Review
published: 06 February 2017
doi: 10.3389/fneur.2017.00025

Systems Biology-Derived Discoveries
of intrinsic Clocks
Arthur Millius1 and Hiroki R. Ueda1,2*
1
Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Suita, Osaka, Japan, 2 Department of Systems
Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

Edited by:Timo Partonen,
National Institute for Health andWelfare, Finland
Reviewed by:
Zheng (Jake) Chen,
University of Texas Health Science
Center at Houston, USA
Angela Moreira Borralho Relógio,Charité Medical University of Berlin,
Germany*Correspondence:
Hiroki R. Ueda
uedah-tky@umin.ac.jp
Specialty section:
This article was submitted toSleep and Chronobiology,
a section of the journal
Frontiers in Neurology
Received: 25 November 2016
Accepted: 17 January 2017
Published: 06 February 2017
Citation:
Millius A and Ueda HR (2017)
Systems Biology-Derived Discoveriesof Intrinsic Clocks.
Front. Neurol. 8:25.
doi: 10.3389/fneur.2017.00025

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A systems approach to studying biology uses a variety of mathematical, computational,
and engineering tools to holistically understand and model properties of cells, tissues,and organisms. Building from early biochemical, genetic, and physiological studies,systems biology became established through the development of genome-wide
methods, high-throughput procedures, modern computational processing power, andbioinformatics. Here, we highlight a variety of systems approaches to the study of
biological rhythms that occur with a 24-h period—circadian rhythms. We review how
systems methods have helped to elucidate complex behaviors of the circadian clock
including temperature compensation, rhythmicity, and robustness. Finally, we explain
the contribution of systems biology to the transcription–translation feedback loop andposttranslational oscillator models of circadian rhythms and describe new technologies
and “–omics” approaches to understand circadian timekeeping and neurophysiology.
Keywords: systems biology, models, theory, RNA sequencing, neurophysiology, circadian rhythm, ribosome
profiling

SYSTEMS BIOLOGY—A BRIEF HISTORY
In contrast to a reductionist approach, systems biology emphasizes the interaction of components
rather than the components themselves: to see the forest for the trees. This holistic approach is
not a modern idea, but can be traced as far back as the Greek Aristotle “…the totality is not, as
it were, a mere heap, but the whole is something besides the parts…” In the modern era, Karl
Ludwig von Bertalanffy is generally credited as one of the founders of general systems theory
with his model of individual cell growth in the early 20th century (1). Later, the Dutch physicist
Balthasar van der Pol working with electric circuits developed his eponymous equation to describe
relaxation oscillations (2), which was used for theoretical models of neuronal systems (3, 4). In the
1950s, Alan Hodgkin and Andrew Huxley described the first mathematical model of an action
potential propagating along a neuron, which famously predicted the existence of ion channels
before their experimental discovery (5), and Alan Turing proposed a reaction–diffusion system
in “The Chemical Basis of Morphogenesis” to explain how an initially homogenous system—theembryo—forms patterns through the action of morphogens (6).
These early systems models of cellular behavior were overshadowed by the excitement of themolecular biology revolution. Geneticists and biochemists learned to devise assays to measure theimpact of single genes and single enzymes. In the 1970s, Ronald Konopka in Seymour Benzer’s lab
used chemical mutagenesis to screen fruit flies for defects in their rhythmic emergence from thepupae state. He discovered three alleles of the Period gene, which is one of the earliest examples
of a gene determining behavior in an organism (7). For the next 30 years, circadian biologists
mostly pursued reductionist approaches similar to Konopka’s strategy to examine circadian

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behaviors in different organisms by knocking out single genes
or isolating individual tissues.
The era of functional genomics and next-generation sequencing has begun to shift the balance back toward systems biology. In the following sections, we review the contributions ofmathematical models, microarray technology, RNA sequencing,proteomics, and neurophysiological approaches to systematicallydissect circadian behavior and uncover new modes of regulation(for an overview, see Figure 1).

circadian rhythms (22–30). In the next subsections, we discuss
how these and other models contributed to our understanding of
the systems properties of circadian rhythms.

Periodicity and Design of theTranscription–Translation Feedback Loop

The period of a biological rhythm is tied to the 24-h rotational
movement of the Earth. Organisms across different domains oflife evolved timing mechanisms called biological clocks to coordinate function and behavior to specific times of the day (31).
Each day environmental cues such as light and temperature reset
your biological clock in a process called entrainment (32). Foodcan also entrain biological rhythms by affecting clock machineryin the liver (33, 34). Entrainment allows us to recover from thejet lag inducing effects of airplane travel by either advancing ordelaying the phase of the circadian clock. Response to externalcues is not instantaneous—timekeeping of the circadian clock
persists, which is why we feel jet lagged in the first place.
Flexibility in period length was apparent from the earliest
studies of mutant organisms (7, 35, 36). Systematic screeningof chemical libraries also revealed chemical compounds that
could alter period length by targeting specific clock proteins(24, 37–44). Pharmacological and/or genetic perturbation
could extend the range of periods in the fibroblast from 27 to
54 h (41) and suprachiasmatic nucleus (SCN) from 17 to 42 h(45). Investigating why some mutant organisms have short orlong periods revealed the molecular mechanisms of circadianrhythms and researchers could begin to test models by designingand manipulating components in the circuit. They were perhaps
inspired by synthetic bacteria genetic circuits that recapitulatetranscriptional oscillations (46) and bistable switches (47). For
circadian rhythms, mathematical modeling guided constructionof a synthetic 26-h oscillator based on siRNA-based silencing ofa tetracycline-dependent transactivator (48). Construction of a
mammalian promoter/enhancer database allowed researchers to
identify high-scoring or low-scoring cis-elements and validate
high- or low-amplitude expression, respectively, in cells (49),which enabled synthetic reconstruction of different circadianphases in cells by mixing combinations of promoter elements(50, 51). Researchers have also implemented artificial photic
input pathways to clock cells to investigate singularity behavior,in which the circadian clock is reset after perturbations of different strengths and timing (52). More recently, researchers havesucceeded in replacing the endogenous repressor in mice witha tunable one (53) and artificially manipulating the molecular
circuitry of pacemaker cells in the brain (54, 55) to alter period
length. These synthetic biology reconstruction experiments probe
the sufficiency of circadian networks to generate oscillations andoscillations of different periods as well as test ideas about how
network components interact and function within cells.

MODELING THE SYSTEMS PROPERTIESOF CIRCADIAN RHYTHMS
The circadian clock is an interconnected network—a networkof small molecules and metabolites, a network of genes andproteins, and a network of cells, neurons, and tissues. At eachlevel, the interacting network of components can create complex
behaviors. These systems-level properties include three defining
characteristics of circadian rhythms: (1) periodicity—rhythms
are autonomous with a period that matches the daily 24-h rotation of the Earth, (2) entrainment—rhythms can be reset byenvironmental cues such as light, temperature, or food intake,and (3) temperature compensation—periodicity of rhythms
persistent despite fluctuations over physiologically relevanttemperature ranges.
Before genetics led to the identification of molecular components governing a transcription and translation feedback loopthat underlies the mechanism of circadian oscillation in manyorganisms, theoretical studies sought to model how oscillation,
periodicity, entrainment, and temperature compensation could
arise. The first was Goodwin’s model of a molecular oscillator
using negative feedback (8–10). Understanding the different
types of behavior in networks have enabled mathematical biologists to make predictions about which biological processes affectcircadian rhythm behavior such as period length and temperature compensation. For example, in a hypothetical biochemical
network with negative feedback, there are necessary constraints
on reaction rates for the generation of instability at steady state(11). Using this constraint and other ideas from signal processing in the Goodwin model for circadian oscillation, it could beshown that transcription and translation rate are not importantfor setting period length, but instead a critical feature is thedegradation rate of the repressor (12). These studies highlight thefundamental contributions of systems modelers even without
knowledge of the molecular network underpinning circadianrhythms.
Identification of the molecular components of circadianrhythms led to an explosion of models incorporating these
proteins and functions. Goldbeter’s model used non-linearityof Hill-type equations in the Goodwin model when he reported
the first model of circadian rhythms based on observations of
PERIOD phosphorylation and degradation in Drosophila (13).
Non-linearity in feedback repression could occur throughcooperative binding of multiple repressors to a promoter or via
repressive multisite phosphorylation of a transcriptional activator. Derivations of this type of model have been used to examine
Drosophila (13–19), Neurospora (14, 16, 20, 21), and mammalian

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Periodicity and the Rise of the
Posttranslation Circadian Oscillator

Scientists originally thought that a transcription–translation
feedback network was required for 24-h rhythms. But then, aremarkable study was published. Working in cyanobacteria,

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Figure 1 | Systems approaches to studying circadian rhythms. On an organism level, researchers are using CRISPR/Cas9 and TALEN coupled with new
sleep staging techniques to uncover mutations in genes that increase or decrease sleep. On a tissue level, new tissue clearing techniques such as CLARITY and
CUBIC are enabling researchers to investigate the neuroanatomical basis of behavior (see Systems Neurophysiology). On a cell level, systems transcriptomics
experiments have revealed not only rhythmic mRNA levels through microarrays and RNA sequencing but also other molecular details such as chromatin state,mRNA structure and modification, ribosome binding, and rhythmic protein abundance (see Systems Transcriptomics, Systems Proteomics and Metabolomics, andSystems Approaches to Study Translation Regulation in Circadian Rhythms). On a molecular level, reconstitution of a cyanobacteria posttranslational oscillator andthe discovery of transcription/translation independent peroxiredoxin rhythms have expanded our understanding of circadian oscillations (see Periodicity and the Riseof the Posttranslation Circadian Oscillator). Systems modelers have discovered insights into constraints and parameters necessary for unique features of thecircadian clock such as entrainment, periodicity, robustness, and temperature compensation (see Modeling the Systems Properties of Circadian Rhythms).

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Kondo and colleagues mixed a small number of cyanobacterial
proteins KaiA, KaiB, and KaiC, and ATP in a test tube to produce
rhythmic 24-h oscillations in KaiC protein phosphorylation(56). In a manner similar to simple chemical reaction–diffusion
systems creating Turing patterns, 24-h periodicity could beestablished in the absence of a transcription–translation negative
feedback loop architecture.
A few years later, it was discovered that an antioxidant
enzyme called peroxiredoxin in cultured human red blood
cells undergoes temperature-independent circadian cycles
of hyperoxidation. Because red blood cells lack a nucleus andperoxiredoxin rhythms persist in the presence of transcription
and translation inhibitors, these rhythms prove the existence of a
non-transcriptional-based circadian oscillator in mammals (57)and was later found to be conserved in a wide range of species(58). In mice, rhythmic peroxiredoxin oxidation is thought tooccur through hemoglobin-dependent H2O2 generation andproteasome degradation (59), but it remains unclear how rhythmic oxygen delivery occurs in isolated cells and how the rhythmsof peroxiredoxin oxidation are temperature compensated. In
the future, a more detailed understanding of the relationshipbetween rhythmic peroxiredoxin oxidation and canonical circadian clocks is needed.
The reconstitution of a phosphorylation oscillator in cyanobacteria (56) prompted modelers and synthetic biologists toquestion what the minimal components are for a circadianoscillator. In cyanobacteria, biochemical studies have driven
our understanding of the mechanism of the oscillator. KaiC was
discovered to be both a kinase and a phosphatase (60–62). KaiC
autophosphorylation is triggered by allosteric activation by KaiA
(63, 64) and regulated through feedback inhibition by KaiB (60,65). Importantly, a sequential ordering of phosphorylation at twosites on KaiC is necessary for oscillation (66, 67) and remarkably,
when Kai protein complexes from different starting phases are
mixed, the phosphorylation state of the population remains insynchrony (68).
Several models have been proposed to explain the mechanismof oscillation (69–71) and synchrony of the cyanobacteria oscillator on a population level (67, 72, 73). A central idea is that
there is monomer shuffling between KaiC hexamers, which was
proposed in mathematical models (72, 74) and by experimentsfrom the Kondo laboratory (65, 68), and confirmed elsewhere by
FRET experiments (75). Other models do not explicitly rely on
monomer exchange for synchrony (67, 73), but rather synchrony
arises as an emergent property of the system based on KaiA’s
affinity for different phosphorylated forms of KaiC. Of course,
concepts such as differential affinity and monomer exchange havebeen incorporated together into more sophisticated models of
cyanobacteria rhythms (76, 77).
Studies in cyanobacteria provide a foundation to understand the requirements (ordered phosphorylation, synchrony,etc.) for a generic phosphorylation oscillator. Most models of
non-circadian phosphorylation oscillators require additional
mechanisms for rhythmicity such as protein synthesis anddegradation (78) or allosteric feedback from substrate (79, 80).
However, a theoretical study demonstrated that autonomous
circadian oscillations are possible with a single substrate

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reversibly phosphorylated at only two sites (81) and suggested
that a well-defined ordering of phosphorylation states andsequestering checkpoints for enzyme activity could be designprinciples for single-molecule oscillators for the circadian clock
and potentially other cellular oscillators. The Jolley model (81)results in a substrate with four possible modification states
similar to MAPK (82) and cyanobacteria models (67). While ageneral phosphorylation oscillator has not yet been built basedon these models, the reconstruction of temporal (56) and spatial(83) oscillators from purified components provide inspiration
for future work. Furthermore, the recently reported success intransplanting the circadian clock from cyanobacteria into thenon-circadian bacterium Escherichia coli (84) implies someamount generality for the network and design principles uponwhich circadian rhythms lie.

Temperature Compensation

Insensitivity to temperature was originally identified as an essential characteristic of biological time-measuring systems in bees,
flies, and marine organisms (85–88) and references therein. In
particular, it was postulated that temperature independence was
the result of a temperature compensation mechanism involving
the opposing effects of enzyme activities in response to changes intemperature (87). Researchers began to identify genetic mutants
with defects in temperature compensation in Neurospora (89,90) and Drosophila (91, 92). In flies, repressor dimerization was
thought to be involved in temperature compensation because
loss of the repressor’s dimerization domain caused the period tostrongly depend on temperature (91). Researchers incorporated
these ideas into models of circadian rhythms by suggesting that
nuclear import of the repressor decreases with temperature andrepressor dimerization increases with temperature (93, 94).
Other models emphasized the importance of degradation of therepressor (95, 96) and other parameters needed for temperaturecompensation (97). The conceptual point of these models is thatfor circadian rhythms to be temperature compensated, somebiochemical reactions accelerate circadian oscillations, whileother biochemical reactions decelerate circadian oscillations.
The balance model supposes that the former acceleration reactions are less sensitive to temperature, whereas the latter deceleration reactions are more sensitive to temperature. A molecularbasis for this type of temperature compensation was proposedin plants (98) and also formulated mathematically as a balance
equation (99) to explain how Neurospora repressor stability
decreases with an increase in temperature (95, 100), which isultimately caused by phosphorylation-dependent degradationfrom a kinase (101).
In 1968, Pittendrigh and colleagues argued against a balancing
model in which temperature shortens a reaction in the first half ofa circadian cycle while simultaneously lengthening a reaction in
the second half of the cycle in their experiments with Drosophila(102). They used short light pulses to shift the phase of Drosophila
pupae at different temperatures and showed that the period andwave form of the phase response curve changes only a little bitwith temperature. They proposed a model where circadian output
from a temperature-dependent oscillation is subjected to feedback inhibition from another temperature-dependent reaction

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Systems Contributions to Circadian Biology(102, 103). These early studies suggested a model in which the
enzymatic reactions that comprise the clock are temperaturecompensated. However, the idea of a temperature-compensatedenzyme is counterintuitive because most chemical processes aretemperature dependent. In cyanobacteria, the kinetic profile ofthe phosphorylation to dephosphorylation ratio is temperaturecompensated in vitro (56, 104). This was the first indication thattemperature compensation could occur through the enzymes
themselves as opposed to compensation that occurs throughcompeting biochemical reactions.
The canonical transcription–translation feedback loop
underlying circadian rhythms in eukaryotes may also be affectedby temperature-insensitive enzymatic reactions. In eukaryotes,
it was first discovered in mammals that the phosphorylationdependent degradation rate of the repressor is temperature
insensitive in cells, and temperature-insensitive phosphorylation is preserved in vitro (41). This suggests that temperatureinsensitive enzymatic reactions can influence the circadiantranscription–translation network. In addition to componentlevel temperature compensation (41), detailed examination of
the degradation of the repressor revealed three distinct stagesof degradation that depend on when during the circadian cycle
protein translation is arrested (105). The authors in this study
suggested that temperature-insensitive and -sensitive phosphorylation at different sites of the repressor are responsible fortemperature compensation. In the future, it will be particularlyinteresting to uncover the mechanisms and structural basis oftemperature compensation in these individual reactions and tosynthetically engineer temperature compensation in circadianclocks similar to synthetically temperature-compensated genetic
networks in bacteria (106).

activators rather than binding DNA is important for robust
circadian timekeeping (124). Experiments in mammals seemto support these models because rhythm generation in mouseembryonic fibroblasts can be abolished by constitutive expressionof the mammalian repressor (125) or by artificially altering the
stoichiometry between activators and repressors (126). Indeed,
the natural stoichiometry between activators and repressors in amouse liver is close to 1:1 as measured by western blotting (127)and mass spectrometry (128).
The difference in repression mechanisms—Hill-type nonlinearity from models based on the Goodwin oscillator orprotein-based sequestration leads to subtle differences in theactivity of the activator in circadian models as the concentration of the repressor increases. For Hill-type models, there is an
all-or-none switch that occurs when multisite phosphorylationor cooperative binding reaches some critical level. The activator
is like a light bulb that is on until it suddenly gets switched off.
For protein-sequestration models, the activity of the activator linearly decreases as a function of the molar ratio between
activator and repressor, which is like a light bulb slowly turned
down by a dimmer. These differences can affect the synchronizedperiod between coupled heterogeneous oscillators compared tothe mean period of uncoupled oscillators (129). Importantly,
understanding the differences in repression mechanisms forcoupled oscillators can lead to testable predictions on how clock
components interact with other proteins, such as regulation of
the tumor antigen p53 (130).

SYSTEMS TRANSCRIPTOMICS
Identification of the Components of the
Circadian Clock

Robustness to Gene Dosage

On a tissue level, the central clock in mammals is located ina structure of the brain called the SCN. Ganglion cells in theretina detect light signals through a photopigment called melanopsin and relay this information to the SCN. SCN neurons
project to different regions of the brain and synchronize biological clocks in peripheral tissues by secretion of hormones
as previously reviewed (131, 132). However, most tissues inan organism have the core transcriptional architecture forcircadian rhythmicity including liver, lung, and muscles (133)
as well as cultured cells (134–136).
The genetic network for circadian rhythms is based ondelayed feedback repression of transcription. Briefly, a
CLOCK:BMAL1 heterodimer activates transcription at promoter elements called E-boxes. A protein called PERIOD (PER)
heterodimerizes with another protein CRYPTOCHROME(CRY) and translocates to the nucleus where it represses
transcription of the Period gene and other genes that activate Period transcription, reviewed elsewhere extensively(137–140). Several components of the core transcriptional
network were identified in forward-genetics screens (i.e.,
random mutation of an organism’s genome and searching formutants with abnormal rhythms) including Period (7) and
Timeless (141) in Drosophila, Frequency (35) in Neurospora,and Clock (142, 143) in mice.

Circadian rhythms are surprisingly robust to changes in genedosage—there has been much discussion about why knockoutof core clock genes only results in subtle period lengtheningor shortening (107). There have been efforts to understandnetworks effects by systematically altering individual gene levels (108) or by globally altering transcription levels with drugs(109). Resistance to internal noise from the stochastic natureof biochemical networks in the cell is an essential property fora robust circadian clock network (110). Theoretical models
suggested that intercellular coupling between individual oscillator cells is necessary for synchrony and noise resistance (111).
Indeed, dissociated SCN neurons and isolated cells from tissues
such as lung and liver are arrhythmic compared to intact tissues with altered rhythmicity (112, 113). Robustness is also
ensured by interlocking-feedback loops at the genetic circuit
level, for review, see Ref. (114), and has been featured in modelsof circadian rhythms from different organisms (17, 115–118).
In mammals, genetic (119–122) and pharmacological (38, 44)
perturbation of the secondary feedback loop showed that it
primarily served as a stabilizing mechanism.
Modeling approaches have revealed that activator and repressor complex formation are necessary for noise resistance (123)and that a 1:1 stoichiometric balance of repressors binding

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Systems approaches have been successful in identifying othercore clock components such as Bmal, which was identified
using an iterative search for other bHLH proteins (144, 145).
Genomics-based strategies helped to identify activators of Bmalsuch as Rora (122) and Nr1d1 (121), and functional genomics
strategies in Drosophila revealed Clockwork Orange (146–148) asa homolog of the mammalian Dec1 and Dec2 (149).

circadian deadenylation of the histone methyltransferase mixedlineage leukemia 1 (179). Circadian regulation results in cycles of
NAD + biosynthesis (180), NAD + recycling (181), alters Clockand Bmal1 binding (182), and NAD redox rhythms have been
observed directly in cells (183). Together, these studies suggest
a direct link between metabolism and epigenetic regulation of
circadian rhythms.

Systems Experiments to Study theTranscriptome

MicroRNAs (miRNAs) in Circadian
Rhythms

Some of the earliest systems approaches to study circadian rhythms
were to simply analyze all the mRNA in a tissue or organism todetermine which mRNAs had cyclic expression. These studies
used microarrays to identify cycling mRNAs in Drosophila (150,151), in the mouse liver, heart, and SCN (152–155), rat pineal
gland (156), isolated fibroblasts (157, 158), and in plants (159).
There was considerable tissue specificity in rhythmic genes
because only approximately 10% of cycling genes were common
to at least one other tissue (160). Additionally, there are approximately 100-fold fewer cycling transcripts in NIH3T3 and U2OS
cell culture models compared to mice tissue (161). This study also
revealed 12-h oscillatory transcripts in liver, heart, lungs, andother tissues, but not in cultured cells (161). These “harmonic”
rhythms are perturbed by a disrupted circadian clock in the SCN(162). Rhythmicity of the core clock component PER2 in thesetissues could be confirmed with luminescent reporter mice (163).
Recent studies have begun to use RNA sequencing to measuresteady-state mRNA expression in tissues such as the mouse liver(164–166) or to identify transcription factor-binding sites using
chromatin-immunoprecipitation coupled with RNA-sequencing(CHIP-seq) (164–170). Comparative genomic approaches
revealed the importance of E-boxes, D-boxes (171), and RREs
(155, 171) in timing circadian mRNA expression, which haveallowed ensemble-based predictions of phase response fromcombinations of these elements (25).

In addition to discovering cycling transcripts, systems transcriptomics experiments have uncovered other cycling RNAs
such as long non-coding RNAs (lncRNAs) and miRNAs. For
example, CHIP-seq experiments revealed clock proteins such as
Clock, Bmal1, and Nr1d1 binding at sites outside of canonicalgene promoters (166, 167, 169, 170, 184, 185), which suggested
circadian regulation of non-protein-coding transcripts. MiRNAs
bind target mRNAs typically in 3′ untranslated regions (3′ UTRs)
to inhibit translation and destabilize the mRNA, for review see
Ref. (186–189). Microarray studies uncovered miRNA expression
inversely correlated with circadian activators Clock and Bmal1
and positively correlated with circadian suppressors Per, Cry1,and Nr1d1 (190), and other miRNAs that have diurnal expression
patterns (191). MiRNAs are regulated by circadian proteins suchas CLOCK (170, 192, 193) and NR1D1 (194) and modulate theexpression of circadian genes such as Bmal1 (195–198), Clock(193, 199, 200), the circadian polyA deadenylase Nocturnin (201),
Per1 and Per2 (202–204), Clockwork Orange (205), Timeless inDrosophila (206), and Cry1 (207). Knockout of the core miRNAprocessing machinery in mouse liver revealed that ~30% of therhythmic transcriptome is posttranscriptionally modulated bymiRNAs (208).

lncRNAs in Circadian Rhythms

In addition to miRNAs, next-generation sequencing experiments
have revealed extensive transcription of lncRNAs (209, 210) andcircadian expression of lncRNAs (166, 211, 212). An in depth
study revealed differential expression of 112 lncRNAs in the rat
pineal gland, and light expression at night could modulate thelevel of some of these lncRNAs (213). A study of mouse liver
revealed 19 out of 123 lncRNAs detected with robust oscillations
and detected antisense transcripts associated with Per2 (166).
Antisense transcription of Per2 in mice liver has been reported
by others (164, 165) and originally in the silk moth (214), but
it remains unclear what the function of antisense Per2 is forcircadian rhythms. In Neurospora, the antisense transcript of
frequency (called Qrf—Frq, spelled backward) is important for
entrainment to light, oscillates in a reciprocal pattern to Frq,
and promotes Frq gene silencing via heterochromatin formation(215–217). Deletion of a lncRNA associated with Prader–Willi
syndrome in mice results in increased energy expenditure andaltered expression of circadian genes such as Clock, Cry, andPer (218). Additionally, a lncRNA highly upregulated in liver
perturbs the expression levels of Clock, Cry, and Per in hepatoma
cells (219). Together, these studies suggest a role for non-proteincoding transcripts in the regulation of circadian rhythms.

Systems Experiments Analyzing
Chromatin State

Next-generation sequencing experiments revealed both circadian
initiation and recruitment of RNA polymerase II (RNAPII) to circadian promoters (164, 168) and concomitant circadian changes
in chromatin state (164, 166, 168). In particular, H3K4me3
histone methylation have circadian oscillations that slightly lag
RNAPII occupancy (168). Circadian regulation of chromatinstate was first observed in an increase in phosphorylation ofhistone H3S10 in the SCN in response to light (172). Additionally,rhythmic acetylation of histone 3 was observed in the promotersof Per1, Per2, and Cry1 in mouse liver (173, 174). CLOCK itself
has intrinsic histone acetylase activity (175) and is rhythmicallyrecruited to circadian promoters (174, 176). CLOCK can acetylate
other non-histone proteins including BMAL1, which promotes
recruitment of CRY1 and thus BMAL1–CLOCK inactivation(177). SIRT1, a sirtuin histone deacetylase whose activity depends
on the coenzyme nicotinamide adenine dinucleotide (NAD+),
interacts with CLOCK and can deacetylate BMAL1 (174) andPER2 (178). SIRT1 also controls H3K4me3 methylation through

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Posttranscriptional Regulation of
Circadian Rhythms

method to measure polyA tail length suggested that rhythmic
nuclear adenylation is coupled to rhythmic transcription and thatrhythmicity in polyA tail length is related to rhythmic protein
expression (246). These studies suggest that posttranscriptional
regulation by deadenylation may be important for proper circadian rhythms and that next-generation sequencing techniquessuch as polyA tail profiling (247, 248) will be critical for fully
understanding the contribution of polyA tail length to circadianrhythms.

Next-generation sequencing studies have also examined towhat extent rhythmic steady-state mRNA transcripts result
from de novo rhythmic transcription versus rhythms via posttranscriptional regulation. By analyzing expression of introns
as an indicator of pre-mRNA levels, a study by Koike et al.
determined that the majority circadian mRNAs do not undergo
rhythmic transcription (164). Another method to directly assess
de novo transcription called Nascent-seq confirmed this resultand further showed that many mRNAs with de novo rhythmictranscription do not have rhythms in steady levels of mRNA
(165). A similar nascent-seq study in Drosophila also revealed aconsiderable posttranscriptional contribution to cycling mRNA
amplitudes (220).
There are a variety of mechanisms for posttranscriptional
regulation of circadian rhythms including splicing, mRNA
export, polyadenylation, mRNA stability, methylation, and regulated translation—for review, see Ref. (221). The first indicationof posttranscriptional regulation of circadian rhythms was thatstability of Drosophila Per mRNA oscillates (222), which was also
later observed in mammals (223). Posttranscriptional regulatorssuch as LARK bind to the 3′ UTR of Per1 mRNA to enhance PER1
translation (224, 225). LARK also promotes alternative translation of the casein kinase homolog Doubletime in Drosophila(226). Researchers have uncovered other proteins that regulatetranslation of clock components. For example, the heterogenous
nuclear ribonucleoprotein Q (hnRNP Q) modulates translationof Nr1d1, Per1, Per3, Cry1, and the rate-limiting enzyme in
melatonin synthesis AANAT (227–232). Cry1 mRNA stability isalso regulated by AU-rich element RNA-binding protein (AUF1)
also known as hnRNP D (233, 234), and Per2 mRNA stability was
found to be modulated by polypyrimidine tract-binding protein
also known as hnRNP I (235).

Systems Experiments to Measure mRNA
Modification, Structure, and RNA-Binding
Proteins

Besides polyadenylation, mRNA processing by other mechanisms
may contribute to circadian rhythms. A recent study showed that
reduction of Mettl3, an m6A mRNA methylase involved in mRNA
processing and nuclear export, reduces m6A methylation of
circadian transcripts and extends period (249). Next-generation
sequencing studies of m6A methylation may reveal other contexts
in which methylation of mRNA is important for circadian rhythms(250). In addition, other RNA-sequencing techniques to probe
RNA secondary structure such as dimethyl sulfate sequencing—
DMS-seq and parallel analysis of RNA structure—PARS-seq(251, 252), BRIC-seq for mRNA stability (253, 254), and various
methods to analyze RNA-binding sites of specific RNA-binding
proteins such as CLIP, CLIP-seq, HITS-CLIP, iCLIP, and PARCLIP (255–260) will be critical for understanding how mRNA
processing is involved in circadian rhythms. For example, CLIPseq of mRNAs bound to cold-inducible binding protein, which is
required for high-amplitude circadian gene expression, revealedbinding to Clock and other circadian transcripts (261).

SYSTEMS PROTEOMICS ANDMETABOLOMICSmRNA PolyA Tail Length and Circadian
Rhythms

Circadian Proteomics

Researchers are beginning to use systems approaches to study
the circadian proteome and metabolome. Using two-dimensional
difference gel electrophoresis (2D-DIGE), Reddy and colleagues
revealed that approximately 20% of the soluble proteins in themouse liver oscillate. Surprisingly, for many rhythmic proteins,the corresponding mRNA was not rhythmic, which suggests
translational and posttranslational control of protein rhythms(262). 2D-DIGE has also been used to investigate circadian differences in the mouse retina (263) and day and night differencesin the mouse heart (264). In addition to mice, 2D gel-based mass
spectrometry has been used to investigate chronological changes
in eukaryotic algae (265, 266) and in plants (267, 268).
Other groups have employed stable isotope labeling by aminoacids in cell culture (SILAC) to compare two groups of samples—one mixed with “heavy” amino acids and one mixed with“light” amino acids based on the composition of different elementisotopes (269). SILAC-based quantitative mass spectrometry has
been used to uncover cycling proteins in the mouse liver (270,271) and SCN (272). Traditional SILAC approaches use chemical
synthesis of peptides with isotopically labeled amino acids (269,

Other mRNA processing mechanims may also posttranscriptionally regulate circadian rhythms. The 3′ end of newly transcribed
pre-mRNA in the nucleus is cleaved and a polyA tail is added
at one of the several possible sites (236). Deadenylation of thispolyA tail in the cytoplasm by enzymes such as the poly(A)specific ribonuclease and the Ccr4-Not complex can shorten
tail length and accelerate mRNA degradation (237, 238). Daily
variation in polyA tail length was first observed for vasopressinmRNA in the SCN (239). In Xenopus, another deadenylase called
Nocturnin was discovered in a screen to detect rhythmically
expressed mRNAs in retinal photoreceptors (240, 241) and waslater shown to be expressed in multiple mouse tissues (242).
Nocturnin is one of the few mRNAs that remain rhythmic after theliver clock is conditionally inactivated by drug-mediated Bmal1
expression (243) and can be posttranscriptionally regulated bymiR-122 (201). Mice lacking Nocturnin do not have any obvious
circadian behavior deficiencies, but are resistant to diet-induced
obesity (244). However, in Drosophila, loss of Nocturnin results inabnormal behavior rhythms in constant light (245). A microarray

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February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology273) or gene expression systems in E. coli (274, 275). However,cell-free protein synthesis systems are potentially a more costeffect tool to express isotope-labeled peptides because the volumeof the reaction is much lower and purification is easier because
there is no need for culturing, harvesting, and disrupting cells(275–278). Recently, a cell-free protein synthesis system called
the PURE system (279) coupled with high-resolution mass spectrometry in a workflow called MS-QBiC was used to quantify 20
selected circadian clock proteins over a 24-h time series (128).
This study estimated the absolute number of protein molecules
for core clock components per cell and the delay between steadystate levels of mRNA (measured by qPCR) and protein copy
number (128).
In addition to SILAC, label-free approaches such as MaxLFQ(280) have been used to quantify proteins in mouse skeletal muscle (281). Mass spectrometry has been used to examine the global
proteome in cyanobacteria (282). Mass spectrometry has also
been used to analyze the global phosphoproteome and revealed~5,000 phosphosites that significantly oscillate in the mouse
liver (283) and ~3,000 phosphosites in Arabidopsis (284). Given
the widespread discrepancies between transcript and proteinrhythmicity in a number of organisms, in the future, it will be
useful to understand the role of translation and posttranslational
regulation as well as cycling protein modification states (e.g.,
phosphorylation) to circadian networks.

efficacy and that many of the most successful drugs in the UnitedStates target proteins with circadian rhythm components (212),
ongoing clinical trials rarely exploit time-of-day-dependent drugdelivery (304).

SYSTEMS APPROACHES TO STUDY
TRANSLATION REGULATION IN
CIRCADIAN RHYTHMS
Although 10% of genes are rhythmic in the liver (152), de novotranscription is only responsible for a small fraction of this rhythmicity (164). Thus, gene expression studies using microarrays
and RNA-sequencing may not correlate with translation of thecorresponding mRNA nor with protein abundance (305). In themouse liver, systems studies of the proteome are unable to detect
low-abundant components of the core circadian circuit (270,271), unless special care is taken to examine a particular protein
on a case-by-case basis (128). Thus, researchers have begun touse next-generation sequencing techniques of mRNA attached tomRNA in monosomes and polysomes (306, 307) and with affinity
purification (308–310) as a proxy for protein abundance and tounderstand how translation regulation affects protein abundance.
It has been known for more than 50 years that perturbation oftranslation disrupts circadian rhythms (311). Until recently, there
has been a shortage of good tools to measure translation directly.
In 2009, Nicholas Ingolia in Jonathan Weissman’s lab developed
a technique called ribosomal profiling, which uses RNA sequencing of ribosome-bound mRNA protected from RNAse degradation, to determine the location and abundance of ribosomes inthe yeast transcriptome (312). Researchers have begun to use thismethod to study circadian rhythms in ribosomal occupancy (313,314). These studies discovered a class of rhthmically translated
mRNAs without corresponding steady-state mRNA rhythms(313, 314), which in the case of mouse liver may be a result ofrhythmic ribosomal biogenesis (315). Researchers have previously observed that global translation is rhythmic in the mouse
liver (316, 317), which is probably a result of activation of theTORC1 pathway (315, 318–320). Interestingly, diurnally regulated translation in the mouse liver is only moderately affectedby knockout of the core clock component Bmal1 and many genesthat contained 5′-terminal oligo pyrimidine tract or translation
initiator of short 5′ untranslated region (5′-UTR) sequence haverhythms in ribosomal occupancy independent of trancriptional
rhythms (321). These studies in addition to previous research(322–326) suggest that feeding rhythms can synchronize the liverin the absence of cues from neuronal pacemaker cells in the SCN.
The Janich and Jang studies (313, 314) also revealed widespread circadian translation of upstream open reading frames(uORFs) in 5′ UTRs. Translation of uORFs globally represses
translation efficiency—a measure of the ratio of ribosomal occupancy, determined by ribosomal profiling, to steady-state mRNA,
measured by RNA-sequencing (314). Interesting, many circadianmRNAs also have uORFs in their 5′ UTRs (Table 1), which
may disrupt translation of the downstream coding sequenceby ribosomal pausing on the mRNA, alternative translation, orother mechanisms (327). Ribosome pausing on uORFs may be

Circadian Metabolomics

Researchers have looked at rhythmic metabolites in humans(285–288) and in mice (289–292) and have shown that circadianproteins directly regulate metabolism (44, 184). Researchers havealso used comprehensive metabolite profiling to analyze dieteffects in mice (293–295) and the effects of sleep loss in humans(296–299). Computational databases have been developed tocompare published transcriptomes, proteomes, and metabolomes(292). Metabolic profiling is still quite noisy compared to transcriptome data at least for identifying tissue-specific signatures(300), and many challenges remain including identification of
unknown metabolites, standardization of data repositories andreporting methods, and integration with other types of data.
Researchers are beginning to use metabolic profiling over larger
time courses and with higher resolution in cell culture lines(301). In the future, coupling these methods with gene knockout
or knockdown of core clock components will enable researchers
to identify connections between circadian rhythms and metabolism. For example, are there harmonics in metabolite rhythms(i.e., multiples of a 24-h rhythm like 8- and 12-h rhythms) similar
to the harmonics of mRNA rhythms (161, 162), and would theserhythms be influenced by circadian genes?
One benefit of systems studies is the development of a molecular timetable to detect an individual’s body time based on a single
time point assay. Molecular timetables have been developed withmice transcriptome data (212, 302) and applied to mice (291) andhuman (288) metabolite data, proteomic data (128), and evenhuman breath (303). In theory, metabolite timetables could enable researchers to hone chronotherapeutic strategies for clinicalconditions. However, despite the strong evidence that circadiantiming effects xenobiotic metabolism, bioavailability, and drug

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Systems Contributions to Circadian Biology

usage affects circadian function in cyanobacteria (336), Neurospora(337), and Drosophila (338). While cyanobacteria with codonoptimized Kai genes have enhanced circadian rhythmicty atcooler temperatures, this modification impairs cell growth, whichsuggests that non-optimal translation could provide an adaptive
response to changes in the environment (336). In Neurospora,codon optimization of Frq alters FRQ protein structure, which
impairs circadian rhythms (337). Similarly, in Drosophila, codon
optimization results in conformational changes of the Drosophila
PER protein altering PER phosphorylation, stability, and impairs
behavioral rhythms (338). Additionally, it is becoming clear thattranslation control is interlinked with both circadian rhythms
and sleep disorders. For example, Ataxin2 functions as a criticaltranslation activator of Per2 in flies (339, 340), and individuals
with disease mutations in human Ataxin2 have disturbed rapid
eye movement sleep (341, 342).

alleviated by the action of the non-canonical initiation factors
density regulated protein (DENR) and multiple copies in T-cell
lymphoma (MCT-1), which act to promote translation reinitiation downstream of uORFs (328, 329). Depletion of DENR byshRNAs in NIH3T3 cells shortens the period by 1.5 h, whichsuggests that uORFs may be relevant for circadian function(314). In other biological contexts, repression of translation by
uORFs can be regulated by trans-acting factors. For example, inDrosphila, the master switch gene Sex-lethal (Sxl) is importantfor sex, for review see Ref. (330–332). SXL-binding downstreamof a short uORF on male-specific lethal (msl)-2 enhances translation repression by the uORF on downstream reading frametranslation (333). During mitosis, one of the most translationally
repressed mRNAs is early mitotic inhibitor 1 (Emi1) that inhibits
the activity of the anaphase-promoting complex (334). Emi1 has
multiple transcript isoforms and the isoform with several uORFs
in the 5′ UTR is severely crippled for translation initiation insingle-molecule reporter experiments (335). These studies suggest that uORF-mediated translational repression is important ina variety of biological functions and may have an unexplored rolein circadian rhythms.
What is the consequence of disrupted translation for circadianrhythms? One clue came when researchers showed that codon

SYSTEMS NEUROPHYSIOLOGY
Systems neurophysiologists are beginning to connect the circadian circuit to more complex outputs from the clock such as activity rhythms. Forward genetics in mice have already uncovered
core components in the circadian network (142), and researchers
have begun to use forward genetics for complex behavior such as
sleep (343). On the other hand, the development of TALEN (344),
Zinc-Finger Nucleases (345), and CRISPR/Cas9 (346, 347) geneknockout systems have accelerated the pace at which researchers
can pursue reverse genetics in mice. In particular, CRISPR/Cas9
systems have been extensively modified to improve targeting efficiency and specificity (346–353). However, the need for invasive
techniques such as electroencephalography and electromyography to characterize sleep hampers high-throughput phenotyping.
To facilitate rapid phenotyping, researchers have developed a
respiration-based, sleep staging system in combination withredundant CRISPR targeting to reveal new genes important forsleep regulation (354, 355). In particular, researchers generated
and analyzed more than 21 different KO mice and discovered different ion channels that could increase or decrease sleep duration(355). These studies have revealed the genetic bases for behaviors
such as sleep, but do not show how neural networks and structures in the brain are wired to carry out such behavior. In the past,
researchers have used conventional histology and immunohistochemistry of sliced brain sections to reveal the when and where ofgene function, but recent advances in tissue clearing have begun
to enable direct imaging of intact organs (356).
Optical sectioning using light-sheet microscopy in combination with recently developed tissue-clearing techniques is apotent strategy to begin to explore the neuroanatomical basis ofbehavior (357–362). Image analysis algorithms, automated comparative analysis, and feature extraction will enable researchers
to quickly test and analyze neural activity in different parts of thebrain with different mutant mice and under a variety of experimental conditions. These approaches will be useful to determine
what areas of the brain are affected by sleep/wake pharmaceuticalreagents such as methamphetamine and to develop a whole-brain
anatomical atlas to catalog and characterize every individual cellin the brain.

Table 1 | Number of upstream open reading frames (uORFs) in common
circadian clock genes.
Gene name

Ref Seq ID

Number of uORFs

uORF length (nt)Bhlhe40

NM_011498

1

18

Bmal1

NM_007489
NM_001243048

42

72; 42; 21; 33
201; 171

Clock

NM_007715
NM_001289826

3
4

66; 48; 30
339; 66; 48; 30

Cry1

NM_007771

2

36; 24

Cry2

NM_009963

0

—

CK1d

NM_139059

2

27; 21

CK1e

NM_013767
NM_001289898
NM_001289899

0
0
2

—
—
126; 66

Dbp

NM_016974

2

12; 42

Nfil3

NM_017373

3

15; 51; 12

Nr1d1

NM_145434

3117; 192; 21

Nr1d2

NM_011584

3

120; 120; 117

Per1

NM_011065
NM_001159367

1
1

15
15

Per2

NM_011066

1

6

Per3

NM_011067
NM_001289877
NM_001289878

4
4
4

63; 30; 84; 48
63; 30; 84; 48
63; 30; 84; 48

Rorc

NM_011281

0

—

Tef

NM_017376
NM_153484

1
0

291
—

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9February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

CONCLUSION

phenotypes to particular features and neurons in the brain andother tissues. Developments in computational processing power,
data storage, and deep learning approaches will aid researchersin handling and analyzing the overwhelming amount of data
generated by systems studies. Nevertheless, it will be importantto validate findings with molecular techniques, case studies, andsynthetic biology approaches to reconstitute behavior. Finally,
can we translate this knowledge base to relevance in the clinic?
It will be important to develop new assays and algorithms forbody time estimation from samples at one or two time points.
A combination of transcriptome, metabolome, and proteome
timetables may further reduce the need for additional samples
and increase accuracy of body time estimation. Integration ofchronotherapeutics to clinical trial design and dosing protocols
may enhance the success of drug candidates and perhaps lead to areevaluation of the timing of drug delivery to achieve the greatest
benefit to patients.

Systems experiments from modeling to metabolomics havesignificantly increased our understanding of circadian rhythms,
but many challenges remain. For modeling, we still do not have acomprehensive understanding of temperature compensation nor
the role individual enzymes have in temperature-independent and-dependent reactions. We do not understand the contribution oftemperature-compensating reactions at the molecular, cell, tissueor organism level and how these temperature-compensating systems interface with one another. At an enzymatic level, we could
learn much by designing and building de novo temperaturecompensated reactions or by converting temperature-sensitive
enzymes into temperature-compensating ones. There is also aneed for understanding how recently discovered posttranslational
oscillators such as the peroxiredoxin system interface with thegenetic circadian circuit, and for an evolutionary investigation
into how and why these distinct circadian timekeeping systems
arose. Modeling is needed to make connections between different
timekeeping systems, different organization levels of timekeeping from molecule to tissue, and between circadian rhythms andother rhythms such as the cell cycle.
For systems “–omics” researchers, there is a large variationin the rhythmicity of transcripts, metabolites, and proteomes
detected even with similarly defined experimental systems. Thismay be in part due to how different algorithms detect rhythmicity (153, 363, 364), differences in sampling intervals (every 2, 3,
4, or 6 h), sampling duration, environmental conditions, andbiological variability (365). As surveys of the circadian proteome increase, there has been an increasing realization of thewidespread gap between transcript rhythms and protein levels.
Posttranscriptional and posttranslational studies that examinemRNA structure and processing, translation, and protein modification will enhance our understanding of how transcriptional
rhythms become protein rhythms, and how rhythms could evolve
without genetic underpinnings.
For systems neurophysiologists, there is a pressing need todevelop fast and reproducible assays that connect behavioral

AUTHOR CONTRIBUTIONS
Discussed and conceived of the direction of the review: AM and
HU. Illustrated the table and figure and wrote the paper: AM.

ACKNOWLEDGMENTS
We thank all lab members at The University of Tokyo and
RIKEN QBiC, in particular, Koji Ode for critical reading
and advice. This work was supported by a RIKEN Foreign
Postdoctoral Fellowship (AM).

FUNDING
This work was supported by a grant from AMED-CREST (HU),
Brain/MINDS (HU), Basic Science and Platform Technology
Program for Innovative Biological Medicine (HU), a KAKENHI
Grant-in-Aid from JSPS (Scientific Research S, 25221004, HU),
an intramural Grant-in-Aid from RIKEN QBiC (HU), and a
grant from the Takeda Science Foundation (HU).

REFERENCES

9. Goodwin BC. Oscillatory behavior in enzymatic control processes. Adv
Enzyme Regul (1965) 3:425–38. doi:10.1016/0065-2571(65)90067-1
10. Woller A, Gonze D, Erneux T. The Goodwin model revisited: Hopf bifurcation, limit-cycle, and periodic entrainment. Phys Biol (2014) 11:045002.
doi:10.1088/1478-3975/11/4/045002
11. Thron CD. The secant condition for instability in biochemical feedback
control – I. The role of cooperativity and saturability. Bull Math Biol (1991)
53:383–401. doi:10.1016/S0092-8240(05)80394-5
12. Forger DB. Signal processing in cellular clocks. Proc Natl Acad Sci U S A(2011) 108:4281–5. doi:10.1073/pnas.1004720108
13. Goldbeter A. A model for circadian oscillations in the Drosophila
period protein (PER). Proc Biol Sci (1995) 261:319–24. doi:10.1098/rspb.
1995.0153
14. Gonze D, Leloup JC, Goldbeter A. Theoretical models for circadianrhythms in Neurospora and Drosophila. C R Acad Sci III (2000) 323:57–67.
doi:10.1016/S0764-4469(00)00111-6
15. Leloup JC, Goldbeter A. A model for circadian rhythms in Drosophila
incorporating the formation of a complex between the PER and TIM
proteins. J Biol Rhythms (1998) 13:70–87. doi:10.1177/074873098128
999934

1. Drack M, Apfalter W, Pouvreau D. On the making of a system theory oflife: Paul A Weiss and Ludwig von Bertalanffy’s conceptual connection. Q
Rev Biol (2007) 82:349–73. doi:10.1086/522810
2. van der Pol B. LXXXVIII. On “relaxation-oscillations”. Philos Mag (1926)
2:978–92. doi:10.1080/14786442608564127
3. Fitzhugh R. Impulses and physiological states in theoretical models of nerve
membrane. Biophys J (1961) 1:445–66. doi:10.1016/S0006-3495(61)86902-6
4. Nagumo J, Arimoto S, Yoshizawa S. An active pulse transmission line
simulating nerve axon. Proc IEEE Inst Electr Electron Eng (1962) 50:2061–70.
doi:10.1109/JRPROC.1962.288235
5. Le Novère N. The long journey to a Systems Biology of neuronal function.
BMC Syst Biol (2007) 1:28. doi:10.1186/1752-0509-1-28
6. Turing AM. The chemical basis of morphogenesis. Philos Trans R Soc Lond
B Biol Sci (1952) 237:37–72. doi:10.1098/rstb.1952.0012
7. Konopka RJ, Benzer S. Clock mutants of Drosophila melanogaster. Proc Natl
Acad Sci U S A (1971) 68:2112–6. doi:10.1073/pnas.68.9.2112
8. Gonze D, Abou-Jaoudé W. The Goodwin model: behind the Hill function.
PLoS One (2013) 8:e69573. doi:10.1371/journal.pone.0069573

Frontiers in Neurology | www.frontiersin.org

10

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

16. Leloup JC, Gonze D, Goldbeter A. Limit cycle models for circadian rhythms
based on transcriptional regulation in Drosophila and Neurospora. J Biol
Rhythms (1999) 14:433–48. doi:10.1177/074873099129000948
17. Ruoff P, Christensen MK, Sharma VK. PER/TIM-mediated amplification,
gene dosage effects and temperature compensation in an interlocking-feedback loop model of the Drosophila circadian clock. J Theor Biol (2005)
237:41–57. doi:10.1016/j.jtbi.2005.03.030
18. Ueda HR, Hagiwara M, Kitano H. Robust oscillations within the interlocked feedback model of Drosophila circadian rhythm. J Theor Biol (2001)
210:401–6. doi:10.1006/jtbi.2000.2226
19. Wang J, Zhou T. A computational model clarifies the roles of positive andnegative feedback loops in the Drosophila circadian clock. Phys Lett A (2010)
374:2743–9. doi:10.1016/j.physleta.2010.04.059
20. Ruoff P, Vinsjevik M, Monnerjahn C, Rensing L. The Goodwin model:
simulating the effect of light pulses on the circadian sporulation rhythm of
Neurospora crassa. J Theor Biol (2001) 209:29–42. doi:10.1006/jtbi.2000.2239
21. Tseng Y-Y, Hunt SM, Heintzen C, Crosthwaite SK, Schwartz J-M.
Comprehensive modelling of the Neurospora circadian clock and its temperature compensation. PLoS Comput Biol (2012) 8:e1002437. doi:10.1371/
journal.pcbi.1002437
22. Becker-Weimann S, Wolf J, Herzel H, Kramer A. Modeling feedback
loops of the Mammalian circadian oscillator. Biophys J (2004) 87:3023–34.
doi:10.1529/biophysj.104.040824
23. Forger DB, Peskin CS. A detailed predictive model of the mammalian
circadian clock. Proc Natl Acad Sci U S A (2003) 100:14806–11. doi:10.1073/
pnas.2036281100
24. Hirota T, Lee JW, St John PC, Sawa M, Iwaisako K, Noguchi T, et al.
Identification of small molecule activators of cryptochrome. Science (2012)
337:1094–7. doi:10.1126/science.1223710
25. Jolley CC, Ukai-Tadenuma M, Perrin D, Ueda HR. A mammalian circadianclock model incorporating daytime expression elements. Biophys J (2014)
107:1462–73. doi:10.1016/j.bpj.2014.07.022
26. Leloup J-C, Goldbeter A. Toward a detailed computational model for the
mammalian circadian clock. Proc Natl Acad Sci U S A (2003) 100:7051–6.
doi:10.1073/pnas.1132112100
27. Leloup J-C, Goldbeter A. Modelling the dual role of Per phosphorylationand its effect on the period and phase of the mammalian circadian clock.
IET Syst Biol (2011) 5:44. doi:10.1049/iet-syb.2009.0068
28. Mirsky HP, Liu AC, Welsh DK, Kay SA, Doyle FJ. A model of the cell-autonomous mammalian circadian clock. Proc Natl Acad Sci U S A (2009)
106:11107–12. doi:10.1073/pnas.0904837106
29. Relógio A, Westermark PO, Wallach T, Schellenberg K, Kramer A, Herzel H.
Tuning the mammalian circadian clock: robust synergy of two loops. PLoS
Comput Biol (2011) 7:e1002309. doi:10.1371/journal.pcbi.1002309
30. Yan J, Shi G, Zhang Z, Wu X, Liu Z, Xing L, et al. An intensity ratio ofinterlocking loops determines circadian period length. Nucleic Acids Res(2014) 42:10278–87. doi:10.1093/nar/gku701
31. Dunlap JC. Molecular bases for circadian clocks. Cell (1999) 96:271–90.
doi:10.1016/S0092-8674(00)80566-8
32. Roenneberg T, Daan S, Merrow M. The art of entrainment. J Biol Rhythms(2003) 18:183–94. doi:10.1177/0748730403018003001
33. Froy O. The relationship between nutrition and circadian rhythms
in mammals. Front Neuroendocrinol (2007) 28:61–71. doi:10.1016/j.
yfrne.2007.03.001
34. Panda S. Circadian physiology of metabolism. Science (2016) 354:1008–15.
doi:10.1126/science.aah4967
35. Feldman JF, Hoyle MN. Isolation of circadian clock mutants of Neurospora
crassa. Genetics (1973) 75:605–13.
36. Ralph MR, Menaker M. A mutation of the circadian system in golden
hamsters. Science (1988) 241:1225–7. doi:10.1126/science.3413487
37. Chen Z, Yoo S-H, Park Y-S, Kim K-H, Wei S, Buhr E, et al. Identificationof diverse modulators of central and peripheral circadian clocks byhigh-throughput chemical screening. Proc Natl Acad Sci U S A (2012)
109:101–6. doi:10.1073/pnas.1118034108
38. He B, Nohara K, Park N, Park Y-S, Guillory B, Zhao Z, et al. The small
molecule nobiletin targets the molecular oscillator to enhance circadianrhythms and protect against metabolic syndrome. Cell Metab (2016)
23:610–21. doi:10.1016/j.cmet.2016.03.007

Frontiers in Neurology | www.frontiersin.org

39. Hirota T, Lewis WG, Liu AC, Lee JW, Schultz PG, Kay SA. A chemical
biology approach reveals period shortening of the mammalian circadianclock by specific inhibition of GSK-3beta. Proc Natl Acad Sci U S A (2008)
105:20746–51. doi:10.1073/pnas.0811410106
40. Hirota T, Lee JW, Lewis WG, Zhang EE, Breton G, Liu X, et al. Highthroughput chemical screen identifies a novel potent modulator of cellularcircadian rhythms and reveals CKIα as a clock regulatory kinase. PLoS Biol(2010) 8:e1000559. doi:10.1371/journal.pbio.1000559
41. Isojima Y, Nakajima M, Ukai H, Fujishima H, Yamada RG, Masumoto
K, et al. CKIε/δ-dependent phosphorylation is a temperature-insensitive,
period-determining process in the mammalian circadian clock. Proc Natl
Acad Sci U S A (2009) 106(37):15744–9. doi:10.1073/pnas.0908733106
42. Lee JW, Hirota T, Peters EC, Garcia M, Gonzalez R, Cho CY, et al. A small
molecule modulates circadian rhythms through phosphorylation of theperiod protein. Angew Chem Int Ed Engl (2011) 50:10608–11. doi:10.1002/
anie.201103915
43. Meng Q-J, Maywood ES, Bechtold DA, Lu W-Q, Li J, Gibbs JE, et al.
Entrainment of disrupted circadian behavior through inhibition of casein
kinase 1 (CK1) enzymes. Proc Natl Acad Sci U S A (2010) 107:15240–5.
doi:10.1073/pnas.1005101107
44. Solt LA, Wang Y, Banerjee S, Hughes T, Kojetin DJ, Lundasen T, et al.
Regulation of circadian behaviour and metabolism by synthetic REV-ERB
agonists. Nature (2012) 485:62–8. doi:10.1038/nature11030
45. Patton AP, Chesham JE, Hastings MH. Combined pharmacological andgenetic manipulations unlock unprecedented temporal elasticity andreveal phase-specific modulation of the molecular circadian clock of themouse suprachiasmatic nucleus. J Neurosci (2016) 36:9326–41. doi:10.1523/
JNEUROSCI.0958-16.2016
46. Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional
regulators. Nature (2000) 403:335–8. doi:10.1038/35002125
47. Gardner TS, Cantor CR, Collins JJ. Construction of a genetic toggle switch
in Escherichia coli. Nature (2000) 403:339–42. doi:10.1038/35002131
48. Tigges M, Dénervaud N, Greber D, Stelling J, Fussenegger M. A synthetic
low-frequency mammalian oscillator. Nucleic Acids Res (2010) 38:2702–11.
doi:10.1093/nar/gkq121
49. Kumaki Y, Ukai-Tadenuma M, Uno KD, Nishio J, Masumoto K, Nagano
M, et al. Analysis and synthesis of high-amplitude Cis-elements in the
mammalian circadian clock. Proc Natl Acad Sci U S A (2008) 105:14946–51.
doi:10.1073/pnas.0802636105
50. Ukai-Tadenuma M, Kasukawa T, Ueda HR. Proof-by-synthesis of thetranscriptional logic of mammalian circadian clocks. Nat Cell Biol (2008)
10:1154–63. doi:10.1038/ncb1775
51. Ukai-Tadenuma M, Yamada RG, Xu H, Ripperger JA, Liu AC, Ueda HR.
Delay in feedback repression by cryptochrome 1 is required for circadianclock function. Cell (2011) 144:268–81. doi:10.1016/j.cell.2010.12.019
52. Ukai H, Kobayashi TJ, Nagano M, Masumoto K, Sujino M, Kondo T, et al.
Melanopsin-dependent photo-perturbation reveals desynchronization
underlying the singularity of mammalian circadian clocks. Nat Cell Biol
(2007) 9:1327–34. doi:10.1038/ncb1653
53. D’Alessandro M, Beesley S, Kim JK, Chen R, Abich E, Cheng W, et al.
A tunable artificial circadian clock in clock-defective mice. Nat Commun(2015) 6:8587. doi:10.1038/ncomms9587
54. Edwards MD, Brancaccio M, Chesham JE, Maywood ES, Hastings MH.
Rhythmic expression of cryptochrome induces the circadian clock ofarrhythmic suprachiasmatic nuclei through arginine vasopressin signaling.
Proc Natl Acad Sci U S A (2016) 113:2732–7. doi:10.1073/pnas.1519044113
55. Smyllie NJ, Chesham JE, Hamnett R, Maywood ES, Hastings MH.
Temporally chimeric mice reveal flexibility of circadian period-setting inthe suprachiasmatic nucleus. Proc Natl Acad Sci U S A (2016) 113:3657–62.
doi:10.1073/pnas.1511351113
56. Nakajima M, Imai K, Ito H, Nishiwaki T, Murayama Y, Iwasaki H, et al.
Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro. Science (2005) 308:414–5. doi:10.1126/science.1108451
57. O’Neill JS, Reddy AB. Circadian clocks in human red blood cells. Nature(2011) 469:498–503. doi:10.1038/nature09702
58. Edgar RS, Green EW, Zhao Y, van Ooijen G, Olmedo M, Qin X, et al.
Peroxiredoxins are conserved markers of circadian rhythms. Nature (2012)
485:459–64. doi:10.1038/nature11088

11February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

59. Cho C-S, Yoon HJ, Kim JY, Woo HA, Rhee SG. Circadian rhythm of
hyperoxidized peroxiredoxin II is determined by hemoglobin autoxidationand the 20S proteasome in red blood cells. Proc Natl Acad Sci U S A (2014)
111:12043–8. doi:10.1073/pnas.1401100111
60. Kitayama Y, Iwasaki H, Nishiwaki T, Kondo T. KaiB functions as an attenuator of KaiC phosphorylation in the cyanobacterial circadian clock system.
EMBO J (2003) 22:2127–34. doi:10.1093/emboj/cdg212
61. Nishiwaki T, Iwasaki H, Ishiura M, Kondo T. Nucleotide binding andautophosphorylation of the clock protein KaiC as a circadian timing processof cyanobacteria. Proc Natl Acad Sci U S A (2000) 97:495–9. doi:10.1073/
pnas.97.1.495
62. Xu Y, Mori T, Johnson CH. Cyanobacterial circadian clockwork: roles of
KaiA, KaiB and the KaiBC promoter in regulating KaiC. EMBO J (2003)
22:2117–26. doi:10.1093/emboj/cdg168
63. Nishiwaki T, Satomi Y, Nakajima M, Lee C, Kiyohara R, Kageyama H, et al.
Role of KaiC phosphorylation in the circadian clock system of Synechococcus
elongatus PCC 7942. Proc Natl Acad Sci U S A (2004) 101:13927–32.
doi:10.1073/pnas.0403906101
64. Xu Y, Mori T, Pattanayek R, Pattanayek S, Egli M, Johnson CH. Identificationof key phosphorylation sites in the circadian clock protein KaiC by crystallographic and mutagenetic analyses. Proc Natl Acad Sci U S A (2004)
101:13933–8. doi:10.1073/pnas.0404768101
65. Kageyama H, Nishiwaki T, Nakajima M, Iwasaki H, Oyama T, Kondo
T. Cyanobacterial circadian pacemaker: Kai protein complex dynamics
in the KaiC phosphorylation cycle in vitro. Mol Cell (2006) 23:161–71.
doi:10.1016/j.molcel.2006.05.039
66. Nishiwaki T, Satomi Y, Kitayama Y, Terauchi K, Kiyohara R, Takao T,
et al. A sequential program of dual phosphorylation of KaiC as a basis forcircadian rhythm in cyanobacteria. EMBO J (2007) 26:4029–37. doi:10.1038/
sj.emboj.7601832
67. Rust MJ, Markson JS, Lane WS, Fisher DS, O’Shea EK. Ordered phosphorylation governs oscillation of a three-protein circadian clock. Science (2007)
318:809–12. doi:10.1126/science.1148596
68. Ito H, Kageyama H, Mutsuda M, Nakajima M, Oyama T, Kondo T.
Autonomous synchronization of the circadian KaiC phosphorylation
rhythm. Nat Struct Mol Biol (2007) 14:1084–8. doi:10.1038/nsmb1312
69. Emberly E, Wingreen NS. Hourglass model for a protein-based circadian oscillator. Phys Rev Lett (2006) 96:038303. doi:10.1103/PhysRevLett.96.038303
70. Mehra A, Hong CI, Shi M, Loros JJ, Dunlap JC, Ruoff P. Circadian rhythmicity by autocatalysis. PLoS Comput Biol (2006) 2:e96. doi:10.1371/journal.
pcbi.0020096
71. Takigawa-Imamura H, Mochizuki A. Predicting regulation of the phosphorylation cycle of KaiC clock protein using mathematical analysis. J Biol
Rhythms (2006) 21:405–16. doi:10.1177/0748730406291329
72. Clodong S, Dühring U, Kronk L, Wilde A, Axmann I, Herzel H, et al.
Functioning and robustness of a bacterial circadian clock. Mol Syst Biol
(2007) 3:90. doi:10.1038/msb4100128
73. van Zon JS, Lubensky DK, Altena PRH, ten Wolde PR. An allosteric model ofcircadian KaiC phosphorylation. Proc Natl Acad Sci U S A (2007) 104:7420–5.
doi:10.1073/pnas.0608665104
74. Yoda M, Eguchi K, Terada TP, Sasai M. Monomer-shuffling and allosterictransition in KaiC circadian oscillation. PLoS One (2007) 2:e408. doi:10.1371/
journal.pone.0000408
75. Mori T, Williams DR, Byrne MO, Qin X, Egli M, Mchaourab HS, et al.
Elucidating the ticking of an in vitro circadian clockwork. PLoS Biol (2007)
5:e93. doi:10.1371/journal.pbio.0050093
76. Eguchi K, Yoda M, Terada TP, Sasai M. Mechanism of robust circadianoscillation of KaiC phosphorylation in vitro. Biophys J (2008) 95:1773–84.
doi:10.1529/biophysj.107.127555
77. Nagai T, Terada TP, Sasai M. Synchronization of circadian oscillationof phosphorylation level of KaiC in vitro. Biophys J (2010) 98:2469–77.
doi:10.1016/j.bpj.2010.02.036
78. Liu P, Kevrekidis IG, Shvartsman SY. Substrate-dependent control of ERK
phosphorylation can lead to oscillations. Biophys J (2011) 101:2572–81.
doi:10.1016/j.bpj.2011.10.025
79. Chickarmane V, Kholodenko BN, Sauro HM. Oscillatory dynamics arisingfrom competitive inhibition and multisite phosphorylation. J Theor Biol
(2007) 244:68–76. doi:10.1016/j.jtbi.2006.05.013

Frontiers in Neurology | www.frontiersin.org

80. Shankaran H, Ippolito DL, Chrisler WB, Resat H, Bollinger N, Opresko LK,
et al. Rapid and sustained nuclear-cytoplasmic ERK oscillations induced by
epidermal growth factor. Mol Syst Biol (2009) 5:332. doi:10.1038/msb.2009.90
81. Jolley CC, Ode KL, Ueda HR. A design principle for a posttranslational biochemical oscillator. Cell Rep (2012) 2:938–50. doi:10.1016/j.
celrep.2012.09.006
82. Markevich NI, Hoek JB, Kholodenko BN. Signaling switches and bistability
arising from multisite phosphorylation in protein kinase cascades. J Cell Biol
(2004) 164:353–9. doi:10.1083/jcb.200308060
83. Loose M, Fischer-Friedrich E, Ries J, Kruse K, Schwille P. Spatial regulatorsfor bacterial cell division self-organize into surface waves in vitro. Science(2008) 320:789–92. doi:10.1126/science.1154413
84. Chen AH, Lubkowicz D, Yeong V, Chang RL, Silver PA. Transplantability ofa circadian clock to a noncircadian organism. Sci Adv (2015) 1:e1500358.
doi:10.1126/sciadv.1500358
85. Bruce VG, Pittendrigh CS. Temperature independence in a unicellular“clock”. Proc Natl Acad Sci U S A (1956) 42:676–82. doi:10.1073/pnas.42.9.676
86. Enright JT. Temperature compensation in short-duration time-measurement by an intertidal amphipod. Science (1967) 156:1510–2. doi:10.1126/
science.156.3781.1510
87. Hastings JW, Sweeney BM. On the mechanism of temperature independence
in a biological clock. Proc Natl Acad Sci U S A (1957) 43:804–11. doi:10.1073/
pnas.43.9.804
88. Pittendrigh CS. On temperature independence in the clock system controlling emergence time in Drosophila. Proc Natl Acad Sci U S A (1954)
40:1018–29. doi:10.1073/pnas.40.10.1018
89. Gardner GF, Feldman JF. Temperature compensation of circadian period
length in clock mutants of Neurospora crassa. Plant Physiol (1981) 68:1244–8.
doi:10.1104/pp.68.6.1244
90. Loros JJ, Feldman JF. Loss of temperature compensation of circadian period
length in the frq-9 mutant of Neurospora crassa. J Biol Rhythms (1986)
1:187–98. doi:10.1177/074873048600100302
91. Huang ZJ, Curtin KD, Rosbash M. PER protein interactions and temperaturecompensation of a circadian clock in Drosophila. Science (1995) 267:1169–72.
doi:10.1126/science.7855598
92. Sawyer LA, Hennessy JM, Peixoto AA, Rosato E, Parkinson H, Costa R, et al.
Natural variation in a Drosophila clock gene and temperature compensation.
Science (1997) 278:2117–20. doi:10.1126/science.278.5346.2117
93. Hong CI, Tyson JJ. A proposal for temperature compensation of thecircadian rhythm in Drosophila based on dimerization of the per protein.
Chronobiol Int (1997) 14:521–9. doi:10.3109/07420529709001473
94. Leloup JC, Goldbeter A. Temperature compensation of circadianrhythms: control of the period in a model for circadian oscillationsof the per protein in Drosophila. Chronobiol Int (1997) 14:511–20.
doi:10.3109/07420529709001472
95. Ruoff P, Vinsjevik M, Monnerjahn C, Rensing L. The Goodwin oscillator:
on the importance of degradation reactions in the circadian clock. J Biol
Rhythms (1999) 14:469–79. doi:10.1177/074873099129001037
96. Tyson JJ, Hong CI, Thron CD, Novak B. A simple model of circadian rhythms
based on dimerization and proteolysis of PER and TIM. Biophys J (1999)
77:2411–7. doi:10.1016/S0006-3495(99)77078-5
97. Takeuchi T, Hinohara T, Kurosawa G, Uchida K. A temperature-­compensated model for circadian rhythms that can be entrained bytemperature cycles. J Theor Biol (2007) 246:195–204. doi:10.1016/j.jtbi.
2006.12.028
98. Gould PD, Locke JCW, Larue C, Southern MM, Davis SJ, Hanano S, et al. The
molecular basis of temperature compensation in the Arabidopsis circadianclock. Plant Cell (2006) 18:1177–87. doi:10.1105/tpc.105.039990
99. Ruoff P, Rensing L, Kommedal R, Mohsenzadeh S. Modeling temperaturecompensation in chemical and biological oscillators. Chronobiol Int (1997)
14:499–510. doi:10.3109/07420529709001471
100. Ruoff P, Loros JJ, Dunlap JC. The relationship between FRQ-protein
stability and temperature compensation in the Neurospora circadianclock. Proc Natl Acad Sci U S A (2005) 102:17681–6. doi:10.1073/pnas.
0505137102
101. Mehra A, Shi M, Baker CL, Colot HV, Loros JJ, Dunlap JC. A role for casein
kinase 2 in the mechanism underlying circadian temperature compensation.
Cell (2009) 137:749–60. doi:10.1016/j.cell.2009.03.019

12

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

102. Zimmerman WF, Pittendrigh CS, Pavlidis T. Temperature compensation of the circadian oscillation in Drosophila pseudoobscura and its
entrainment by temperature cycles. J Insect Physiol (1968) 14:669–84.
doi:10.1016/0022-1910(68)90226-6
103. Pavlidis T, Zimmerman WF, Osborn J. [A mathematical model for thetemperature effects on circadian rhythms]. J Theor Biol (1968) 18:210–21.
doi:10.1016/0022-5193(68)90162-8
104. Tomita J, Nakajima M, Kondo T, Iwasaki H. No transcription-translation
feedback in circadian rhythm of KaiC phosphorylation. Science (2005)
307:251–4. doi:10.1126/science.1102540
105. Zhou M, Kim JK, Eng GWL, Forger DB, Virshup DM. A Period2 phosphoswitch regulates and temperature compensates circadian period. Mol
Cell (2015) 60:77–88. doi:10.1016/j.molcel.2015.08.022
106. Hussain F, Gupta C, Hirning AJ, Ott W, Matthews KS, Josic K, et al.
Engineered temperature compensation in a synthetic genetic clock. Proc
Natl Acad Sci U S A (2014) 111:972–7. doi:10.1073/pnas.1316298111
107. Hogenesch JB, Ueda HR. Understanding systems-level properties: timely stories from the study of clocks. Nat Rev Genet (2011) 12:407–16. doi:10.1038/
nrg2972108. Baggs JE, Price TS, DiTacchio L, Panda S, Fitzgerald GA, Hogenesch JB.
Network features of the mammalian circadian clock. PLoS Biol (2009) 7:e52.
doi:10.1371/journal.pbio.1000052
109. Dibner C, Sage D, Unser M, Bauer C, d’Eysmond T, Naef F, et al. Circadiangene expression is resilient to large fluctuations in overall transcription rates.
EMBO J (2009) 28:123–34. doi:10.1038/emboj.2008.262
110. Barkai N, Leibler S. Circadian clocks limited by noise. Nature (2000)
403:267–8. doi:10.1038/35002255
111. Ueda HR, Hirose K, Iino M. Intercellular coupling mechanism for synchronized and noise-resistant circadian oscillators. J Theor Biol (2002)
216:501–12. doi:10.1006/jtbi.2002.3000
112. DeBruyne JP, Weaver DR, Reppert SM. Peripheral circadian oscillators
require CLOCK. Curr Biol (2007) 17:R538–9. doi:10.1016/j.cub.2007.05.067
113. Liu AC, Welsh DK, Ko CH, Tran HG, Zhang EE, Priest AA, et al. Intercellular
coupling confers robustness against mutations in the SCN circadian clock
network. Cell (2007) 129:605–16. doi:10.1016/j.cell.2007.02.047
114. Zhang EE, Kay SA. Clocks not winding down: unravelling circadian
networks. Nat Rev Mol Cell Biol (2010) 11:764–76. doi:10.1038/nrm2995
115. Akman OE, Rand DA, Brown PE, Millar AJ. Robustness from flexibility in thefungal circadian clock. BMC Syst Biol (2010) 4:88. doi:10.1186/1752-0509-4-88
116. Saithong T, Painter KJ, Millar AJ. The contributions of interlocking loops
and extensive nonlinearity to the properties of circadian clock models. PLoS
One (2010) 5:e13867. doi:10.1371/journal.pone.0013867
117. Smolen P, Baxter DA, Byrne JH. Modeling circadian oscillations with interlocking positive and negative feedback loops. J Neurosci (2001) 21:6644–56.
118. Sriram K, Gopinathan MS. A two variable delay model for the circadianrhythm of Neurospora crassa. J Theor Biol (2004) 231:23–38. doi:10.1016/j.
jtbi.2004.04.006
119. André E, Conquet F, Steinmayr M, Stratton SC, Porciatti V, Becker-André
M. Disruption of retinoid-related orphan receptor beta changes circadianbehavior, causes retinal degeneration and leads to vacillans phenotype inmice. EMBO J (1998) 17:3867–77. doi:10.1093/emboj/17.14.3867
120. Liu AC, Tran HG, Zhang EE, Priest AA, Welsh DK, Kay SA. Redundantfunction of REV-ERBalpha and beta and non-essential role for Bmal1 cyclingin transcriptional regulation of intracellular circadian rhythms. PLoS Genet(2008) 4:e1000023. doi:10.1371/journal.pgen.1000023
121. Preitner N, Damiola F, Lopez-Molina L, Zakany J, Duboule D, Albrecht U,
et al. The orphan nuclear receptor REV-ERBα controls circadian transcription within the positive limb of the mammalian circadian oscillator. Cell
(2002) 110:251–60. doi:10.1016/S0092-8674(02)00825-5
122. Sato TK, Panda S, Miraglia LJ, Reyes TM, Rudic RD, McNamara P, et al. A
functional genomics strategy reveals Rora as a component of the mammalian
circadian clock. Neuron (2004) 43:527–37. doi:10.1016/j.neuron.2004.07.018
123. Vilar JMG, Kueh HY, Barkai N, Leibler S. Mechanisms of noise-resistance ingenetic oscillators. Proc Natl Acad Sci U S A (2002) 99:5988–92. doi:10.1073/
pnas.092133899
124. Kim JK, Forger DB. A mechanism for robust circadian timekeeping via
stoichiometric balance. Mol Syst Biol (2012) 8:630. doi:10.1038/msb.2012.62
125. Chen R, Schirmer A, Lee Y, Lee H, Kumar V, Yoo S-H, et al. Rhythmic
PER abundance defines a critical nodal point for negative feedback within

Frontiers in Neurology | www.frontiersin.org

the circadian clock mechanism. Mol Cell (2009) 36:417–30. doi:10.1016/j.
molcel.2009.10.012
126. Lee Y, Chen R, Lee H, Lee C. Stoichiometric relationship among clock
proteins determines robustness of circadian rhythms. J Biol Chem (2011)
286:7033–42. doi:10.1074/jbc.M110.207217
127. Lee C, Etchegaray JP, Cagampang FR, Loudon AS, Reppert SM.
Posttranslational mechanisms regulate the mammalian circadian clock. Cell
(2001) 107:855–67. doi:10.1016/S0092-8674(01)00610-9
128. Narumi R, Shimizu Y, Ukai-Tadenuma M, Ode KL, Kanda GN, Shinohara
Y, et al. Mass spectrometry-based absolute quantification reveals rhythmic
variation of mouse circadian clock proteins. Proc Natl Acad Sci U S A (2016)
113:E3461–7. doi:10.1073/pnas.1603799113
129. Kim JK. Protein sequestration versus Hill-type repression in circadianclock models. IET Syst Biol (2016) 10:125–35. doi:10.1049/iet-syb.2015.0090
130. Gotoh T, Kim JK, Liu J, Vila-Caballer M, Stauffer PE, Tyson JJ, et al. Modeldriven experimental approach reveals the complex regulatory distributionof p53 by the circadian factor Period 2. Proc Natl Acad Sci U S A (2016)
113(47):13516–21. doi:10.1073/pnas.1607984113
131. Saper CB, Lu J, Chou TC, Gooley J. The hypothalamic integrator for circadianrhythms. Trends Neurosci (2005) 28:152–7. doi:10.1016/j.tins.2004.12.009
132. Turek FW. Circadian neural rhythms in mammals. Annu Rev Physiol (1985)
47:49–64. doi:10.1146/annurev.ph.47.030185.000405
133. Yamazaki S, Numano R, Abe M, Hida A, Takahashi R, Ueda M, et al.
Resetting central and peripheral circadian oscillators in transgenic rats.
Science (2000) 288:682–5. doi:10.1126/science.288.5466.682
134. Balsalobre A, Damiola F, Schibler U. A serum shock induces circadiangene expression in mammalian tissue culture cells. Cell (1998) 93:929–37.
doi:10.1016/S0092-8674(00)81199-X
135. Yagita K, Okamura H. Forskolin induces circadian gene expression of rPer1,
rPer2 and dbp in mammalian rat-1 fibroblasts. FEBS Lett (2000) 465:79–82.
doi:10.1016/S0014-5793(99)01724-X
136. Yagita K, Tamanini F, van Der Horst GT, Okamura H. Molecular mechanismsof the biological clock in cultured fibroblasts. Science (2001) 292:278–81.
doi:10.1126/science.1059542
137. Bell-Pedersen D, Cassone VM, Earnest DJ, Golden SS, Hardin PE, Thomas
TL, et al. Circadian rhythms from multiple oscillators: lessons from diverse
organisms. Nat Rev Genet (2005) 6:544–56. doi:10.1038/nrg1633
138. Mohawk JA, Green CB, Takahashi JS. Central and peripheral circadianclocks in mammals. Annu Rev Neurosci (2012) 35:445–62. doi:10.1146/
annurev-neuro-060909-153128
139. Partch CL, Green CB, Takahashi JS. Molecular architecture of the mammalian circadian clock. Trends Cell Biol (2014) 24:90–9. doi:10.1016/j.
tcb.2013.07.002
140. Reppert SM, Weaver DR. Molecular analysis of mammalian circadianrhythms. Annu Rev Physiol (2001) 63:647–76. doi:10.1146/annurev.
physiol.63.1.647
141. Sehgal A, Price JL, Man B, Young MW. Loss of circadian behavioral rhythms
and per RNA oscillations in the Drosophila mutant timeless. Science (1994)
263:1603–6. doi:10.1126/science.8128246
142. King DP, Zhao Y, Sangoram AM, Wilsbacher LD, Tanaka M, Antoch MP,
et al. Positional cloning of the mouse circadian clock gene. Cell (1997)
89:641–53. doi:10.1016/S0092-8674(00)80245-7
143. Vitaterna MH, King DP, Chang AM, Kornhauser JM, Lowrey PL, McDonald
JD, et al. Mutagenesis and mapping of a mouse gene, Clock, essential
for circadian behavior. Science (1994) 264:719–25. doi:10.1126/science.
8171325
144. Hogenesch JB, Chan WK, Jackiw VH, Brown RC, Gu YZ, Pray-Grant M,
et al. Characterization of a subset of the basic-helix-loop-helix-PAS superfamily that interacts with components of the dioxin signaling pathway. J Biol
Chem (1997) 272:8581–93. doi:10.1074/jbc.272.13.8581
145. Ikeda M, Nomura M. cDNA cloning and tissue-specific expression of anovel basic helix-loop-helix/PAS protein (BMAL1) and identification ofalternatively spliced variants with alternative translation initiation siteusage. Biochem Biophys Res Commun (1997) 233:258–64. doi:10.1006/
bbrc.1997.6371
146. Kadener S, Stoleru D, McDonald M, Nawathean P, Rosbash M. Clockwork
Orange is a transcriptional repressor and a new Drosophila circadianpacemaker component. Genes Dev (2007) 21:1675–86. doi:10.1101/
gad.1552607

13

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

147. Lim C, Chung BY, Pitman JL, McGill JJ, Pradhan S, Lee J, et al. Clockwork
orange encodes a transcriptional repressor important for circadian-clock
amplitude in Drosophila. Curr Biol (2007) 17:1082–9. doi:10.1016/j.
cub.2007.05.039
148. Matsumoto A, Ukai-Tadenuma M, Yamada RG, Houl J, Uno KD, Kasukawa
T, et al. A functional genomics strategy reveals clockwork orange as a transcriptional regulator in the Drosophila circadian clock. Genes Dev (2007)
21:1687–700. doi:10.1101/gad.1552207
149. Honma S, Kawamoto T, Takagi Y, Fujimoto K, Sato F, Noshiro M, et al.
Dec1 and Dec2 are regulators of the mammalian molecular clock. Nature(2002) 419:841–4. doi:10.1038/nature01123
150. McDonald MJ, Rosbash M. Microarray analysis and organization of circadian gene expression in Drosophila. Cell (2001) 107:567–78. doi:10.1016/
S0092-8674(01)00545-1
151. Ueda HR, Matsumoto A, Kawamura M, Iino M, Tanimura T, Hashimoto
S. Genome-wide transcriptional orchestration of circadian rhythms in
Drosophila. J Biol Chem (2002) 277:14048–52. doi:10.1074/jbc.C100765200
152. Akhtar RA, Reddy AB, Maywood ES, Clayton JD, King VM, Smith AG,
et al. Circadian cycling of the mouse liver transcriptome, as revealed by
cDNA microarray, is driven by the suprachiasmatic nucleus. Curr Biol (2002)
12:540–50. doi:10.1016/S0960-9822(02)00759-5
153. Panda S, Antoch MP, Miller BH, Su AI, Schook AB, Straume M, et al.
Coordinated transcription of key pathways in the mouse by the circadianclock. Cell (2002) 109:307–20. doi:10.1016/S0092-8674(02)00722-5
154. Storch K-F, Lipan O, Leykin I, Viswanathan N, Davis FC, Wong WH, et al.
Extensive and divergent circadian gene expression in liver and heart. Nature(2002) 417:78–83. doi:10.1038/nature744
155. Ueda HR, Chen W, Adachi A, Wakamatsu H, Hayashi S, Takasugi T, et al. A
transcription factor response element for gene expression during circadiannight. Nature (2002) 418:534–9. doi:10.1038/nature00906
156. Humphries A, Klein D, Baler R, Carter DA. cDNA array analysis of pineal
gene expression reveals circadian rhythmicity of the dominant negative
helix-loop-helix protein-encoding gene, Id-1. J Neuroendocrinol (2002)
14:101–8. doi:10.1046/j.0007-1331.2001.00738.x
157. Duffield GE, Best JD, Meurers BH, Bittner A, Loros JJ, Dunlap JC. Circadianprograms of transcriptional activation, signaling, and protein turnover
revealed by microarray analysis of mammalian cells. Curr Biol (2002)
12:551–7. doi:10.1016/S0960-9822(02)00765-0
158. Grundschober C, Delaunay F, Pühlhofer A, Triqueneaux G, Laudet V,
Bartfai T, et al. Circadian regulation of diverse gene products revealed bymRNA expression profiling of synchronized fibroblasts. J Biol Chem (2001)
276:46751–8. doi:10.1074/jbc.M107499200
159. Schaffer R, Landgraf J, Accerbi M, Simon V, Larson M, Wisman E.
Microarray analysis of diurnal and circadian-regulated genes in Arabidopsis.
Plant Cell (2001) 13:113–23. doi:10.1105/tpc.13.1.113
160. Duffield GE. DNA microarray analyses of circadian timing: thegenomic basis of biological time. J Neuroendocrinol (2003) 15:991–1002.
doi:10.1046/j.1365-2826.2003.01082.x
161. Hughes ME, DiTacchio L, Hayes KR, Vollmers C, Pulivarthy S, Baggs JE,
et al. Harmonics of circadian gene transcription in mammals. PLoS Genet(2009) 5:e1000442. doi:10.1371/journal.pgen.1000442
162. Hughes ME, Hong H-K, Chong JL, Indacochea AA, Lee SS, Han M, et al.
Brain-specific rescue of Clock reveals system-driven transcriptional rhythms
in peripheral tissue. PLoS Genet (2012) 8:e1002835. doi:10.1371/journal.
pgen.1002835
163. Yoo S-H, Yamazaki S, Lowrey PL, Shimomura K, Ko CH, Buhr ED, et al.
PERIOD2:LUCIFERASE real-time reporting of circadian dynamics reveals
persistent circadian oscillations in mouse peripheral tissues. Proc Natl Acad
Sci U S A (2004) 101:5339–46. doi:10.1073/pnas.0308709101
164. Koike N, Yoo S-H, Huang H-C, Kumar V, Lee C, Kim T-K, et al.
Transcriptional architecture and chromatin landscape of the core circadianclock in mammals. Science (2012) 338:349–54. doi:10.1126/science.1226339
165. Menet JS, Rodriguez J, Abruzzi KC, Rosbash M. Nascent-Seq reveals novel
features of mouse circadian transcriptional regulation. Elife (2012) 1:e00011.
doi:10.7554/eLife.00011
166. Vollmers C, Schmitz RJ, Nathanson J, Yeo G, Ecker JR, Panda S. Circadianoscillations of protein-coding and regulatory RNAs in a highly dynamic
mammalian liver epigenome. Cell Metab (2012) 16:833–45. doi:10.1016/j.
cmet.2012.11.004

Frontiers in Neurology | www.frontiersin.org

167. Hatanaka F, Matsubara C, Myung J, Yoritaka T, Kamimura N, Tsutsumi S,
et al. Genome-wide profiling of the core clock protein BMAL1 targets reveals
a strict relationship with metabolism. Mol Cell Biol (2010) 30:5636–48.
doi:10.1128/MCB.00781-10
168. Le Martelot G, Canella D, Symul L, Migliavacca E, Gilardi F, Liechti R, et al.
Genome-wide RNA polymerase II profiles and RNA accumulation reveal
kinetics of transcription and associated epigenetic changes during diurnal
cycles. PLoS Biol (2012) 10:e1001442. doi:10.1371/journal.pbio.1001442
169. Rey G, Cesbron F, Rougemont J, Reinke H, Brunner M, Naef F. Genomewide and phase-specific DNA-binding rhythms of BMAL1 control circadian
output functions in mouse liver. PLoS Biol (2011) 9:e1000595. doi:10.1371/
journal.pbio.1000595
170. Yoshitane H, Ozaki H, Terajima H, Du N-H, Suzuki Y, Fujimori T, et al.
CLOCK-controlled polyphonic regulation of circadian rhythms throughcanonical and noncanonical e-boxes. Mol Cell Biol (2014) 34:1776–87.
doi:10.1128/MCB.01465-13
171. Ueda HR, Hayashi S, Chen W, Sano M, Machida M, Shigeyoshi Y, et al.
System-level identification of transcriptional circuits underlying mammalian
circadian clocks. Nat Genet (2005) 37:187–92. doi:10.1038/ng1504
172. Crosio C, Cermakian N, Allis CD, Sassone-Corsi P. Light induces chromatin
modification in cells of the mammalian circadian clock. Nat Neurosci (2000)
3:1241–7. doi:10.1038/81767
173. Etchegaray J-P, Lee C, Wade PA, Reppert SM. Rhythmic histone acetylation
underlies transcription in the mammalian circadian clock. Nature (2003)
421:177–82. doi:10.1038/nature01314
174. Nakahata Y, Kaluzova M, Grimaldi B, Sahar S, Hirayama J, Chen D, et al.
The NAD+-dependent deacetylase SIRT1 modulates CLOCK-mediatedchromatin remodeling and circadian control. Cell (2008) 134:329–40.
doi:10.1016/j.cell.2008.07.002
175. Doi M, Hirayama J, Sassone-Corsi P. Circadian regulator CLOCK is a histone
acetyltransferase. Cell (2006) 125:497–508. doi:10.1016/j.cell.2006.03.033
176. Ripperger JA, Schibler U. Rhythmic CLOCK-BMAL1 binding to multiple
E-box motifs drives circadian Dbp transcription and chromatin transitions.
Nat Genet (2006) 38:369–74. doi:10.1038/ng1738
177. Hirayama J, Sahar S, Grimaldi B, Tamaru T, Takamatsu K, Nakahata Y,
et al. CLOCK-mediated acetylation of BMAL1 controls circadian function.
Nature (2007) 450:1086–90. doi:10.1038/nature06394
178. Asher G, Gatfield D, Stratmann M, Reinke H, Dibner C, Kreppel F, et al.
SIRT1 regulates circadian clock gene expression through PER2 deacetylation.
Cell (2008) 134:317–28. doi:10.1016/j.cell.2008.06.050
179. Aguilar-Arnal L, Katada S, Orozco-Solis R, Sassone-Corsi P. NAD(+)-SIRT1
control of H3K4 trimethylation through circadian deacetylation of MLL1.
Nat Struct Mol Biol (2015) 22:312–8. doi:10.1038/nsmb.2990
180. Peek CB, Affinati AH, Ramsey KM, Kuo H-Y, Yu W, Sena LA, et al. Circadianclock NAD+ cycle drives mitochondrial oxidative metabolism in mice.
Science (2013) 342:1243417. doi:10.1126/science.1243417
181. Nakahata Y, Sahar S, Astarita G, Kaluzova M, Sassone-Corsi P. Circadian
control of the NAD+ salvage pathway by CLOCK-SIRT1. Science (2009)
324:654–7. doi:10.1126/science.1170803
182. Rutter J, Reick M, Wu LC, McKnight SL. Regulation of clock and NPAS2
DNA binding by the redox state of NAD cofactors. Science (2001) 293:510–4.
doi:10.1126/science.1060698
183. Huang G, Zhang Y, Shan Y, Yang S, Chelliah Y, Wang H, et al. Circadianoscillations of NADH redox state using a heterologous metabolic sensor
in mammalian cells. J Biol Chem (2016) 291:23906–14. doi:10.1074/jbc.
M116.728774
184. Cho H, Zhao X, Hatori M, Yu RT, Barish GD, Lam MT, et al. Regulationof circadian behaviour and metabolism by REV-ERB-α and REV-ERB-β.
Nature (2012) 485:123–7. doi:10.1038/nature11048
185. Feng D, Liu T, Sun Z, Bugge A, Mullican SE, Alenghat T, et al. A circadianrhythm orchestrated by histone deacetylase 3 controls hepatic lipid metabolism. Science (2011) 331:1315–9. doi:10.1126/science.1198125
186. Bartok O, Kyriacou CP, Levine J, Sehgal A, Kadener S. Adaptation of molecular circadian clockwork to environmental changes: a role for alternative
splicing and miRNAs. Proc Biol Sci (2013) 280:20130011. doi:10.1098/
rspb.2013.0011
187. Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet (2010) 11:597–610. doi:10.1038/
nrg2843

14

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

188. Mehta N, Cheng H-YM. Micro-managing the circadian clock: the roleof microRNAs in biological timekeeping. J Mol Biol (2013) 425:3609–24.
doi:10.1016/j.jmb.2012.10.022
189. Yates LA, Norbury CJ, Gilbert RJC. The long and short of microRNA. Cell
(2013) 153:516–9. doi:10.1016/j.cell.2013.04.003
190. Na YJ, Sung JH, Lee SC, Lee YJ, Choi YJ, Park WY, et al. Comprehensive
analysis of microRNA-mRNA co-expression in circadian rhythm. Exp Mol
Med (2009) 41:638–47. doi:10.3858/emm.2009.41.9.070
191. Xu S, Witmer PD, Lumayag S, Kovacs B, Valle D. MicroRNA (miRNA)transcriptome of mouse retina and identification of a sensory organ-specific miRNA cluster. J Biol Chem (2007) 282:25053–66. doi:10.1074/jbc.
M700501200
192. Cheng H-YM, Papp JW, Varlamova O, Dziema H, Russell B, Curfman JP,
et al. microRNA modulation of circadian-clock period and entrainment.
Neuron (2007) 54:813–29. doi:10.1016/j.neuron.2007.05.017
193. Gao Q, Zhou L, Yang S-Y, Cao J-M. A novel role of microRNA 17-5p inthe modulation of circadian rhythm. Sci Rep (2016) 6:30070. doi:10.1038/
srep30070
194. Gatfield D, Le Martelot G, Vejnar CE, Gerlach D, Schaad O, Fleury-Olela F,
et al. Integration of microRNA miR-122 in hepatic circadian gene expression.
Genes Dev (2009) 23:1313–26. doi:10.1101/gad.1781009
195. Curtis AM, Fagundes CT, Yang G, Palsson-McDermott EM, Wochal P,
McGettrick AF, et al. Circadian control of innate immunity in macrophages
by miR-155 targeting Bmal1. Proc Natl Acad Sci U S A (2015) 112:7231–6.
doi:10.1073/pnas.1501327112
196. Shende VR, Neuendorff N, Earnest DJ. Role of miR-142-3p in the post-transcriptional regulation of the clock gene Bmal1 in the mouse SCN. PLoS One(2013) 8:e65300. doi:10.1371/journal.pone.0065300
197. Tan X, Zhang P, Zhou L, Yin B, Pan H, Peng X. Clock-controlled mir142-3p can target its activator, Bmal1. BMC Mol Biol (2012) 13:27.
doi:10.1186/1471-2199-13-27
198. Zhang W, Wang P, Chen S, Zhang Z, Liang T, Liu C. Rhythmic expressionof miR-27b-3p targets the clock gene Bmal1 at the posttranscriptional levelin the mouse liver. FASEB J (2016) 30:2151–60. doi:10.1096/fj.201500120
199. Ding X, Sun B, Huang J, Xu L, Pan J, Fang C, et al. The role of miR182 in regulating pineal CLOCK expression after hypoxia-ischemia brain
injury in neonatal rats. Neurosci Lett (2015) 591:75–80. doi:10.1016/j.
neulet.2015.02.026
200. Lerner I, Bartok O, Wolfson V, Menet JS, Weissbein U, Afik S, et al.
Clk post-transcriptional control denoises circadian transcription both
temporally and spatially. Nat Commun (2015) 6:7056. doi:10.1038/
ncomms8056
201. Kojima S, Gatfield D, Esau CC, Green CB. MicroRNA-122 modulates therhythmic expression profile of the circadian deadenylase Nocturnin in mouse
liver. PLoS One (2010) 5:e11264. doi:10.1371/journal.pone.0011264
202. Chen R, D’Alessandro M, Lee C. miRNAs are required for generating atime delay critical for the circadian oscillator. Curr Biol (2013) 23:1959–68.
doi:10.1016/j.cub.2013.08.005
203. Nagel R, Clijsters L, Agami R. The miRNA-192/194 cluster regulates the
Period gene family and the circadian clock. FEBS J (2009) 276:5447–55.
doi:10.1111/j.1742-4658.2009.07229.x
204. Zhao X, Zhu X, Cheng S, Xie Y, Wang Z, Liu Y, et al. MiR-29a/b/c regulate
human circadian gene hPER1 expression by targeting its 3’UTR. Acta
Biochim Biophys Sin (2014) 46:313–7. doi:10.1093/abbs/gmu007
205. Chen W, Liu Z, Li T, Zhang R, Xue Y, Zhong Y, et al. Regulation of Drosophila
circadian rhythms by miRNA let-7 is mediated by a regulatory cycle. Nat
Commun (2014) 5:5549. doi:10.1038/ncomms6549
206. Chen X, Rosbash M. mir-276a strengthens Drosophila circadian rhythms
by regulating timeless expression. Proc Natl Acad Sci U S A (2016)
113:E2965–72. doi:10.1073/pnas.1605837113
207. Lee K-H, Kim S-H, Lee H-R, Kim W, Kim D-Y, Shin J-C, et al. MicroRNA-185
oscillation controls circadian amplitude of mouse Cryptochrome 1 via
translational regulation. Mol Biol Cell (2013) 24:2248–55. doi:10.1091/mbc.
E12-12-0849
208. Du N-H, Arpat AB, De Matos M, Gatfield D. MicroRNAs shape circadian
hepatic gene expression on a transcriptome-wide scale. Elife (2014) 3:e02510.
doi:10.7554/eLife.02510
209. Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, et al. The
GENCODE v7 catalog of human long noncoding RNAs: analysis of their

Frontiers in Neurology | www.frontiersin.org

gene structure, evolution, and expression. Genome Res (2012) 22:1775–89.
doi:10.1101/gr.132159.111
210. Hangauer MJ, Vaughn IW, McManus MT. Pervasive transcription of
the human genome produces thousands of previously unidentified longintergenic noncoding RNAs. PLoS Genet (2013) 9:e1003569. doi:10.1371/
journal.pgen.1003569
211. Schick S, Becker K, Thakurela S, Fournier D, Hampel MH, Legewie S, et al.
Identifying novel transcriptional regulators with circadian expression. Mol
Cell Biol (2015) 36:545–58. doi:10.1128/MCB.00701-15
212. Zhang R, Lahens NF, Ballance HI, Hughes ME, Hogenesch JB. A circadiangene expression atlas in mammals: implications for biology and medicine.
Proc Natl Acad Sci U S A (2014) 111:16219–24. doi:10.1073/pnas.1408886111
213. Coon SL, Munson PJ, Cherukuri PF, Sugden D, Rath MF, Møller M, et al.
Circadian changes in long noncoding RNAs in the pineal gland. Proc Natl
Acad Sci U S A (2012) 109:13319–24. doi:10.1073/pnas.1207748109
214. Sauman I, Reppert SM. Circadian clock neurons in the silkmoth Antheraea
pernyi: novel mechanisms of Period protein regulation. Neuron (1996)
17:889–900. doi:10.1016/S0896-6273(00)80220-2
215. Kramer C, Loros JJ, Dunlap JC, Crosthwaite SK. Role for antisense RNA
in regulating circadian clock function in Neurospora crassa. Nature (2003)
421:948–52. doi:10.1038/nature01427
216. Li N, Joska TM, Ruesch CE, Coster SJ, Belden WJ. The frequency naturalantisense transcript first promotes, then represses, frequency gene expression via facultative heterochromatin. Proc Natl Acad Sci U S A (2015)
112:4357–62. doi:10.1073/pnas.1406130112
217. Xue Z, Ye Q, Anson SR, Yang J, Xiao G, Kowbel D, et al. Transcriptional
interference by antisense RNA is required for circadian clock function.
Nature (2014) 514:650–3. doi:10.1038/nature13671
218. Powell WT, Coulson RL, Crary FK, Wong SS, Ach RA, Tsang P, et al. A
Prader-Willi locus lncRNA cloud modulates diurnal genes and energy
expenditure. Hum Mol Genet (2013) 22:4318–28. doi:10.1093/hmg/ddt281
219. Cui M, Zheng M, Sun B, Wang Y, Ye L, Zhang X. A long noncoding RNA
perturbs the circadian rhythm of hepatoma cells to facilitate hepatocarcinogenesis. Neoplasia (2015) 17:79–88. doi:10.1016/j.neo.2014.11.004
220. Rodriguez J, Tang C-HA, Khodor YL, Vodala S, Menet JS, Rosbash M.
Nascent-Seq analysis of Drosophila cycling gene expression. Proc Natl Acad
Sci U S A (2013) 110:E275–84. doi:10.1073/pnas.1219969110
221. Lim C, Allada R. Emerging roles for post-transcriptional regulation incircadian clocks. Nat Neurosci (2013) 16:1544–50. doi:10.1038/nn.3543
222. So WV, Rosbash M. Post-transcriptional regulation contributes to Drosophila
clock gene mRNA cycling. EMBO J (1997) 16:7146–55. doi:10.1093/
emboj/16.23.7146
223. Wilsbacher LD, Yamazaki S, Herzog ED, Song E-J, Radcliffe LA, Abe M, et al.
Photic and circadian expression of luciferase in mPeriod1-luc transgenic
mice invivo. Proc Natl Acad Sci U S A (2002) 99:489–94. doi:10.1073/
pnas.012248599
224. Kojima S, Hirose M, Tokunaga K, Sakaki Y, Tei H. Structural and functional
analysis of 3’ untranslated region of mouse Period1 mRNA. Biochem Biophys
Res Commun (2003) 301:1–7. doi:10.1016/S0006-291X(02)02938-8
225. Kojima S, Matsumoto K, Hirose M, Shimada M, Nagano M, Shigeyoshi
Y, et al. LARK activates posttranscriptional expression of an essential
mammalian clock protein, PERIOD1. Proc Natl Acad Sci U S A (2007)
104:1859–64. doi:10.1073/pnas.0607567104
226. Huang Y, McNeil GP, Jackson FR. Translational regulation of the
DOUBLETIME/CKIδ/ε kinase by LARK contributes to circadian period
modulation. PLoS Genet (2014) 10:e1004536. doi:10.1371/journal.
pgen.1004536
227. Kim D-Y, Woo K-C, Lee K-H, Kim T-D, Kim K-T. hnRNP Q and PTB modulate the circadian oscillation of mouse Rev-erb alpha via IRES-mediatedtranslation. Nucleic Acids Res (2010) 38:7068–78. doi:10.1093/nar/gkq569
228. Kim D-Y, Kwak E, Kim S-H, Lee K-H, Woo K-C, Kim K-T. hnRNP Q
mediates a phase-dependent translation-coupled mRNA decay of mouse
Period3. Nucleic Acids Res (2011) 39:8901–14. doi:10.1093/nar/gkr605
229. Kim T-D, Woo K-C, Cho S, Ha D-C, Jang SK, Kim K-T. Rhythmic controlof AANAT translation by hnRNP Q in circadian melatonin production.
Genes Dev (2007) 21:797–810. doi:10.1101/gad.1519507
230. Kwak E, Kim T-D, Kim K-T. Essential role of 3’-untranslated region-mediated mRNA decay in circadian oscillations of mouse Period3 mRNA. J Biol
Chem (2006) 281:19100–6. doi:10.1074/jbc.M511927200

15

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

231. Lee K-H, Woo K-C, Kim D-Y, Kim T-D, Shin J, Park SM, et al. Rhythmic
interaction between Period1 mRNA and hnRNP Q leads to circadiantime-dependent translation. Mol Cell Biol (2012) 32:717–28. doi:10.1128/
MCB.06177-11
232. Lim I, Jung Y, Kim D-Y, Kim K-T. HnRNP Q has a suppressive role in
the translation of mouse Cryptochrome1. PLoS One (2016) 11:e0159018.
doi:10.1371/journal.pone.0159018
233. Lee K-H, Kim S-H, Kim H-J, Kim W, Lee H-R, Jung Y, et al. AUF1 contributes
to Cryptochrome1 mRNA degradation and rhythmic translation. Nucleic
Acids Res (2014) 42:3590–606. doi:10.1093/nar/gkt1379
234. Woo K-C, Ha D-C, Lee K-H, Kim D-Y, Kim T-D, Kim K-T. Circadian
amplitude of cryptochrome 1 is modulated by mRNA stability regulation
via cytoplasmic hnRNP D oscillation. Mol Cell Biol (2010) 30:197–205.
doi:10.1128/MCB.01154-09
235. Woo K-C, Kim T-D, Lee K-H, Kim D-Y, Kim W, Lee K-Y, et al. Mouse period
2 mRNA circadian oscillation is modulated by PTB-mediated rhythmicmRNA degradation. Nucleic Acids Res (2009) 37:26–37. doi:10.1093/nar/
gkn893236. Proudfoot NJ, Furger A, Dye MJ. Integrating mRNA processing withtranscription. Cell (2002) 108:501–12. doi:10.1016/S0092-8674(02)00617-7
237. Doidge R, Mittal S, Aslam A, Winkler GS. Deadenylation of cytoplasmicmRNA by the mammalian Ccr4-Not complex. Biochem Soc Trans (2012)
40:896–901. doi:10.1042/BST20120074
238. Virtanen A, Henriksson N, Nilsson P, Nissbeck M. Poly(A)-specificribonuclease (PARN): an allosterically regulated, processive and mRNA
cap-interacting deadenylase. Crit Rev Biochem Mol Biol (2013) 48:192–209.
doi:10.3109/10409238.2013.771132
239. Robinson BG, Frim DM, Schwartz WJ, Majzoub JA. Vasopressin mRNAin the suprachiasmatic nuclei: daily regulation of polyadenylate tail length.
Science (1988) 241:342–4. doi:10.1126/science.3388044
240. Green CB, Besharse JC. Identification of a novel vertebrate circadianclock-regulated gene encoding the protein nocturnin. Proc Natl Acad Sci
U S A (1996) 93:14884–8. doi:10.1073/pnas.93.25.14884
241. Green CB, Besharse JC. Use of a high stringency differential display screen
for identification of retinal mRNAs that are regulated by a circadianclock. Brain Res Mol Brain Res (1996) 37:157–65. doi:10.1016/0169-328X(95)00307-E
242. Wang Y, Osterbur DL, Megaw PL, Tosini G, Fukuhara C, Green CB, et al.
Rhythmic expression of Nocturnin mRNA in multiple tissues of the mouse.
BMC Dev Biol (2001) 1:9. doi:10.1186/1471-213X-1-9
243. Kornmann B, Schaad O, Bujard H, Takahashi JS, Schibler U. System-driven
and oscillator-dependent circadian transcription in mice with a conditionallyactive liver clock. PLoS Biol (2007) 5:e34. doi:10.1371/journal.pbio.0050034
244. Green CB, Douris N, Kojima S, Strayer CA, Fogerty J, Lourim D, et al. Lossof Nocturnin, a circadian deadenylase, confers resistance to hepatic steatosis
and diet-induced obesity. Proc Natl Acad Sci U S A (2007) 104:9888–93.
doi:10.1073/pnas.0702448104
245. Nagoshi E, Sugino K, Kula E, Okazaki E, Tachibana T, Nelson S, et al.
Dissecting differential gene expression within the circadian neuronal circuitof Drosophila. Nat Neurosci (2010) 13:60–8. doi:10.1038/nn.2451
246. Kojima S, Sher-Chen EL, Green CB. Circadian control of mRNA polyadenylation dynamics regulates rhythmic protein expression. Genes Dev (2012)
26:2724–36. doi:10.1101/gad.208306.112
247. Chang H, Lim J, Ha M, Kim VN. TAIL-seq: genome-wide determination ofpoly(A) tail length and 3’ end modifications. Mol Cell (2014) 53:1044–52.
doi:10.1016/j.molcel.2014.02.007
248. Subtelny AO, Eichhorn SW, Chen GR, Sive H, Bartel DP. Poly(A)-tailprofiling reveals an embryonic switch in translational control. Nature (2014)
508:66–71. doi:10.1038/nature13007
249. Fustin J-M, Doi M, Yamaguchi Y, Hida H, Nishimura S, Yoshida M,
et al. RNA-methylation-dependent RNA processing controls the speedof the circadian clock. Cell (2013) 155:793–806. doi:10.1016/j.cell.2013.
10.026250. Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D, et al. N6-methyladenosinedependent regulation of messenger RNA stability. Nature (2014) 505:117–20.
doi:10.1038/nature12730
251. Rouskin S, Zubradt M, Washietl S, Kellis M, Weissman JS. Genome-wide
probing of RNA structure reveals active unfolding of mRNA structuresin vivo. Nature (2014) 505:701–5. doi:10.1038/nature12894

Frontiers in Neurology | www.frontiersin.org

252. Wan Y, Qu K, Zhang QC, Flynn RA, Manor O, Ouyang Z, et al. Landscapeand variation of RNA secondary structure across the human transcriptome.
Nature (2014) 505:706–9. doi:10.1038/nature12946
253. Imamachi N, Tani H, Mizutani R, Imamura K, Irie T, Suzuki Y, et al. BRICseq: a genome-wide approach for determining RNA stability in mammaliancells. Methods (2014) 67:55–63. doi:10.1016/j.ymeth.2013.07.014
254. Tani H, Akimitsu N. Genome-wide technology for determining RNA stability in mammalian cells: historical perspective and recent advantages basedon modified nucleotide labeling. RNA Biol (2012) 9:1233–8. doi:10.4161/
rna.22036
255. Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C,
et al. Insights into RNA biology from an atlas of mammalian mRNA-binding
proteins. Cell (2012) 149:1393–406. doi:10.1016/j.cell.2012.04.031
256. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger
P, et al. Transcriptome-wide identification of RNA-binding protein andmicroRNA target sites by PAR-CLIP. Cell (2010) 141:129–41. doi:10.1016/j.
cell.2010.03.009
257. Jensen KB, Darnell RB. CLIP: crosslinking and immunoprecipitation ofin vivo RNA targets of RNA-binding proteins. Methods Mol Biol (2008)
488:85–98. doi:10.1007/978-1-60327-475-3_6
258. König J, Zarnack K, Rot G, Curk T, Kayikci M, Zupan B, et al. iCLIP
reveals the function of hnRNP particles in splicing at individual nucleotide
resolution. Nat Struct Mol Biol (2010) 17:909–15. doi:10.1038/nsmb.1838
259. Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, et al. HITS-CLIP
yields genome-wide insights into brain alternative RNA processing. Nature(2008) 456:464–9. doi:10.1038/nature07488
260. Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, Darnell RB. CLIP identifies
Nova-regulated RNA networks in the brain. Science (2003) 302:1212–5.
doi:10.1126/science.1090095
261. Morf J, Rey G, Schneider K, Stratmann M, Fujita J, Naef F, et al. Coldinducible RNA-binding protein modulates circadian gene expression posttranscriptionally. Science (2012) 338:379–83. doi:10.1126/science.1217726
262. Reddy AB, Karp NA, Maywood ES, Sage EA, Deery M, O’Neill JS,
et al. Circadian orchestration of the hepatic proteome. Curr Biol (2006)
16:1107–15. doi:10.1016/j.cub.2006.04.026
263. Tsuji T, Hirota T, Takemori N, Komori N, Yoshitane H, Fukuda M, et al.
Circadian proteomics of the mouse retina. Proteomics (2007) 7:3500–8.
doi:10.1002/pmic.200700272
264. Podobed P, Pyle WG, Ackloo S, Alibhai FJ, Tsimakouridze EV, Ratcliffe
WF, et al. The day/night proteome in the murine heart. Am J Physiol Regul
Integr Comp Physiol (2014) 307:R121–37. doi:10.1152/ajpregu.00011.2014
265. Akimoto H, Wu C, Kinumi T, Ohmiya Y. Biological rhythmicity in
expressed proteins of the marine dinoflagellate Lingulodinium polyedrum
demonstrated by chronological proteomics. Biochem Biophys Res Commun(2004) 315:306–12. doi:10.1016/j.bbrc.2004.01.054
266. Wagner V, Fiedler M, Markert C, Hippler M, Mittag M. Functional proteomics of circadian expressed proteins from Chlamydomonas reinhardtii. FEBS
Lett (2004) 559:129–35. doi:10.1016/S0014-5793(04)00051-1
267. Choudhary MK, Nomura Y, Shi H, Nakagami H, Somers DE. Circadian
profiling of the arabidopsis proteome using 2D-DIGE. Front Plant Sci (2016)
7:1007. doi:10.3389/fpls.2016.01007
268. Hwang H, Cho M-H, Hahn B-S, Lim H, Kwon Y-K, Hahn T-R, et al.
Proteomic identification of rhythmic proteins in rice seedlings. Biochim
Biophys Acta (2011) 1814:470–9. doi:10.1016/j.bbapap.2011.01.010
269. Brun V, Masselon C, Garin J, Dupuis A. Isotope dilution strategies for absolute quantitative proteomics. J Proteomics (2009) 72:740–9. doi:10.1016/j.
jprot.2009.03.007
270. Mauvoisin D, Wang J, Jouffe C, Martin E, Atger F, Waridel P, et al. Circadianclock-dependent and -independent rhythmic proteomes implement distinct
diurnal functions in mouse liver. Proc Natl Acad Sci U S A (2014) 111:167–72.
doi:10.1073/pnas.1314066111
271. Robles MS, Cox J, Mann M. In-vivo quantitative proteomics reveals a key
contribution of post-transcriptional mechanisms to the circadian regulationof liver metabolism. PLoS Genet (2014) 10:e1004047. doi:10.1371/journal.
pgen.1004047
272. Chiang C-K, Mehta N, Patel A, Zhang P, Ning Z, Mayne J, et al. The proteomic landscape of the suprachiasmatic nucleus clock reveals large-scale
coordination of key biological processes. PLoS Genet (2014) 10:e1004695.
doi:10.1371/journal.pgen.1004695

16

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

273. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS.
Proc Natl Acad Sci U S A (2003) 100:6940–5. doi:10.1073/pnas.0832254100
274. Hanke S, Besir H, Oesterhelt D, Mann M. Absolute SILAC for accuratequantitation of proteins in complex mixtures down to the attomole level.
J Proteome Res (2008) 7:1118–30. doi:10.1021/pr7007175
275. Simicevic J, Schmid AW, Gilardoni PA, Zoller B, Raghav SK, Krier I, et al.
Absolute quantification of transcription factors during cellular differentiation using multiplexed targeted proteomics. Nat Methods (2013) 10:570–6.
doi:10.1038/nmeth.2441
276. Beynon RJ, Doherty MK, Pratt JM, Gaskell SJ. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenatedsignature peptides. Nat Methods (2005) 2:587–9. doi:10.1038/nmeth774
277. Mirzaei H, McBee JK, Watts J, Aebersold R. Comparative evaluation ofcurrent peptide production platforms used in absolute quantificationin proteomics. Mol Cell Proteomics (2008) 7:813–23. doi:10.1074/mcp.
M700495-MCP200
278. Stergachis AB, MacLean B, Lee K, Stamatoyannopoulos JA, MacCoss
MJ. Rapid empirical discovery of optimal peptides for targeted proteomics.
Nat Methods (2011) 8:1041–3. doi:10.1038/nmeth.1770
279. Shimizu Y, Inoue A, Tomari Y, Suzuki T, Yokogawa T, Nishikawa K, et al.
Cell-free translation reconstituted with purified components. Nat Biotechnol(2001) 19:751–5. doi:10.1038/90802
280. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate
proteome-wide label-free quantification by delayed normalization andmaximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics(2014) 13:2513–26. doi:10.1074/mcp.M113.031591
281. Deshmukh AS, Murgia M, Nagaraj N, Treebak JT, Cox J, Mann M. Deep
proteomics of mouse skeletal muscle enables quantitation of protein isoforms, metabolic pathways, and transcription factors. Mol Cell Proteomics(2015) 14:841–53. doi:10.1074/mcp.M114.044222
282. Guerreiro ACL, Benevento M, Lehmann R, van Breukelen B, Post H,
Giansanti P, et al. Daily rhythms in the cyanobacterium Synechococcus
elongatus probed by high-resolution mass spectrometry-based proteomics
reveals a small defined set of cyclic proteins. Mol Cell Proteomics (2014)
13:2042–55. doi:10.1074/mcp.M113.035840
283. Robles MS, Humphrey SJ, Mann M. Phosphorylation is a central mechanism
for circadian control of metabolism and physiology. Cell Metab (2017)25:118–27. doi:10.1016/j.cmet.2016.10.004
284. Reiland S, Messerli G, Baerenfaller K, Gerrits B, Endler A, GrossmannJ, et al. Large-scale Arabidopsis phosphoproteome profiling reveals novel
chloroplast kinase substrates and phosphorylation networks. Plant Physiol(2009) 150:889–903. doi:10.1104/pp.109.138677
285. Ang JE, Revell V, Mann A, Mäntele S, Otway DT, Johnston JD, et al.
Identification of human plasma metabolites exhibiting time-of-day
variation using an untargeted liquid chromatography-mass spectrometry
metabolomic approach. Chronobiol Int (2012) 29:868–81. doi:10.3109/07
420528.2012.699122
286. Chua EC-P, Shui G, Lee IT-G, Lau P, Tan L-C, Yeo S-C, et al. Extensive
diversity in circadian regulation of plasma lipids and evidence for different
circadian metabolic phenotypes in humans. Proc Natl Acad Sci U S A (2013)
110:14468–73. doi:10.1073/pnas.1222647110
287. Dallmann R, Viola AU, Tarokh L, Cajochen C, Brown SA. The humancircadian metabolome. Proc Natl Acad Sci U S A (2012) 109:2625–9.
doi:10.1073/pnas.1114410109
288. Kasukawa T, Sugimoto M, Hida A, Minami Y, Mori M, Honma S, et al.
Human blood metabolite timetable indicates internal body time. Proc Natl
Acad Sci U S A (2012) 109:15036–41. doi:10.1073/pnas.1207768109
289. Eckel-Mahan KL, Patel VR, Mohney RP, Vignola KS, Baldi P, Sassone-Corsi
P. Coordination of the transcriptome and metabolome by the circadian clock.
Proc Natl Acad Sci U S A (2012) 109:5541–6. doi:10.1073/pnas.1118726109
290. Fustin J-M, Doi M, Yamada H, Komatsu R, Shimba S, Okamura H. Rhythmic
nucleotide synthesis in the liver: temporal segregation of metabolites. Cell
Rep (2012) 1:341–9. doi:10.1016/j.celrep.2012.03.001
291. Minami Y, Kasukawa T, Kakazu Y, Iigo M, Sugimoto M, Ikeda S, et al.
Measurement of internal body time by blood metabolomics. Proc Natl Acad
Sci U S A (2009) 106:9890–5. doi:10.1073/pnas.0900617106

Frontiers in Neurology | www.frontiersin.org

292. Patel VR, Eckel-Mahan K, Sassone-Corsi P, Baldi P. CircadiOmics: integrating circadian genomics, transcriptomics, proteomics and metabolomics. Nat
Methods (2012) 9:772–3. doi:10.1038/nmeth.2111
293. Abbondante S, Eckel-Mahan KL, Ceglia NJ, Baldi P, Sassone-Corsi P.
Comparative circadian metabolomics reveal differential effects of nutritional challenge in the serum and liver. J Biol Chem (2016) 291:2812–28.
doi:10.1074/jbc.M115.681130
294. Eckel-Mahan KL, Patel VR, de Mateo S, Orozco-Solis R, Ceglia NJ, Sahar
S, et al. Reprogramming of the circadian clock by nutritional challenge. Cell
(2013) 155:1464–78. doi:10.1016/j.cell.2013.11.034
295. Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S,
et al. Time-restricted feeding without reducing caloric intake prevents
metabolic diseases in mice fed a high-fat diet. Cell Metab (2012) 15:848–60.
doi:10.1016/j.cmet.2012.04.019
296. Chua EC-P, Shui G, Cazenave-Gassiot A, Wenk MR, Gooley JJ. Changes
in plasma lipids during exposure to total sleep deprivation. Sleep (2015)
38:1683–91. doi:10.5665/sleep.5142
297. Davies SK, Ang JE, Revell VL, Holmes B, Mann A, Robertson FP, et al.
Effect of sleep deprivation on the human metabolome. Proc Natl Acad Sci
U S A (2014) 111:10761–6. doi:10.1073/pnas.1402663111
298. Giskeødegård GF, Davies SK, Revell VL, Keun H, Skene DJ. Diurnal rhythms
in the human urine metabolome during sleep and total sleep deprivation.
Sci Rep (2015) 5:14843. doi:10.1038/srep14843
299. Weljie AM, Meerlo P, Goel N, Sengupta A, Kayser MS, Abel T, et al. Oxalic
acid and diacylglycerol 36:3 are cross-species markers of sleep debt. Proc Natl
Acad Sci U S A (2015) 112:2569–74. doi:10.1073/pnas.1417432112
300. Su G, Burant CF, Beecher CW, Athey BD, Meng F. Integrated metabolome
and transcriptome analysis of the NCI60 dataset. BMC Bioinformatics (2011)
12:S36. doi:10.1186/1471-2105-12-S1-S36
301. Sengupta A, Krishnaiah SY, Rhoades S, Growe J, Slaff B, Venkataraman
A, et al. Deciphering the duality of clock and growth metabolism in a cellautonomous system using NMR profiling of the secretome. Metabolites(2016) 6:23. doi:10.3390/metabo6030023
302. Ueda HR, Chen W, Minami Y, Honma S, Honma K, Iino M, et al. Moleculartimetable methods for detection of body time and rhythm disorders fromsingle-time-point genome-wide expression profiles. Proc Natl Acad Sci U S
A (2004) 101:11227–32. doi:10.1073/pnas.0401882101
303. Martinez-Lozano Sinues P, Tarokh L, Li X, Kohler M, Brown SA, Zenobi
R, et al. Circadian variation of the human metabolome captured by realtime breath analysis. PLoS One (2014) 9:e114422. doi:10.1371/journal.
pone.0114422
304. Selfridge JM, Gotoh T, Schiffhauer S, Liu J, Stauffer PE, Li A, et al.
Chronotherapy: intuitive, sound, founded…but not broadly applied. Drugs(2016) 76:1507–21. doi:10.1007/s40265-016-0646-4
305. Gygi SP, Rochon Y, Franza BR, Aebersold R. Correlation between protein andmRNA abundance in yeast. Mol Cell Biol (1999) 19:1720–30. doi:10.1128/
MCB.19.3.1720
306. Sivan G, Kedersha N, Elroy-Stein O. Ribosomal slowdown mediates translational arrest during cellular division. Mol Cell Biol (2007) 27:6639–46.
doi:10.1128/MCB.00798-07
307. Zong Q, Schummer M, Hood L, Morris DR. Messenger RNA translation
state: the second dimension of high-throughput expression screening.
Proc Natl Acad Sci U S A (1999) 96:10632–6. doi:10.1073/pnas.96.
19.10632
308. Doyle JP, Dougherty JD, Heiman M, Schmidt EF, Stevens TR, Ma G,
et al. Application of a translational profiling approach for the comparative
analysis of CNS cell types. Cell (2008) 135:749–62. doi:10.1016/j.cell.2008.
10.029309. Heiman M, Schaefer A, Gong S, Peterson JD, Day M, Ramsey KE, et al. A
translational profiling approach for the molecular characterization of CNS
cell types. Cell (2008) 135:738–48. doi:10.1016/j.cell.2008.10.028
310. Sanz E, Yang L, Su T, Morris DR, McKnight GS, Amieux PS. Cell-typespecific isolation of ribosome-associated mRNA from complex tissues. Proc
Natl Acad Sci U S A (2009) 106:13939–44. doi:10.1073/pnas.0907143106
311. Feldman JF. Lengthening the period of a biological clock in Euglena by
cycloheximide, an inhibitor of protein synthesis. Proc Natl Acad Sci U S A(1967) 57:1080–7. doi:10.1073/pnas.57.4.1080

17February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

312. Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS. Genome-wide
analysis in vivo of translation with nucleotide resolution using ribosome
profiling. Science (2009) 324:218–23. doi:10.1126/science.1168978
313. Jang C, Lahens NF, Hogenesch JB, Sehgal A. Ribosome profiling reveals
an important role for translational control in circadian gene expression.
Genome Res (2015) 25:1836–47. doi:10.1101/gr.191296.115
314. Janich P, Arpat AB, Castelo-Szekely V, Lopes M, Gatfield D. Ribosome
profiling reveals the rhythmic liver translatome and circadian clock regulation by upstream open reading frames. Genome Res (2015) 25:1848–59.
doi:10.1101/gr.195404.115
315. Jouffe C, Cretenet G, Symul L, Martin E, Atger F, Naef F, et al. The circadianclock coordinates ribosome biogenesis. PLoS Biol (2013) 11:e1001455.
doi:10.1371/journal.pbio.1001455
316. Fishman B, Wurtman RJ, Munro HN. Daily rhythms in hepatic polysome
profiles and tyrosine transaminase activity: role of dietary protein. Proc Natl
Acad Sci U S A (1969) 64:677–82. doi:10.1073/pnas.64.2.677
317. Uchiyama Y, Asari A. A morphometric study of the variations in subcellular structures of rat hepatocytes during 24 hours. Cell Tissue Res (1984)
236:305–15. doi:10.1007/BF00214231
318. Cao R, Li A, Cho H, Lee B, Obrietan K. Mammalian target of rapamycin
signaling modulates photic entrainment of the suprachiasmatic circadianclock. J Neurosci (2010) 30:6302–14. doi:10.1523/JNEUROSCI.5482-09.2010
319. Cao R, Robinson B, Xu H, Gkogkas C, Khoutorsky A, Alain T, et al.
Translational control of entrainment and synchrony of the suprachiasmatic
circadian clock by mTOR/4E-BP1 signaling. Neuron (2013) 79:712–24.
doi:10.1016/j.neuron.2013.06.026
320. Cornu M, Oppliger W, Albert V, Robitaille AM, Trapani F, Quagliata L, et al.
Hepatic mTORC1 controls locomotor activity, body temperature, and lipid
metabolism through FGF21. Proc Natl Acad Sci U S A (2014) 111:11592–9.
doi:10.1073/pnas.1412047111
321. Atger F, Gobet C, Marquis J, Martin E, Wang J, Weger B, et al. Circadianand feeding rhythms differentially affect rhythmic mRNA transcription andtranslation in mouse liver. Proc Natl Acad Sci U S A (2015) 112:E6579–88.
doi:10.1073/pnas.1515308112
322. Damiola F, Le Minh N, Preitner N, Kornmann B, Fleury-Olela F, Schibler U.
Restricted feeding uncouples circadian oscillators in peripheral tissues fromthe central pacemaker in the suprachiasmatic nucleus. Genes Dev (2000)
14:2950–61. doi:10.1101/gad.183500
323. Hara R, Wan K, Wakamatsu H, Aida R, Moriya T, Akiyama M, et al.
Restricted feeding entrains liver clock without participation of the suprachiasmatic nucleus. Genes Cells (2001) 6:269–78. doi:10.1046/j.1365-2443.2001.
00419.x324. Izumo M, Pejchal M, Schook AC, Lange RP, Walisser JA, Sato TR, et al.
Differential effects of light and feeding on circadian organization of
peripheral clocks in a forebrain Bmal1 mutant. Elife (2014) 3. doi:10.7554/
eLife.04617
325. Sabath E, Salgado-Delgado R, Guerrero-Vargas NN, Guzman-Ruiz MA, delCarmen Basualdo M, Escobar C, et al. Food entrains clock genes but notmetabolic genes in the liver of suprachiasmatic nucleus lesioned rats. FEBS
Lett (2014) 588:3104–10. doi:10.1016/j.febslet.2014.06.045
326. Stokkan KA, Yamazaki S, Tei H, Sakaki Y, Menaker M. Entrainment of thecircadian clock in the liver by feeding. Science (2001) 291:490–3. doi:10.1126/
science.291.5503.490
327. Barbosa C, Peixeiro I, Romão L. Gene expression regulation by upstream
open reading frames and human disease. PLoS Genet (2013) 9:e1003529.
doi:10.1371/journal.pgen.1003529
328. Schleich S, Strassburger K, Janiesch PC, Koledachkina T, Miller KK, Haneke
K, et al. DENR-MCT-1 promotes translation re-initiation downstream ofuORFs to control tissue growth. Nature (2014) 512:208–12. doi:10.1038/
nature13401
329. Skabkin MA, Skabkina OV, Hellen CUT, Pestova TV. Reinitiation and otherunconventional post-termination events during eukaryotic translation. Mol
Cell (2013) 51:249–64. doi:10.1016/j.molcel.2013.05.026
330. Harrison DA. Sex determination: controlling the master. Curr Biol (2007)
17:R328–30. doi:10.1016/j.cub.2007.03.012
331. Salz HK, Erickson JW. Sex determination in Drosophila: the view from thetop. Fly (Austin) (2010) 4:60–70. doi:10.4161/fly.4.1.11277
332. Wilhelm JE, Smibert CA. Mechanisms of translational regulation in
Drosophila. Biol Cell (2005) 97:235–52. doi:10.1042/BC20040097

Frontiers in Neurology | www.frontiersin.org

333. Medenbach J, Seiler M, Hentze MW. Translational control via protein-regulated upstream open reading frames. Cell (2011) 145:902–13. doi:10.1016/j.
cell.2011.05.005
334. Tanenbaum ME, Stern-Ginossar N, Weissman JS, Vale RD. Regulationof mRNA translation during mitosis. Elife (2015) 4. doi:10.7554/eLife.
07957
335. Yan X, Hoek TA, Vale RD, Tanenbaum ME. Dynamics of translation of
single mRNA molecules in vivo. Cell (2016) 165:976–89. doi:10.1016/j.
cell.2016.04.034
336. Xu Y, Ma P, Shah P, Rokas A, Liu Y, Johnson CH. Non-optimal codon usage
is a mechanism to achieve circadian clock conditionality. Nature (2013)
495:116–20. doi:10.1038/nature11942
337. Zhou M, Guo J, Cha J, Chae M, Chen S, Barral JM, et al. Non-optimal
codon usage affects expression, structure and function of clock protein FRQ.
Nature (2013) 495:111–5. doi:10.1038/nature11833
338. Fu J, Murphy KA, Zhou M, Li YH, Lam VH, Tabuloc CA, et al. Codon
usage affects the structure and function of the Drosophila circadian clock
protein PERIOD. Genes Dev (2016) 30:1761–75. doi:10.1101/gad.281030.116
339. Lim C, Allada R. ATAXIN-2 activates PERIOD translation to sustain
circadian rhythms in Drosophila. Science (2013) 340:875–9. doi:10.1126/
science.1234785
340. Zhang Y, Ling J, Yuan C, Dubruille R, Emery P. A role for Drosophila
ATX2 in activation of PER translation and circadian behavior. Science (2013)
340:879–82. doi:10.1126/science.1234746
341. Tuin I, Voss U, Kang J-S, Kessler K, Rüb U, Nolte D, et al. Stages of sleep
pathology in spinocerebellar ataxia type 2 (SCA2). Neurology (2006)
67:1966–72. doi:10.1212/01.wnl.0000247054.90322.14
342. Velázquez-Pérez L, Voss U, Rodríguez-Labrada R, Auburger G, Canales
Ochoa N, Sánchez Cruz G, et al. Sleep disorders in spinocerebellar
ataxia type 2 patients. Neurodegener Dis (2011) 8:447–54. doi:10.1159/
000324374
343. Funato H, Miyoshi C, Fujiyama T, Kanda T, Sato M, Wang Z, et al. Forwardgenetics analysis of sleep in randomly mutagenized mice. Nature (2016)
539(7629):378–83. doi:10.1038/nature20142
344. Sung YH, Baek I-J, Kim DH, Jeon J, Lee J, Lee K, et al. Knockout micecreated by TALEN-mediated gene targeting. Nat Biotechnol (2013) 31:23–4.
doi:10.1038/nbt.2477
345. Carbery ID, Ji D, Harrington A, Brown V, Weinstein EJ, Liaw L, et al. Targeted
genome modification in mice using zinc-finger nucleases. Genetics (2010)
186:451–9. doi:10.1534/genetics.110.117002
346. Hsu PD, Lander ES, Zhang F. Development and applications of CRISPRCas9 for genome engineering. Cell (2014) 157:1262–78. doi:10.1016/j.
cell.2014.05.010
347. Wang H, Yang H, Shivalila CS, Dawlaty MM, Cheng AW, Zhang F, et al. Onestep generation of mice carrying mutations in multiple genes by CRISPR/
Cas-mediated genome engineering. Cell (2013) 153:910–8. doi:10.1016/j.
cell.2013.04.025
348. East-Seletsky A, O’Connell MR, Knight SC, Burstein D, Cate JHD, Tjian
R, et al. Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA
processing and RNA detection. Nature (2016) 538:270–3. doi:10.1038/
nature19802
349. Fonfara I, Richter H, Bratovič M, Le Rhun A, Charpentier E. The CRISPRassociated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR
RNA. Nature (2016) 532:517–21. doi:10.1038/nature17945
350. Fu Y, Sander JD, Reyon D, Cascio VM, Joung JK. Improving CRISPR-Cas
nuclease specificity using truncated guide RNAs. Nat Biotechnol (2014)
32:279–84. doi:10.1038/nbt.2808
351. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome
engineering using the CRISPR-Cas9 system. Nat Protoc (2013) 8:2281–308.
doi:10.1038/nprot.2013.143
352. Ranganathan V, Wahlin K, Maruotti J, Zack DJ. Expansion of the CRISPRCas9 genome targeting space through the use of H1 promoter-expressed
guide RNAs. Nat Commun (2014) 5:4516. doi:10.1038/ncomms5516
353. Zetsche B, Gootenberg JS, Abudayyeh OO, Slaymaker IM, Makarova KS,
Essletzbichler P, et al. Cpf1 is a single RNA-guided endonuclease of a class
2 CRISPR-Cas system. Cell (2015) 163:759–71. doi:10.1016/j.cell.2015.
09.038354. Sunagawa GA, Sumiyama K, Ukai-Tadenuma M, Perrin D, Fujishima
H, Ukai H, et al. Mammalian reverse genetics without crossing reveals

18

February 2017 | Volume 8 | Article 25Millius and Ueda

Systems Contributions to Circadian Biology

Nr3a as a short-sleeper gene. Cell Rep (2016) 14:662–77. doi:10.1016/j.
celrep.2015.12.052
355. Tatsuki F, Sunagawa GA, Shi S, Susaki EA, Yukinaga H, Perrin D, et al.
Involvement of Ca(2+)-dependent hyperpolarization in sleep duration in
mammals. Neuron (2016) 90:70–85. doi:10.1016/j.neuron.2016.02.032
356. Tainaka K, Kuno A, Kubota SI, Murakami T, Ueda HR. Chemical principles intissue clearing and staining protocols for whole-body cell profiling. Annu Rev
Cell Dev Biol (2016) 32:713–41. doi:10.1146/annurev-cellbio-111315-125001
357. Chung K, Deisseroth K. CLARITY for mapping the nervous system. Nat
Methods (2013) 10:508–13. doi:10.1038/nmeth.2481
358. Dodt H-U, Leischner U, Schierloh A, Jährling N, Mauch CP, Deininger K,
et al. Ultramicroscopy: three-dimensional visualization of neuronal networks
in the whole mouse brain. Nat Methods (2007) 4:331–6. doi:10.1038/
nmeth1036
359. Ertürk A, Becker K, Jährling N, Mauch CP, Hojer CD, Egen JG, et al. Threedimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc(2012) 7:1983–95. doi:10.1038/nprot.2012.119
360. Pan C, Cai R, Quacquarelli FP, Ghasemigharagoz A, Lourbopoulos A,
Matryba P, et al. Shrinkage-mediated imaging of entire organs and organisms
using uDISCO. Nat Methods (2016) 13:859–67. doi:10.1038/nmeth.3964
361. Susaki EA, Tainaka K, Perrin D, Kishino F, Tawara T, Watanabe TM,
et al. Whole-brain imaging with single-cell resolution using chemical
cocktails and computational analysis. Cell (2014) 157:726–39. doi:10.1016/j.
cell.2014.03.042

Frontiers in Neurology | www.frontiersin.org

362. Yang B, Treweek JB, Kulkarni RP, Deverman BE, Chen C-K, Lubeck E, et al.
Single-cell phenotyping within transparent intact tissue through whole-bodyclearing. Cell (2014) 158:945–58. doi:10.1016/j.cell.2014.07.017
363. Hughes ME, Hogenesch JB, Kornacker K. JTK_CYCLE: an efficientnonparametric algorithm for detecting rhythmic components in genomescale data sets. J Biol Rhythms (2010) 25:372–80. doi:10.1177/0748730
410379711
364. Yang R, Su Z. Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation. Bioinformatics (2010)
26:i168–74. doi:10.1093/bioinformatics/btq189
365. Kojima S, Green CB. Circadian genomics reveal a role for post-transcriptional regulation in mammals. Biochemistry (2015) 54:124–33. doi:10.1021/
bi500707c
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.
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