational. Thus, the use of animal models in the study of circadian
rhythm effects is critically important. Animal models have been
used to examine the effects of circadian disruption observed in
dementia, normal aging, or the impacts of being employed on rotating shift work schedules. Furthermore, the observed changes in the
circadian rhythm of organisms following the development of conditions such as diabetes or obesity have also been observed in animal
models. The degree of concurrence between the observations in
humans and animal models provide support for the use of animal
models in the study of circadian rhythm disturbances, health, and
cognition.
5.1. Manipulations of SCN
The manipulation of the SCN under experimental settings
has greatly informed our understanding of endogenous circadian
rhythmicity. The earliest studies examined the effects of SCN ablation, but the advancement of molecular techniques has led to
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the refinement of the study of the influence of the SCN. Though
our understanding of circadian rhythmicity has been bolstered
by the experimental manipulation of SCN, we must expend more
resources toward defining how manipulations of SCN affect the rest
of the brain and body.
The bi-directionality of the SCN connectome makes it difficult
to determine causation within this highly dynamic neural network
(Butler and Silver, 2009; Krout et al., 2002; Webb et al., 2009). In
addition, the SCN functions through the incorporation of exogenous
cues, making it difficult to determine intrinsic from extrinsic modulatory effects. Yet, the bi-directionality and environmental modulation of the SCN are precisely why possessing an endogenous clock
is advantageous (Hut and Beersma, 2011). A clock that was unable
to adjust to environmental demands (e.g., seasonality, parenthood,
etc.) would be detrimental. Thus, the bi-directionality of the SCN
connectome provides an avenue for rapid adaptation and survival
(Okamura, 2004). These factors permit the SCN to align with environmental shifts more quickly than other tissues (Yamazaki et al.,
2000). However, the SCN generates endogenous signals that help
adjust the clocks in other tissues to the updated time.
The SCN’s ability to incorporate exogenous information renders
it susceptible to manipulation in artificial settings (Moore, 1996).
This susceptibility allows the circadian system to be manipulated,
recreating circadian disrupted states often associated with diseases
without additional pathologic characteristics (e.g., neurofibrillary
tangles in AD). In a laboratory setting, a vivarium is typically placed
on a consistent light:dark (LD) schedule (e.g., 12 h of light beginning
at 07:00 h and 12 h of darkness beginning at 19:00 h) and circadian
dysfunction can be elicited by deviating from the original schedule
(Craig and McDonald, 2008). Such manipulations can take many
forms, but the most common are LL, DD, or LD shifts. Typically, male
rodents are used for circadian research due to the increased variability in the circadian rhythms of females associated with estrus
phase. Estrus phase alters the frequency, duration, and onsets of
wheel running behavior in female rodents (Wang, 1923 as cited
in Eikelboom and Mills, 1987). Consequently, it iscan be difficult
to isolate changes in wheel running associated with experimental
manipulation from hormonal effects.
Another, more invasive, approach for perturbing circadian
rhythms is SCN ablation. SCN lesions can be produced in various
ways including electrolytic current administration (Stephan and
Zucker, 1972), the infusion of neurotoxic chemicals (e.g., ibotenic
acid), or surgical excision (for review see, Lesauter and Silver,
1998). Though invasive, the use of these techniques is largely
responsible for identifying the function of the SCN. Ex vivo tissue
samples clearly illustrated that, even in the absence of environmental input, neurons within the SCN can maintain approximate
24 h oscillations (Yamazaki et al., 2000). Furthermore, lesions and
subsequent transplantation studies with Tau mutant hamsters led
to the understanding that neurons in the SCN drive free-running
rhythms (Decoursey and Buggy, 1986; Lehman et al., 1987; Ralph
et al., 1990; Silver et al., 1996a,b). More recently, conditional ablation of BMAL1 within neurons of the SCN of mice has been used
to model the role of CG expression in SCN without perturbing the
neural network itself (Husse et al., 2011).
5.2. Constant illumination paradigms
Constant light paradigms (e.g., LL or DD) are an often used
method for examining the role of light in the circadian system
(Antle et al., 2005; Yan, 2009). The shift from day to night in the
LD cycle is responsible for triggering several metabolic cascades
responsible for rhythm entrainment (Moore, 1996). Eliminating
this trigger through the use of DD schedules unmasks the “freerunning” rhythm of the organism (Antle et al., 2005). In doing
so, it is possible to examine the system without the most salient
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environmental input (i.e., light; Ruby et al., 2002). Both LL and DD
illumination schedules provide meaningful information about how
environmental inputs influence behavior and the SCN (Chabot et al.,
2012; Colwell, 2001).
Free-running rhythms rarely achieve perfect 24 h synchronicity,
so rhythms drift from the true 24 h cycle with relative consistency over time in the absence of Zeitgeber information. However,
the relative maintenance of behavioral circadian rhythmicity does
not mean the system is functioning normally. Though rhythmic patterns of activity are still expressed, the concentrations
of circadian-associated molecules (e.g., melatonin) are attenuated
under DD conditions (Goldman, 2001). LL alters circadian rhythmicity at the level of the endogenous oscillators (i.e., CGs) by
attenuating the magnitude of the fluctuation of various molecules
(e.g., melatonin peaks minimized). Circadian-dependent changes
in body temperature and activity levels also disappear (Yan, 2009)
and the coherence of the oscillations within the SCN are also compromised under LL conditions (Morin, 1999).
The 24 h cycle can be disassembled in other ways. For example,
introducing multiple periods of light and dark over the 24 h cycle
induces splitting of the circadian rhythm, especially in hamsters
(Morin, 1999). This splitting can be observed at both the behavioral level (e.g., locomotion) and at the cellular level (Yan, 2009).
Hamsters exhibiting split circadian rhythms show distinct neuronal clusters within the SCN that have varying oscillation patterns.
Activity within each cluster corresponds to a unique photoperiod
(Morin, 1999). In other words, multiple independent clocks begin
to emerge and populations of neurons within SCN diverge and
then coalesce to form their own oscillatory network (i.e., there
is potential within the SCN to represent multiple, discrete circadian rhythms). Furthermore, the specific neuronal populations
that emerge as distinct oscillators may depend on which species
is examined or what paradigm is used. For example, hemispheric
splitting usually occurs when animals (e.g., hamsters) are exposed
to LL whereas dorsomedial:ventrolateral splitting tends to occur
when animals are placed on 22 h LD cycles (Lee et al., 2009).
The manipulation of circadian rhythms through the elimination
of the LD cycle is advantageous for many reasons, but caution must
be taken in interpreting the results of such studies. Though subjects are able to maintain some semblance of circadian rhythmicity
under these conditions, they are reduced in amplitude. Furthermore, the animal model employed will produce vastly different
affects in the SCN. Thus, it is critically important for researchers
to determine the aims of their scientific endeavors and chose the
animal model and type of manipulation that best fits with the
phenomena they wish to investigate. For example, a LL paradigm
may not be the most advantageous for studying the role of daily
fluctuations in melatonin profiles.
5.3. Shifting the LD cycle
Manipulating Zeitgebers (e.g., introducing an artificial sunrise
in the middle of the night) provides another method for disturbing
circadian function that does not rely on mechanical alteration of
neural regions directly. The purposeful manipulation of circadian
time using Zeitgebers is called a LD shift. LD shifting is a technique
used to examine biological systems while the circadian system is in
disarray. By examining the circadian system when it is desynchronized, it is possible to examine the effects of circadian disruption
independently, rather than as one component in a suite of symptoms often observed in disease states (e.g., dementia).
LD shifts are typically accomplished by altering light schedules,
which can be effective because the SCN relies heavily on retinal
input (Morin and Allen, 2006). Changing the LD schedule affects CG
expression in the SCN of hamsters (Tournier et al., 2003; Tournier
et al., 2009). In rats, LD shifts are typically induced by adjustment
of the light schedule (Illnerova and Sumova, 1997). Alternatively,
SCN entrainment and shifts in daily patterns of activity can also be
induced through the use of restricted eating, drinking, and exercise
schedules. It is thought, however, that these cues act primarily on
mechanisms outside the CNS and, therefore, influence the SCN in
a less direct fashion (Dallman and Mrosovsky, 2006; Girotti et al.,
2009; Hirota and Fukada, 2004).
LD shifting can proceed in two directions (Pittendrigh and Daan,
1976). The subject’s circadian rhythm can be brought forward in
time (i.e., phase advance) or reversed (i.e., phase delay). LD shifts
can be accomplished in numerous ways, but the two most common are light pulses when assessing phase response curves (Antle
and Silver, 2005; Yan, 2009) or adjustment of the LD schedule
when assessing the influence of circadian disruption on behavior
(Craig and McDonald, 2008; Devan et al., 2001; Yan, 2009). The
administration of light, and to a lesser extent dark, pulses is most
useful when employed under constant lighting conditions. Excluding relatively few species of diurnal rodents (Arvicanthis niloticus;
Blanchong et al., 1999), most nonhuman animal research of mammalian circadian rhythmicity is conducted in nocturnal species.
Light pulses elicit LD shifts more strongly than dark pulses in both
diurnal (e.g., humans) and nocturnal (e.g., rats) species, so light
pulses are more commonly used in circadian research (Pittendrigh
and Daan, 1976).
Another method of inducing circadian disruption is to constantly change circadian time through manipulations of light or
activity schedules. In this method, the light schedule is advanced
or delayed each day by a number of hours. The constant updating
demands that the SCN continually re-entrain to the new circadian
time. If the duration of these disruptions is relatively brief, it is
possible to use it to model conditions such as jetlag. Conversely,
if the duration of disruption is prolonged, it can be used to model
conditions such as shift work (Reddy et al., 2002; Salgado-Delgado
et al., 2008). Numerous methods have been employed as models
for day-to-day circadian disruption in human lives. Unfortunately,
scientists are attempting to mimic a phenomenon that is woefully
heterogeneous. For example, scheduling practices employed in various hospitals, and at times across departments within a single
hospital, for rotating shift workers are remarkably unstandardized
(Zelinski and McDonald, unpublished observation). Thus, animal
models of circadian disruption must rely on techniques that elicit
disruption that are not perfectly analogous to what is occurring in
human populations, but that do mimic the key component of shift
work which is circadian disruption (e.g., LD shifting).
LD-shifting is also an excellent method of mimicking circadian
disruption that is typically observed in association with several disease states. The impairments elicited by LD-shifts resemble the circadian rhythm abnormalities observed with aging, AD (for review,
see Benca et al., 2009; Van Someren, 2000), and cancer (Blask et al.,
2005). Other relationships exist in terms of normal brain function and daily circadian rhythms including, but not limited to, the
increased incidence of stroke in early morning (Lewis et al., 2010),
altered hormone profiles that contribute to a vast array of behaviors (Few et al., 1987), and time of day effects on cognitive ability
(Bennett et al., 2008). By manipulating circadian rhythms independent of other disease or condition related pathologies a clearer
picture of what circadian rhythms contribute can be attained.
6. Effects on learning and memory
Cognitive performance fluctuates across the circadian cycle in
various cognitive/affective disorders such as dementia, depression,
and schizophrenia (Benca et al., 2009). Initially, this decline was
thought to be a normal part of the aging process rather than part
of a disease state. However, Antoniadis et al. (2000) revealed
that circadian rhythm status is a better predictor of cognitive
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
performance than age in hamsters. Circadian disruption is a
fundamental characteristic of dementia (Van Someren et al., 1996).
Therefore, understanding the role of the circadian disruption is
important for delineating the etiology of these disease states.
A considerable body of evidence exists describing the negative
affects of circadian disruption on navigational ability and spatial
memory that depend on the hippocampus. Disruption of circadian
rhythms via SCN lesions (Phan et al., 2011) or the mutation of CGs
(Per1−/−: Jilg et al., 2010; Cry1−/− Cry2−/−: Van der Zee et al.,
2008; Per2−/−: Wang et al., 2009) cause impairment in various
hippocampal dependent tasks in a range of species. For example, LD shifted mice (Loh et al., 2010) and hamsters (Ruby et al.,
2008) exhibit impairment on hippocampal dependent contextual
fear conditioning and novel object recognition tasks, respectively.
Morris water task (MWT) performance gauged by platform location memory (i.e., place memory) is also impaired when circadian
disruption is induced via LD shifts (Devan et al., 2001). Though
retention was impaired, acquisition was no different from controls.
This finding suggests that processes related to consolidation may
be impaired. If the period of circadian disruption is extended, both
acquisition and retention of the platform location are disrupted
(Craig and McDonald, 2008). However, circadian desynchronization was present during both training and testing which may have
strengthened the association. In other words, circadian rhythm disruption at both time points may link the training to the testing
because of state similarity. To assess this possibility, one must conduct a study wherein circadian rhythmicity is only disrupted during
training or testing, not both.
Novelty is an important contributor to the salience of learning. Consequently, it is possible that by altering the circadian time
of training episodes (i.e., increasing the novelty of each learning episode) cognitive performance is enhanced. It could also be
the case that the increased variability induced by altering circadian time potentiates the memory trace, thereby increasing the
strength of the association (i.e., memory retention). Subsequent
work using the same paradigm employed by Craig and McDonald
(2008) revealed impairments following both acute (Zelinski et al.,
2014a) and chronic (Zelinski et al., 2013) circadian disruption when
training and testing occurred while circadian rhythms were stable,
but rats were subjected to LD shifts in the interim.
Zelinski et al. (2014a) report partial hippocampal dependent
impairments on the standard, spatial version of the MWT following acute, or a relatively brief period of circadian disruption.
Conversely, performance on tasks dependent on regions such
as the dorsal striatum and prefrontal cortex were unaffected
(Zelinski et al., 2014a). When the period of circadian disruption
was extended, the impairments on MWT were potentiated and
performance on an Stimulus–Response (S–R) visual discrimination
version of an 8-arm radial maze task that required proper function of the dorsal striatum and prefrontal cortex was also affected
(Zelinski et al., 2013). Importantly, in both these experiments, training and consolidation occurred prior to circadian rhythm disruption
indicating that long-term memories are susceptible to circadian
disruption in otherwise healthy individuals.
Age related cognitive decline is more pronounced if circadian rhythmicity is also impaired (Antoniadis et al., 2000;
Krishnan et al., 2012). The ability to develop an appetitive place
preference is impaired in aged hamsters with poorer circadian
rhythms than their age-matched, but circadian-intact counterparts
(Antoniadis et al., 2000). Using drosophila Per-KOs, Krishnan et al.
(2012) observed an increase in neurodegeneration when Per1 was
removed from the neural milieu. These animal studies begin to
describe the role of circadian dysregulation in the ontogeny of agerelated cognitive decline rather than unrelated, but concomitant,
declines in cognitive functions and circadian rhythmicity (Cohen
and Albers, 1991; Karatsoreos et al., 2010).
93
Time-stamping refers to a theory whereby performance on
learning and memory tasks is enhanced when training and testing occur at the same circadian time (Holloway and Wansley,
1973). Original reports supported the theory of time stamping,
but subsequent assessment of this phenomena failed to show time
stamping on several behavioral tasks including place learning on
the MWT, discriminative contextual fear conditioning, appetitive
contextual conditioning, and S–R associations on the 8-arm radial
maze (McDonald et al., 2002). Alterations in CG expression has been
linked to cognitive decline in AD in humans (Cermakian et al., 2011;
Wu and Swaab, 2005). Variation in the expression of CGs across
the LD cycle exhibit extensive phylogenetic preservation (e.g.e.g.,
Aplysia; Lyons et al., 2005), lending support for the use of animal
models toward understanding the relationship between circadian
dysfunction and human health.
Time stamping is strong in hamsters (Ko et al., 2003), but inconsistent across rat strains (McDonald et al., 2002). For example,
time-stamping is noted in Wistar strains, but is absent in Hooded
Long-Evans rat strains (Cain et al., 2004). One behavioral measure
that exhibits time stamping is the preference for performing specific activities at the same circadian time (Ko et al., 2003; Ralph
et al., 2002). It has been reported that mice acquire and retain fear
conditioning better when training and retention are conducted at
specific times of day, though this finding requires corroboration
(Chaudhury and Colwell, 2002). Similarly, others have observed
stronger contextual fear conditioning memories in mice when
tested at the time of peak mitogen activated protein kinase (MAPK)
expression in the hippocampus (Eckel-Mahan et al., 2008). This
effect might explain why in an appetitive contextual conditioning
paradigm, hamsters favored a context associated with a running
wheel more strongly at specific times of day (Ko et al., 2003). An
alternative explanation for the observed results is fluctuating motivation across the circadian cycle.
Time place learning (TPL) is similar to time stamping in that
the animals are trained and tested at the same times daily, except
time serves as a discriminative stimulus. In daily TPL studies, a
reward or aversive stimuli is located in one place in the morning
and another in the afternoon (for review, see Thorpe and Wilkie,
2006). Some studies have demonstrated that rats use circadian
timers to acquire time-place associations (Deibel and Thorpe, 2013;
Mistlberger et al., 1996.). Although, daily TPL does not depend on
an intact SCN (Mistlberger et al., 1996), CGs are required, because
Cry1−/− Cry2−/− KO mice failed to acquire a daily TPL task involving the avoidance of foot shocks (Van der Zee et al., 2008). It is
unclear whether the impairment is due to the absence of CGs in the
peripheral oscillators that mediated performance, or if these oscillators are receiving an aberrant signal from a malfunctioning SCN.
Why circadian disruption preferentially elicits hippocampal
dependent memory effects relative to other neural regions is a
large question with several possible answers. It is probable that
the increased neural plasticity characteristic of the hippocampus
is one avenue by which circadian disruption impacts learning and
memory. Circadian affects have been noted on performance of
various hippocampal dependent tasks (e.g., place learning, MWT,
TPL) and some mechanisms (e.g., CG KOs) have been proposed, but
why this is the case in the hippocampus preferentially, are still
largely undefined.
6.1. Mechanisms for hippocampal effects (Fig. 3)
The potential mediators of circadian disruption induced hippocampal memory impairments are numerous. Inconsistent CG
expression across multiple regions may be involved and it is
possible that the desynchronization in CG signaling in peripheral
and central oscillators affects hippocampal plasticity or long
term potentiation (LTP). Further, it is possible that differential CG
94
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
Circadian Disruption
Hippocampal Formation
Clock Genes
No longer
rhythmically
expressed
Phase is altered
Plasticity
Impaired LTP
Disrupted signal
transduction pathway
(e.g., MAPK)
Corticosterone
Elevated nadir
Potentiated cort
response to stressful
stimuli
Impaired hippocampal dependent memory
Fig. 3. Circadian disruption is associated with impaired hippocampal dependent
memory. The timing of circadian disruption and behavioral training and testing have
been manipulated such that memory encoding, consolidation, and retrieval could be
examined separately revealing that circadian disruption likely impacts all aspects
of hippocampal dependent memory (Craig and McDonald, 2008; Devan et al., 2001;
Zelinski et al., 2013). There are numerous mechanisms by which this could occur
including alterations to CGs, plasticity mechanisms, or stress.
expression alters cholinergic input to parahippocampal regions.
Careful examination of plasticity mechanisms and the interregional
coherence of CG expression should be examined.
The observed behavioral effects of circadian disruption are corroborated by various morphological changes and altered plasticity.
CGs are rhythmically expressed in the hippocampus (Feillet et al.,
2008; Jilg et al., 2010; Wakamatsu et al., 2001; Wang et al., 2009).
Hippocampal neurogenesis is reduced following circadian (Gibson
et al., 2010) and sleep disruption (Meerlo et al., 2009). For example,
restricted feeding caused the mPer1 and mPer2 mRNA acrophase
in the hippocampus to occur in the daytime, rather than at night
(Wakamatsu et al., 2001). Although, the anticipatory behavior displayed in restricted feeding paradigms is not mediated by the SCN,
this behavior maintains a circadian component that involves the
hippocampus. Alternatively, rhythmic expression of CGs and their
protein products could be abolished in the hippocampi of circadian
disrupted animals. CLOCK and BMAL1 were downregulated, and in
some hippocampal areas no longer rhythmically expressed in aged
mice (Wyse and Coogan, 2010). In addition to causing impairment
on the MWT, SCN lesions also eradicate the diurnal expression of
enzymes (adenylyl cyclase and MAPK), which are involved in the
signal transduction pathway in the hippocampus (Phan et al., 2011).
MAPK is involved in the regulation of downstream transcription
and it is possible that the MAPK pathway regulates CG expression
(Eckel-Mahan et al., 2008). It is likely that the mismatch in the phase
of CG expression in the hippocampi vs. SCN of circadian disrupted
animals negatively impacts hippocampal plasticity.
If hippocampal oscillations are dependent on the SCN is still
undefined. Some suggest that the oscillatory mechanisms in the
hippocampus are autonomous because the diurnal rhythm in LTP
was preserved in cultured tissue (Wang et al., 2009). However,
SCN neurons also sustain rhythmicity for days in tissue cultures
so the effects of the SCN on the hippocampus could be long lasting (Colwell, 2011). Perturbations in structures associated with
learning and memory could be the result of desynchronization of
peripheral (e.g., hippocampus) and central (e.g., SCN) oscillators.
Conversely, arrhythmicity could also be expressed within nuclei of
individual oscillators.
Plasticity, primarily LTP, is thought to underlie hippocampaldependent long-term memory. The link between plasticity and
circadian rhythms is supported by the observation that LTP dependent population spikes and field excitatory post-synaptic potentials
(fEPSP) in CA1 are greater at night (Barnes et al., 1977; Chaudhury
et al., 2005). In addition, Per2−/− mice display less fEPSP (i.e., LTP)
than WT mice during trace fear conditioning (Wang et al., 2009).
Place cell firing in CA1 also expresses rhythmic patterns of activity
in healthy rats (Munn and Bilkey, 2012). Conversely, experiencedependent plasticity in CA1 place cells is reduced in aged rats and
is corroborated by the concomitant impairment on the MWT (Shen
et al., 1997).
Additional theories have been proposed to account for the
effects of LD shifting on hippocampal dependent memory. If memory consolidation occurs during post training sleep episodes (for
review see, Diekelmann and Born, 2010), it is possible that disrupted sleep episodes in LD shifted animals results in impairments
on hippocampal dependent tasks. However, the total amount of
sleep (Castanon-Cervantes et al., 2010), or ratios of REM and NREM
sleep do not change after single (Loh et al., 2010) or multiple phase
advances (Rota et al., 2014) in mice and rats, respectively. Although
the total amount of REM and NREM sleep does not change after
chronic LD shifting, the distribution of the length of these sleep
episodes is altered only after and not during LD shifting (Rota et al.,
2014). Interestingly, these data might suggest why animals can
acquire hippocampal dependent tasks during shifting, but still display impaired long-term retention when tested after LD shifting.
Alternatively, performance on hippocampal dependent tasks
could be impaired in LD shifted subjects because aberrant light
schedules might increase serum corticosterone levels. While
LD shifting disrupts the circadian expression of corticosterone (Filipski et al., 2004), some studies have failed to find
increased corticosterone levels in animals directly after LD shifting (Castanon-Cervantes et al., 2010; Kort and Weijma, 1982; Sei
et al., 2003). However, increased corticosterone values in hamsters
(Gibson et al., 2010) and degus (Mohawk et al., 2005), have been
observed following LD shifting when the sampling was conducted
during or slightly after the LD shifting, respectively. Similarly, we
have recently demonstrated that elevated corticosterone is only
observed during the second half of our chronic photoperiod shifting
paradigm in female Long-Evans rats (Deibel et al., 2014). Increased
corticosterone in mice exposed to an ultradian LD schedule has also
been reported (LeGates et al., 2012). Although resting serum corticosterone was unaffected by LD shifting, it is possible that the subsequent presentation of a stressor could elicit potentiated patterns
of responses. For example, animals without increased corticosterone after LD shifting (Loh et al., 2010) or restraint stress (Wright
et al., 2006) had significantly elevated corticosterone during behavioral testing indicating functional adaptation following LD shifts.
6.2. Models of diabetes, obesity and food preferences
Abnormal expression of CGs in liver, adipose tissue and muscle
likely impact feelings of hunger, satiety, or restlessness. The aberrant CG expression induced by an episode of circadian disruption
or as a result of CG mutations result in altered feeding patterns,
glucose/insulin metabolism, and hepatic responses. How the body
processes nutrients is altered following circadian disruption and
these effects are likely heightened because circadian disruption also
influences activity profiles.
Circadian disruption is characteristic of metabolic diseases such
as obesity. Feeding behavior is constrained by a complex series of
feedback loops that involve several molecules (e.g., ghrelin, leptin).
These cascades are one example of endogenous peripheral oscillators that provide signals to the SCN about time of day (Challet,
2007). However, this feedback is reciprocal. It is possible that
circadian disruption affects gene expression and molecular concentrations of CGs, leading to altered experiences of hunger, satiety, or
food preferences. These erroneous signals could underlie improper
diet and poor food choice in individuals with disturbed circadian
rhythms (e.g., shift workers). Circadian disruption could increase
the likelihood of making poor diet choices because it has altered
E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
the molecular underpinnings of food choice/cravings. Consistent
with this idea, Bray et al. (2013) have observed that mice placed
on a time-of-day restricted feeding schedule gain more weight and
consume more calories more quickly when presented with food.
Abnormalities in the expression of CGs in liver, adipose, muscle
and heart tissue were also reported (Bray et al., 2013) indicating a
probable disconnect between the SCN and downstream circadian
oscillators.
Corroborating evidence for the relationship between circadian
rhythms and obesity can be found in studies of mutant strains of
rodents. For example, Clock, Per, and Bmal mutant mice exhibit
abnormal feeding patterns and altered glucose, insulin, and hepatic
responses (for review, see Delezie and Challet, 2011). In addition, mutant strains of mice and rats with abnormal adiposity
and glucose homeostasis have revealed several important interactions between circadian rhythmicity, obesity, and diabetes (Delezie
and Challet, 2011). The circadian abnormalities in these strains
were not intended, strengthening the argument for a relationship
between abnormal metabolism in the clock and peripheral tissues.
The impact on metabolism in various CG KO mice strains has also
been observed in rats. Zucker fatty rats exhibit elevated adiposity and attenuated sleep–wake and activity–rest rhythms, and this
observation is consistent with circadian abnormalities in obese
humans (Vorona et al., 2005). Abnormal circadian rhythmicity in
fluctuating corticosterone concentrations has also been observed
in Per2 mutants (for review see, Dickmeis, 2009) and it is likely
that interactions between corticosterone and metabolic cascades
are occurring.
Environmental manipulation of circadian rhythms can also elicit
profound changes in responses to high fat diets and fasting. Shortened LD cycles (e.g., 20 h vs. 24 h) produce increases in adiposity
(Karatsoreos et al., 2010; Oishi et al., 2009) and rates of cardiovascular disease in mice and increased body mass in rats (Vilaplana
et al., 1995). Further, placing mice on a 20 h LD cycle results in
weight gain, altered hormone expression, and changes in dendritic
morphology in prefrontal cortex (Karatsoreos et al., 2010). Similarly, circadian disruption via environmental manipulation can
cause an increase in body weight. However, this effect is typically
only observed when the animals are fed ad libitum (Oishi, 2009;
Salgado-Delgado et al., 2010; Tsai et al., 2005), as no weight gain is
observed when food is restricted to the dark period (Bartol-Munier
et al., 2006; Salgado-Delgado et al., 2010). In addition to body
weight, impaired glucose tolerance (Deibel et al., 2014); McDonald
et al., 2013; Oishi, 2009) and the abolition of glucose rhythmicity (Salgado-Delgado et al., 2010) have been reported in animals
whose circadian rhythms are disrupted by adjustments to the LD
cycle. Alternatively, mice that were rendered arrhythmic via SCN
lesions (Coomans et al., 2013), or by knocking out Bmal1 (Shi et al.,
2013), either gained weight or had changes in body composition
and developed insulin resistance, which in addition to being a precursor to Type 2 Diabetes, leads to increased serum glucose values.
Controlled studies like those of Bray et al. (2013), Dallmann et al.
(2012), and Holmbäck et al. (2003) have revealed that these effects
are the result of metabolic oscillations within the circadian system rather than a consequence of caloric intake, sleep, or fasting
alone.
Other lifestyle choices or patterns of behavior can influence or
be influenced by changes in circadian function. For example, circadian disruption can alter the amount, duration, or timing of wheel
running. It is possible that the decrease in activity often observed
with circadian disruption could model a more sedentary lifestyle in
humans. Reduced activity is associated with weaker performance
on a variety of cognitive tasks and reduced recovery following
injury (Cotman and Berchtold, 2002). The decline in activity level
could provide an additional “hit” to the system that can result in a
greater decrease in health when circadian rhythms are disrupted.
95
Taken together, these findings implicate exercise as an important mediator in the decline of neural and cognitive function
associated with circadian disruption. Neurogenesis appears to be
negatively impacted by circadian disruption (i.e., less neurogenesis; Gibson et al., 2010; Meerlo et al., 2009). On the other hand,
exercise increases both the birth and survival of new neurons in the
hippocampus (Olson et al., 2006; Spanswick et al., 2011) and preliminary findings of Zelinski et al. (2014a) indicate that voluntary
running wheel activity preserves cognitive function in photically
shifted male rats.
6.3. Reproductive effects
Circadian rhythm disruption negatively impacts reproduction
by altering hormone profiles, reproductive timing, and fertility.
However, the rigors of parental care in many species may have
resulted in variation in the resilience of its members to circadian disruption, particularly if parental demands target one sex
preferentially. Maternal circadian rhythms are important for the
coordinating the circadian rhythms in her offspring (Reppert and
Schwartz, 1986) and evidence is beginning to accumulate indicating that the offspring are negatively affected if their mother has
experienced circadian disruption (Zelinski et al., 2014b). Together
these results imply that the influences of circadian disruption need
to be examined beyond the scope of the individual as they could
impact future generations through epigenetic factors.
Many avian species, particularly songbirds, reproduce seasonally (i.e., during periods of prolonged day length). Consequently,
several physiological correlates both in the brain and in the
reproductive system exhibit cyclical, seasonal patterns of activity
and relative senescence (Dawson et al., 2001). Melatonin receptor
expression is strongly correlated with the enhancement of these
regions through processes such as neurogenesis or increases in
steroid receptor expression (Bentley et al., 2003; Dawson et al.,
2001; Strand and Deviche, 2007). Melatonin receptor density
increases in concordance with day length. In several avian species,
the timing of mating is an incredibly important factor in the success or failure of the reproductive venture, because poorly timed
offspring have greatly reduced survival rates (Sheldon et al., 2003).
The rigors of offspring care necessitate constant effort, especially immediately after parturition (MacBeth and Luine, 2010).
The demands of neonatal mammals (e.g., nursing, stimulation of
urination/defecation, exogenous thermoregulation) necessitate
the attention of the mother across the entire 24 h period, at least
initially. The presence of such demands support the hypothesis
that circadian rhythms in females should be more robust relative to males because the demands largely target the female.
Consequently, the impact of circadian rhythm disruption should
be attenuated in females compared to their male counterparts.
These advantages might be present for some aspects of health, but
not others. For example, the need to be cognizant of immediate
surroundings (e.g., predator evasion/detection) may overpower the
need to make advantageous metabolic decisions (e.g., complex vs.
simple carbohydrates). Examination of sex differences following
circadian disruption in rodent models has supported this hypothesis when examining cognitive performance) (Zelinski et al., 2013,
2014a). Further, CG expression in the gonads is associated with
ovulation and spermatogenesis and desynchronization is associated with abnormal function and decreased fertility (Kennaway
et al., 2012).
Research is beginning to show the deleterious effects on
offspring of circadian disruption during pregnancy. Several studies have reported increased rates of preterm births and lower
birth-weights among women employed on shift work schedules,
though there is still some debate regarding whether circadian
disruption is truly the mediating factor (Nurminen, 1998). Prenatal
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E.L. Zelinski et al. / Neuroscience and Biobehavioral Reviews 40 (2014) 80–101
circadian disruption in rats alters glucose metabolism and increases
adiposity across the lifespan (Varcoe et al., 2011).
7. Conclusions
Circadian function, health, and cognition exist as an incredibly
complex reciprocal network in which all components exhibit multidirectional effects on the other systems. Under healthful conditions,
these interactions create a robust, dynamic circuit that allows the
organism to adapt well to its environment. However, when disease
strikes a single component of the system, the effects propagate a
cascade that can lead to a decline in the function of all systems. The
strength of the circadian system is its adaptability. Unfortunately,
the adaptability of the SCN can also compromise the integrity of the
system by incorporating and/or producing erroneous signals. These
erroneous signals can take the form of environmental perturbations
(e.g., travel across numerous time zones or LD shifting) or as a consequence of aberrant function within the body (e.g., diabetes, mental
illness). Ultimately, the misalignment of the systems can perpetuate disease states that can culminate in prolonged, persistent, or
permanent damage. Consequently, it is important to identify the
mechanisms and effects within the system from a genetic level to
an epidemiological one. Only with complete understanding will it
be possible to mitigate the effects of abnormal circadian function
on health.
Acknowledgements
The authors wish to thank Ian D. Blum, Andrew N. Iwaniuk,
and anonymous reviewers for their insightful comments during the
preparation of this manuscript.
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