Analysis and synthesis of high-amplitude Cis-elementsin the mammalian circadian clock
Yuichi Kumaki*†‡, Maki Ukai-Tadenuma*, Ken-ichiro D. Uno§, Junko Nishio§, Koh-hei Masumoto*¶, Mamoru Nagano¶,
Takashi Komori†, Yasufumi Shigeyoshi¶, John B. Hogenesch储, and Hiroki R. Ueda*§***Laboratory for Systems Biology and §Functional Genomics Unit, Center for Developmental Biology, RIKEN, 2-2-3 Minatojima-Minamimachi, Chuo-ku,
Kobe 650-0047, Japan; †INTEC Systems Institute, Inc., 1-3-3 Shinsuna, Koto-ku, Tokyo 136-0075, Japan; ¶Department of Anatomy and Neurobiology,
Kinki University School of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka 589-8511, Japan; and 储Institute for Translational Medicine
and Therapeutics and the Department of Pharmacology, University of Pennsylvania School of Medicine, 810 Biomedical Research Building II/III,
421 Curie Boulevard, Philadelphia, PA 19104-6160
Edited by Joseph S. Takahashi, Northwestern University, Evanston, IL, and approved August 15, 2008 (received for review March 16, 2008)

Mammalian circadian clocks consist of regulatory loops mediatedby Clock/Bmal1-binding elements, DBP/E4BP4 binding elements,
and RevErbA/ROR binding elements. As a step toward system-level
understanding of the dynamic transcriptional regulation of theoscillator, we constructed and used a mammalian promoter/enhancer database (http://promoter.cdb.riken.jp/) with computational models of the Clock/Bmal1-binding elements, DBP/E4BP4
binding elements, and RevErbA/ROR binding elements to predict
new targets of the clock and subsequently validated these targets
at the level of the cell and organism. We further demonstrated thepredictive nature of these models by generating and testing
synthetic regulatory elements that do not occur in nature andshowed that these elements produced high-amplitude circadiangene regulation. Biochemical experiments to characterize thesesynthetic elements revealed the importance of the affinity balance
between transactivators and transrepressors in generating highamplitude circadian transcriptional output. These results highlight thepower of comparative genomics approaches for system-level identification and knowledge-based design of dynamic regulatory circuits.
comparative genomics 兩 promoter and enhancer database 兩synthetic biology 兩 systems biology 兩 transcriptionT

he rapidly expanding number of sequenced mammalian
genomes (1–3), annotated and cloned full-length cDNAs(4–6), transcriptional starts sites (TSSs) (7–9) and transcription
factor binding sites (TFBSs) (10–12) has provided new opportunities to unravel the control of dynamic transcriptional programs. Comparative genomics approaches applying these resources have been used to identify target genes of specificbiological pathways. These efforts used consensus sequence
searches (13, 14), positional weight matrices (15), hidden
Markov models (HMMs) (16). and specifically tailored algorithms (17, 18) to define candidate response elements and target
genes in raw genomic sequence. Additionally, post hoc analysis
employing evolutionary conservation (15, 16, 18) together withpositional information of TSSs (15) and/or translational startsites (16) has helped to further define candidate elements andgenes and greatly expanded our knowledge of transcriptional
output regulation.
The mammalian circadian clock is an ideal system to apply
these tools as it consists of integrated transcriptional regulatory
loops that direct output through at least three types of transcriptional regulatory elements, the Clock/Bmal1-binding elements (E-box) (CACGTG) (19–21), DBP/E4BP4 binding elements (D-box) (TTATG[T/C]AA) (21–23), and RevErbA/ROR
binding elements (RRE) ([A/T]A[A/T]NT[A/G]GGTCA) (15,21, 24, 25). Several groups, including our own, have shown thatapproximately 5–10% of mammalian genes display circadian
expression in central and peripheral clock tissues (26). However,for the most part, the transcriptional regulation of these thousands of clock-controlled genes has remained uncharacterized.
We and others have used comparative genomics approaches to14946 –14951 兩 PNAS 兩 September 30, 2008 兩 vol. 105 兩 no. 39

analyze E-box (21, 27, 28), D-box (21), and RRE (15, 21),highlighting the importance of both their core consensus andflanking sequences (15, 21, 27, 28) in circadian gene control. In
this study, we further extend comparative genomics approaches
toward a system-level understanding of the dynamic transcriptional regulations of the mammalian circadian clock.
Results and Discussion
Prediction of Direct Clock Targets Through Utilization of the Mammalian Promoter/Enhancer Database. To generate a resource thatfacilitates identification of clock-controlled genes, we constructed a mammalian promoter/enhancer database (http://
promoter.cdb.riken.jp/) by integrating information sources suchas conserved non-coding regions, TSSs and TFBSs [supporting
information (SI) Fig. S1 and SI Appendix]. Although excellent
and similar databases exist such as DBTSS (8), CisView (29) and
ECRbase (30), none were tailored to specifically identify clock
gene targets and having local control of the database facilitatedmanipulation of the underlying data (see also SI Appendix). We
then developed a comparative genomics strategy employing thisdatabase and profile HMMs using the HMMER software package (31). Profile HMMs are powerful tools to extract thestatistical properties of input sequences by representing multiple
sequences as a transition probability matrix marching from one
position to the neighboring position. HMMs were built andcalibrated on known functional clock-controlled elements experimentally verified in our previous (15, 21) or current studies(Fig. S2 and Table S1), consisting of 12 E-boxes, 10 D-boxes and
15 RREs (Table S2). Profile HMM searches to identify newclock-controlled elements from conserved non-coding regions
between human and mouse identified 1,108 E-boxes, 2,314
D-boxes, and 3,288 RREs candidate elements (see the circadiansection of the mammalian promoter/enhancer database: http://
promoter.cdb.riken.jp/circadian.html for element lists). To set
appropriate reporting thresholds, we used the match scores ofknown functional clock-controlled elements (Material and Methods). Predicted clock-controlled elements exhibited an un-biased
distribution of chromosomal position spread over whole mouse
genome (Fig. 1A and http://promoter.cdb.riken.jp/circadian.html).
Author contributions: J.B.H. and H.R.U. designed research; Y.K., M.U., K.D.U., J.N., K.M.,
M.N., and Y.S. performed research; Y.K. and T.K. contributed new analytic tools; Y.K.
analyzed data; and Y.K., J.B.H., and H.R.U. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
‡Present address: Laboratory for Mammalian Epigenetic Studies, Center for Developmental

Biology, RIKEN, 2-2-3 Minatojima-Minamimachi, Chuo-ku, Kobe 650-0047, Japan.
**To whom correspondence should be addressed. E-mail: uedah-tky@umin.ac.jp.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0802636105/DCSupplemental.
© 2008 by The National Academy of Sciences of the USA

www.pnas.org兾cgi兾doi兾10.1073兾pnas.0802636105

Conserved element
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Fig. 1. Computational prediction of clock-controlled elements using HMMs.
(A) Chromosomal distributions of predicted conserved clock-controlled elements of conserved non-coding regions mapped on the mouse genome.
Chromosomal positions of the 100 most significant hits for E-boxes, D-boxes,
and RREs are shown (red). (B) Plots of false discovery rates (FDRs) against matchscores of HMM searches in three conditions: (1) searches for conserved elements within conserved non-coding regions (red, ‘‘Conserved element’’), (2)searches for mouse elements within conserved or non-conserved non-coding
regions (blue, ‘‘Non-coding region’’) and (3) searches in the entire genome
relaxing both element conservation and search space (orange, ‘‘Whole genome’’). FDRs in ‘‘Conserved elements’’ search are plotted against the average
match score of human and mouse elements. (C) The distributions of distancefrom transcriptional starts sites (TSSs) for predicted conserved clock-controlledelements of conserved non-coding regions (1,108 E-boxes, 2,314 D-boxes, and3,288 RREs). The E-box displays a biased distribution of distance from TSSs,
while the D-box and RRE show unbiased, near random distributions (‘‘Random
E-box,’’ ‘‘Random D-box,’’ and ‘‘Random RRE,’’ see also SI Appendix). (D) The
average expression of transcripts harboring each element exhibit circadianrhythms in the liver. The average expressions of 36 genes with E-boxes, 29
genes with D-boxes, and 34 genes with RREs exhibited significant circadianoscillations (P ⫽ 0.01 for E-box, P ⫽ 0.0005 for D-box, and P ⫽ 0.001 for RRE).
Data were normalized so that the average signal intensity over 12-point timecourses is 1.0. Estimated peak times of circadian oscillation were also
indicated.

However, the match score on its own does not give a good
estimate of accuracy and false discovery from HMM searches.
To better estimate the prediction for each HMM, we searchedeach against randomized genome sequence to generate a background distribution of false positive occurrences (see SI Appendix for detail). We found that the value of the false discovery rate(FDR) is inversely proportional to the match score of the HMM,
Kumaki et al.

which is a representation of the statistical significance of thecandidate element (Fig. 1B). Importantly, we found the accuracyof the HMM-based prediction as measured by the FDR isdependent on the initial search conditions. HMM searches inconserved non-coding regions (the original condition) had thelowest FDR, while higher rates were observed in conserved or
non-conserved non-coding regions, or in searches of raw genome
sequence (Fig. 1B). These results demonstrate the value of using
human/mouse conservation and a confined search space forgeneration of the most accurate response element predictions.
Several intriguing features resulted from this analysis. Interestingly, like cAMP-responsive elements (CREs) (16), putative E-box
displayed a biased distance distribution from TSSs, while putative
D-box and RRE had random localization distributions in the
genome and were not more likely to be near TSSs (Fig. 1C). This
result is consistent with and extends an earlier report that described
the preferential localization of CpG containing transcription factor
binding sites including the E-box and CRE to proximal promoter
regions of housekeeping genes (32). In addition to housekeeping
genes, we see circadian E-box sequences present in many genes withspecific functions such as enzymes and signaling molecules.
Gratifyingly, we noted a significant enrichment of previously
identified clock-controlled genes (15, 33) in our predicted clockcontrolled elements (the 100 most significant sequences for each
HMM search, E-box, D-box, and RRE, respectively). Afterremoving the 21 clock-controlled genes used for HMM generation and training, we found an additional 19 putative clockcontrolled genes (out of the 6,195 genes common in our mammalian promoter/enhancer database and U74 mousemicroarray) versus the expected 10.67 that would have arisen
from chance, a significant enrichment of clock-controlled genes(P ⬍ 0.01, see also SI Appendix).
The presence of statistically significant clock-controlled elements in the promoters of these genes suggests that their message
levels may oscillate. To examine this possibility, we selected the
100 most significant putative clock-controlled elements for eachmodel (E-box, D-box, and RRE) and determined their gene
expression levels from previously obtained liver data (15). The
average expression of 36 E-box containing genes, 29 D-box
genes, and 34 RRE genes exhibited significant circadian oscillations (P ⫽ 0.01 for E-box, P ⫽ 0.0005 for D-box, and P ⫽ 0.001
for RRE) with a surprisingly consistent peak time of expression(E-box ⫽ 8.8, D-box ⫽ 10.8, and RRE ⫽ 13.6, Fig. 1D). Taken
in sum, these data show that our HMM models and strategy
identifies elements and genes that oscillate with a circadianperiod with peak phases of expression that are consistent withthe previously reported literature.
In Vitro Validation of Putative Clock Controlled Genes. To provide

further validation of these predictions, we used an in vitro systemof the autonomous circadian clock to empirically test candidate
elements in circadian transcriptional output assays. In brief, weused a cell culture system (15, 34) that allowed the monitoringof circadian transcriptional dynamics using a destabilized luciferase reporter (dLuc) driven by known or putative clockcontrolled response elements (Fig. 2A). We selected the ten mostsignificant sequences for each HMM search, E-box, D-box, and
RRE respectively, and located within 1kb of the TSS (Table S3).
We constructed reporter vectors in which three predicted elements were inserted in front of the SV40 basic promoter driving
a dLuc reporter (see Material and Methods). We transfected
these constructs into cultured NIH 3T3 fibroblasts, stimulated
them with forskolin to synchronize circadian rhythms of individual cells, and measured the sum of their transcriptional
activity by monitoring bioluminescence over several days; 40% of
putative E-boxes, 70% of D-boxes, and 60% of RREs generated
strong circadian reporter gene activity (P ⬍ 0.01 and highamplitude) in phase with the Per1 E-box, Per3 D-box, and Arntl
PNAS 兩 September 30, 2008 兩 vol. 105 兩 no. 39 兩 14947

GENETICS

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Time (h)Fig. 2. Experimental validation of HMM-based predictions at cellular andorganismal levels. (A) Circadian rhythms of bioluminescence from the predicted clock-controlled elements fused to the SV40 basic promoter driving adLuc reporter in NIH 3T3 fibroblasts. Three known clock-controlled elementsfrom clock genes (E-box of Per1, D-box of Per3, and RRE of Arntl) are used as
positive controls. The bioluminescence data were detrended in baseline andamplitude and normalized so that their maximum, minimum, and average
were set to 1, ⫺1, and 0, respectively. The colors in descending order frommagenta to black to green represent the detrended bioluminescence. Columns represent time points and rows represent the predicted elements on thedesignated genes. (B) Circadian rhythms of temporal mRNA expression profiles of the predicted clock-controlled genes in mouse seven tissues (‘A’, ‘B’, ‘H’,
‘K’, ‘Li’, ‘Lu’ and ‘M’ for aorta, bone, heart, kidney, liver, lung, and muscle,
respectively). An estimated peak time with color of type of predicted clockcontrolled element (green, red, and blue for E-box, D-box, and RRE, respectively) is also indicated. The colors in descending order from magenta to black
to green represent the normalized data (the average and standard deviation
over 12-point time courses are 0.0 and 1.0, respectively). Columns represent
time points, and rows represent the predicted clock-controlled genes in thedesignated tissues.

RRE, respectively (Fig. 2 A and Fig. S3 A–D). The remaining
sequences generated weak, low amplitude circadian transcriptional activity or were arrhythmic (Fig. S3 E–G). To supplement
our observed 40% prediction success, we constructed 14 reporters containing conserved low-scoring E-boxes and found only
one exhibited high-amplitude oscillations (Fig. S3H and Table
S3). This result indicates that our observed 40% prediction
success is suggestively higher than expected (P ⫽ 0.075, Fisher’s
exact test). Taken in sum, these results demonstrate utility of thisapproach in finding elements within structural genes that dictate
rhythmic transcription.
If these in vitro validated 17 elements (4 E-boxes, 7 D-boxes,
and 6 RREs) play a prominent role in gene regulation in vivo, we
would predict that the endogenous transcripts for these genes
would likely oscillate in a circadian fashion. To test this, we
harvested mRNA from seven tissues (aorta, bone, heart, kidney,
liver, lung, and muscle) isolated from mice entrained to a 12:12
light:dark cycle and then released to free run in constantdarkness. Using quantitative PCR assays, we measured expression profiles from our predicted clock-controlled elements, andevaluated their rhythmicity using a statistical method based on
analysis of variance (ANOVA) followed by curve fitting to acosine wave. These experiments revealed that circadian expression profiles (P ⬍ 0.03) for 13 genes (76%): 3 E-box controlledgenes, 4 D-box controlled genes and 6 RRE controlled genes,
respectively, with a consistent order of peak time (4.1, 15.5, and18.8 for mean value of the peak time of putative E-box, D-box,
and RRE-controlled genes, respectively) (Fig. 2B; See also
14948 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0802636105

http://promoter.cdb.riken.jp/circadian.html for detailed data).
For those genes that did not confirm circadian rhythmicity, theaverage level of expression was lower, implying mRNA detection
was limiting for these genes. Collectively, these in vitro and in vivo
experiments suggest that many predicted E-box, RRE, andD-box containing genes are bona fide first-order clock-controlledgenes.
Design and Validation of the Synthetic Regulatory Elements. One of

the goals of systems biology is the synthesis of knowledge and thegeneration of testable (and tested) hypotheses. We reasoned that
if our HMMs truly represented the functional response elementsof these three transcription factor complexes, then synthetic
regulatory elements derived from these models should mediaterhythmic transcription as well. To test this idea, we emitted
sequences from the E-box, D-box, and RRE models, respectively, and filtered out those that naturally exist in either thehuman or mouse genomes. Furthermore, to not unduly focus ourattention on outliers, we required that all candidates adhere to
the consensus rules for each element, CACGTG for the E-box(19), TTATGTAA for the D-box (22), and [A/T]A[A/T]NT[A/G]GGTCA for the RRE (24). For the remaining sequences, wechose each one of the highest and lowest scoring synthetic
representatives for three types of elements and named them‘‘high-scoring’’ and ‘‘low-scoring’’ elements, respectively (Fig.
3A). We tested these elements in a synthetic reporter system as
above (Fig. 3B). All three ‘‘high-scoring’’ elements showed
high-amplitude circadian transcriptional activity equivalent toknown elements from canonical clock genes (E-box of Per1,
D-box of Per3, and RRE of Arntl are used as 1.0, respectively)(21) (Fig. 3C). On the other hand, the ‘‘low-scoring’’ elements
emitted from the HMMs showed very low- amplitude transcriptional activity, despite the presence of ‘‘consensus’’ E-box, RRE,
or D-box core sequences (Fig. 3C). These results show the utilityof this comparative genomics approach in synthetic design ofdynamic cis-acting elements, as well as highlight the contributionof flanking sequences in generating high-amplitude rhythmicity.
Investigating the Contribution of Flanking Sequences. Using thesesynthetic elements, we next attempted to explore the contributionof E-box flanking regions to identify critical residues that modulate
amplitude and rhythmicity. We clustered their nucleotide sequences, and, interestingly, found two patterns of high-amplitude
E-box flanking sequences adjacent to the core CACGTG element(Fig. S4). However, these positions do not absolutely dictate
high-amplitude rhythmicity, as some elements that meet these rules
exhibit lower-amplitude oscillations, possibly because they exhibit
much higher GC content. In either case, these experimental results
also imply that amplitude information is encoded in specific residues adjacent to the core consensus element and further strengthen
the previous reports by other groups on the importance of flanking
sequence of E-box (27, 28, 35, 36). Interestingly, the identified
patterns in this study partly overlap with the computational models
based on the evolutionarily conserved E-box structure from insects
to mammals (27).
High-Amplitude Oscillations Require Appropriate Affinity Balance
Between Activators and Repressors. To explore the properties ofthese elements that result in high amplitude oscillations, we took
a simplified molecular modeling and experimental approach.
First, we assumed concentrations of activators and repressors
were within similar ranges (see also SI Appendix Discussion in
more general cases). We further hypothesized that flanking
region DNA sequence impacted DNA-binding affinity of clock
gene regulators and therefore altered amplitude. We further
hypothesized that tightly binding sequences would have higher
amplitudes of circadian oscillation. To test this notion, we
analyzed the DNA-binding affinity of activators and repressors
Kumaki et al.

A

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Fig. 3. Computational design and experimental characterization of highamplitude circadian transcriptional activity. (A) HMM-based design of thehigh- and low-scoring elements. Match scores of synthetic elements are 19.84(high-scoring E-box), ⫺1.36 (low-scoring E-box), 22.17 (high-scoring D-box),⫺4.33 (low-scoring D-box), 24.45 (high-scoring RRE) and ⫺0.46 (low-scoring
RRE), respectively. All elements were filtered to identify those that do not exist
in either the human or mouse genomes. (B) Bioluminescence from synthetic
elements inserted into the SV40 basic promoter driving a dLuc reporter(SV40-dLuc) in NIH 3T3 fibroblasts. SV40-dLuc containing known clockcontrolled elements (E-box of Per1, D-box of Per3, or RRE of Arntl) andSV40-dLuc with no insert are the positive and negative controls, respectively.
The negative control of the D-box element was also used for the RRE as
experiments for these elements were performed at the same time. (C) The
amplitude of bioluminescence activity driven by synthetic elements relative tothat of positive controls. The relative amplitude of positive controls is 1.0. Error
bars indicate SEM determined from independent experimental duplicates.

to these response elements using competitive binding assays (Fig.
4A and Fig. S5A). For the D-box and RRE elements, ‘‘highscoring’’ elements showed approximately the same DNAbinding affinity for their activators and repressors, while ‘‘lowscoring’’ elements of D-box and RRE showed relatively weakaffinity, confirming this hypothesis. Surprisingly, in the case of
E-box, ‘‘low-scoring’’ sequences had a higher affinity for the
Arntl/Clock activator complex (4.8 times higher than positive
control; Fig. S5A) than ‘‘high-scoring’’ sequences or the positive
control, whereas the ‘‘low-scoring’’ E-box sequence showedapproximately the same affinity to the Bhlhb2 repressor.
To assist in interpreting these results, we used in silico
modeling (Fig. 4 B and C) and treated affinity of activators andrepressors as parameters and amplitude as the output of themodel. Interestingly, this analysis showed that a high affinity
activator complex coupled with a normal affinity repressor
complex capitulated lower amplitude rhythms (Fig. 4B), sugKumaki et al.

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Fig. 4. Defining the relationship between affinity and amplitude. (A) Binding affinity between synthetic elements and DNA binding activators or repressors was detected by competitive DNA binding assays. The binding between labeled oligonucleotides of positive control elements (10 pmol) andtranscription factors were competed by each of unlabeled oligonucleotides (0,1, 3, 10, 30, and 100 pmol) for positive control elements (blue), high-scoring
elements (red), low-scoring elements (green), or negative control elements(black). Known clock-controlled elements (E-box of Per1, D-box of Per3, or RRE
of Arntl) and mutated clock-controlled elements are the positive and negative
controls, respectively. Arntl/Clock, Dbp, Rora were used as DNA binding
activator of E-box, D-box, and RRE, respectively. Bhlhb2, Nfil3, Nr1d1, wereused as DNA binding repressors of E-box, D-box, and RRE, respectively. The
relative signal without competitor is 1.0. Error bars indicate SEM determinedfrom independent experimental duplicates. (B and C) In silico analysis ofaffinity to amplitude mechanism. Gene expression of idealized transcriptional
activators (blue dotted line) and repressors (gray dotted line) and the normalized output of different strengths of activator binding affinity (weakeraffinity 1/Ka ⫽ 4 is red line and stronger affinity 1/Ka ⫽ 20 is green line) areindicated (B). The relative amplitude of oscillation of the output plottedagainst the strength of activator binding affinity (C). Amplitude was normalized so that the maximum value is set to 100%.

gesting that the enhanced retention of an activator alone promotes its saturation on a promoter and consequently dampens
amplitude in competition-based models. Further, in silico analysis showed not only affinity strength of the activator andrepressor (Fig. S5B) but also the appropriate affinity balance ofactivators and repressors is necessary for high-amplitude circadian oscillations (Fig. 4C; See also SI Appendix Discussions forin silico modeling).
Supporting this notion, a new clock gene, clockwork orange(cwo, a fly ortholog of mammalian Bhlhb2 and Bhlhb3) was
recently reported to directly suppress gene expression of several
clock genes through E-box elements (37–39). Quantitative andqualitative impairment of cwo revealed an important role of thistranscriptional repressor for high-amplitude oscillation of the
Drosophila circadian clock. The findings in this report, along withthe studies of the cwo gene, collectively show that a competitivebalance between direct activator(s) and direct repressor(s) for
the E-box element is important for driving high-amplitude
oscillations of circadian output genes. In addition to this in vivo
biological evidence in the fly, we listed the evolutionary conserved ‘‘low-scoring’’ E-boxes in the mammalian genome (predicted low-amplitude) as candidates for unbalanced affinities.
Interestingly, this list includes E-boxes on core clock genes (Cry2,
Bhlhb3, Nr1d1, and Rorc) and some low-amplitude clockcontrolled genes such as Id2, and to promote follow-up, isavailable at http://promoter.cdb.riken.jp/circadian.html.
What could explain the discrepancy between E-box motifs and
PNAS 兩 September 30, 2008 兩 vol. 105 兩 no. 39 兩 14949

GENETICS

D-box

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Directactivator

E-box

A

other circadian response elements? We hypothesized that thesedifferences might be encoded at the protein sequence level of the
DNA-binding domains of activators and repressors. Interestingly, the DNA binding domains of transactivators and transrepressors of the RRE and D-box are more similar to each other(65% identity and 81% homology for RRE regulators, and 44%
identity and 69% homology for D-box regulators) than those of
E-box transactivators and transrepressor (22⬃30% identity and55⬃57% homology) (Table S4). Based on these findings, we
speculate that the DNA-binding domains for transactivators andtransrepressors of the RRE and D-box have evolved similaraffinities. In contrast, the evolutionarily and structurally divergent regulators of E-boxes, 108 bHLH proteins including several
families of activators and repressors, as well as the unrelated period
and cryptochrome gene families, may have required the co-evolutionof specific DNA-binding domains and E-box sequences with specific flanking regions to generate higher amplitude rhythmicity.
Conclusion
In summary, we have applied a comparative genomics strategy
to the understanding of a dynamic transcriptional regulatory
system, the mammalian circadian clock. Our informatics strategy
employs a model-based search with excellent statistical properties, the evolutionary conservation of putative transcriptional
regulatory elements across mouse and human non-coding regions, and statistical evaluation of false discovery rates in eachprediction. Experimental validation of this strategy in vitro andin vivo using real-time monitoring of transcriptional activity andquantitative PCR assay has led to the identification of dozens ofnovel clock-controlled genes and the elements that likely dictate
their rhythmicity. High-scoring conserved E-boxes (meanHMM-score ⫽ 16.15) had a 40% rate of validation, while
low-scoring conserved E-boxes (mean HMM-score ⫽ 2.5143)
had a 7.1% probability of generating high-amplitude rhythmicity
in reporter assays. Linear interpolation from these two numbers
generates an estimate of approximately 347 novel conserved
E-boxes that likely confer circadian rhythmicity (see also SI
Appendix). Moreover, to demonstrate their predictive nature, wehave taken these in silico models and designed synthetic elementsthat exhibit high-amplitude transcriptional rhythmicity as well as
the best canonical regulatory elements. Furthermore, experimental measurement and in silico analysis of affinity of regulators to synthetic elements revealed the importance of theappropriate affinity balance between activators and repressors
for high-amplitude rhythmicity. Surprisingly, for E-box sequences, lower affinity DNA element generates higher amplitude rhythms. The experimental, analytical, and synthetic approaches discussed here are especially timely as genomics tools
are increasingly uncovering the complexity and flexibility oftranscriptional regulatory circuits. We predict the generalthemes and resources reported here will enhance understandingof the biology mediated by complex and dynamic transcriptional
regulation including the mammalian circadian clock.
Materials and Methods
Detailed information on the construction of the mammalian promoter/enhancer database, determination of distance from TSSs for natural andrandomly positioned elements, calculation of FDR for putative elements,
animals, genome sequences, oligonucleotide sequences, plasmid constructions, quantitative PCR, rhythmicity analysis of real-time bioluminescencedata, amplitude analysis of real-time bioluminescence data, rhythmicity analysis of quantitative PCR data, over representation analysis of clock-controlledgenes, estimation of the number of high-amplitude E-boxes, microarray expression data analysis of genes with predicted clock-controlled elements,
affinity analysis of competitive DNA binding data, and in silico analysis ofaffinity to amplitude mechanism are available in SI Appendix.
Real-Time Circadian Reporter Assays. Real-time circadian assays were performed as previously described (40) with the following modifications. NIH
14950 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0802636105

3T3 cells (American Type Culture Collection) were grown in DMEM (Invitrogen) supplemented with 10% FBS (JRH Biosciences) and antibiotics (25 unitsml⫺1 penicillin, 25 ␮g ml⫺1 streptomycin; Invitrogen). Cells were plated at
5 ⫻ 104 cells per well in 24-well plates 24 h before transfection. Cells weretransfected with 0.32 ␮g of plasmids in total (0.13 ␮g reporter plasmid and0.19 ␮g empty plasmid) per well using FuGENE6 (Roche Applied Science)
according to the manufacturer’s instructions. After 72 h, medium in eachwell was replaced with 500 ␮l of culture medium (DMEM/10% FBS) supplemented with 10 mM Hepes (pH 7.2), 0.1 mM luciferin (Promega),
antibiotics and 0.01 ␮M forskolin (nacalai tesque). Bioluminescence wasmeasured with photomultiplier tube (PMT) detector assemblies(Hamamatsu Photonics). The modules and cultures were maintained in a
darkroom at 30 °C and interfaced with computers for continuous dataacquisition until 96 h after forskolin stimulation. Photons were counted 2
min at 24-min intervals.
Construction, Search, and Design of Putative cis-Acting Elements. A HMM is astatistical model in which the target system is assumed to be a Markov processwith unknown parameters. A HMM describes a probability distribution over
input training sequences, i.e., probabilities of the state transition and emission. The extracted model can be used to find the probability of query
sequence that is a product of all transition and emission probabilities attraining sequences. Nucleotide sequences for known functional clockcontrolled elements, 12 E-boxes (18 bp), 10 D-boxes (24 bp), and 15 RREs (23bp), experimentally verified in previous (21) and current studies (Table S1 andFig. S2), were used as a training dataset to construct HMMs. We also attempted
to construct an HMM for the E’-box, but were unable (i.e., positive controls
exhibited poor scores) because of the small number of experimentally validated E’-box (only three: Per2, Bhlhb3, and Cry1) and the relatively short coreconsensus sequence of the E-box. Thus, we did not use an E’-box HMM in thisstudy. The lengths of these known functional elements were based on ourprevious experiments (21) and these were sufficient to produce circadiantranscriptional activity in circadian reporter assays. These sites were aligned
without gaps according to the direction of consensus sequences (TTATG[T/C]AA for the D-box; ref. 22), [A/T]A[A/T]NT[A/G]GGTCA for the RRE; ref. 24).
Because the consensus sequence for E-box is palindromic (CACGTG; ref. 19),
we generated all possible alignments by changing sequence directions (forward and reverse) and selected one alignment as described below. Thesealignments were used to build HMMs using hmmt program in the HMMER
1.8.4 software package (31) with default parameters (using sim annealing,
starting kT for sim annealing run as 5.0, and multiplier for sim annealing as0.95). We used the older version 1.8.4 package (the current version is 2.3.2) inthis study because the version 2 series was optimized for analysis of protein
sequences. Following construction, models were used to search genomic
regions for putative clock-controlled elements using the hmmls program withdefault parameters (by using threshold matches score to report as 0) except
use ‘-c’ option only for bidirectionally search. The average score was used in
the search for the conserved elements between human and mouse. To select
only one alignment for each E-box, we constructed 2048 HMMs of all possiblealignments, and calculated match scores of 12 known E-box sequences in
directional HMMER search. We selected the alignment that generated thehighest average match score for further work.
To design the ‘‘high-scoring’’ and ‘‘low-scoring’’ sequence of clockcontrolled elements, bidirectional HMMER searches were performed against
all possible sequences of the same lengths as training dataset (18 bp for E-box,
24 bp for D-box, and 23 bp for RRE) that contain ordinary consensus sequenceat the center (CACGTG for E-box; ref. 19; TTATGTAA for D-box; ref. 22,[A/T]A[A/T]NT[A/G]GGTCA for RRE; ref. 24), then filtered out those that naturally exist in either the human or mouse genome. The sequence of thehighest and lowest score was selected as the ‘‘high-scoring’’ and ‘‘lowscoring’’ sequences, respectively. All HMMER searches, except the directional
search in the selection of E-box alignments, were performed bidirectionally.
The higher score was adopted if match scores were obtained for both directions at the same position. The training data are available in Table S2. The
HMMs are publicly available on the circadian section of the mammalianpromoter/enhancer database: http://promoter.cdb.riken.jp/circadian.html.
Competitive DNA Binding Assays. In vitro transcription/translation of Flagtagged mouse protein from pMU2-Arntl, pMU2-Clock, pMU2-Bhlhb2, pMU2Dbp, pMU2-Nfil3, pMU2-Nr1d1, and pMU2-Rora were performed with TNT T7
Quick Coupled Transcription/Translation System (Promega) according to the
manufacturer’s specifications. In vitro transcribed/translated Arntl and Clockproteins were mixed in equal volume. The complementary oligonucleotides ofthree tandem repeats sequence of designed and control cis-acting elements,
which were labeled with biotin on 5⬘-end or non-labeled (for competitor)

Kumaki et al.

ACKNOWLEDGMENTS. We thank Hajime Tei and Yoshiyuki Sakaki for Arntl
and Clock expression vectors, Yutaka Suzuki, Sumio Sugano, and Seiichi
Hashimoto for advice on the mammalian promoter/enhancer database, Takao
Kondo for high-throughput monitoring systems, Hideki Ukai, Ryotaku Kito,
and Kazuhiro Yagita for real-time circadian reporter assay, Rikuhiro G.
Yamada, Tetsuya J. Kobayashi, and Tekeya Kasukawa for statistical analysis,
and Michael Royle for critical reading. This work was supported by RIKEN
Research Collaborations with Industry Program (H.R.U.), and in part, by RIKEN
Center for Developmental Biology (CDB) internal grant (H.R.U.), New Energy
and Industrial Technology Development Organization (NEDO) Scientific
Project grant (H.R.U.) and a Grant-in-Aid for Scientific Research from the
Ministry of Education, Culture, Sports, Science and Technology of Japan(Genome Network Project to H.R.U.). J.B.H. is supported by grants from the
National Institute of Mental Health and National Institute of Neurological
Disorders and Stroke of the National Institutes of Health (P50MH074924 – 01,
Joseph S. Takahashi, P. I, NIMH; 1R01NS054794 – 01A2, J.B.H, P. I, NINDS).

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PNAS 兩 September 30, 2008 兩 vol. 105 兩 no. 39 兩 14951

GENETICS

NoShift Antibody Dilution Buffer were diluted up to 0.5 ⫻ solution using
water in dilution for a working solution.

(Hokkaido System Science), were annealed to generate probes. Competitive
DNA binding assays were performed with NoShift Transcription Factor Assay
Kit (Novagen) according to the manufacturer’s specifications with the following modifications. Ten pmol biotinylated annealed oligonucleotides wereincubated with competitor oligonucleotides (final concentration were 0, 1, 3,
10, 30, and 100 pmol) and 5 ␮l of in vitro transcribed/translated reticulocyte
lysates in the binding mixture. After the samples bound to streptavidin-coatedmicroassay plate, the wells were washed, and Anti-Flag M2 Monoclonal
Antibody-Peroxidase Conjugate (SIGMA) was applied into the each well. The
wells were washed, and TMB substrate was added to each sample to develop
a colorimetric signal, which was subsequently read on a spectrophotometer at
450 nm (Power Wave XS, BioTek). Nr1d1 and Rora proteins were used withadditional modifications. Binding reactions were performed with their own
binding buffer (8 mM Tris-HCl, pH 7.5, 40 mM NaCl, 0.4 mM EDTA, 1.6 mM
MgCl2, 3.2% Glycerol, 0.4 mM DTT, 0.4 mg/ml BSA, and 0.5 ␮M poly dI;dC); 1
␮M ZnSO4 is further added into the binding buffer for Rora proteins and wereincubated for 90 min at room temperature. And NoShift Wash Buffer andSI Appendix

SI Materials and Methods

Construction of Promoter/Enhancer Database. Detailed database construction procedures are described here. We performed thefollowing steps to construct the mammalian promoter/enhancer database. We mapped RefSeq (1) (15/11/2004 release 28,712 sequences
for human and 8/11/2004 release 26,221 sequences for mouse), UniGene (2) (4,790,589 sequences of build #175 for human and
3,650,800 sequences of build #141 for mouse) and 5′-end sequences (3) (1,394,825 sequences) to human (NCBI Human genome build35) or mouse (NCBI Mouse genome build 33) genomes respectively using the BLAT program (4). The results of mapping andinformation from Ensembl genes (19,580 known genes and 2,641 novel genes for human, and 20,718 known genes and 4,665 novel
genes for mouse), Ensembl EST genes (27,678 genes for mouse) (Version 26) (5) and Vega genes (6,166 genes for human) (6) were used
to identify gene structure and putative transcriptional start site (TSS). We identified 24,749 human genes with 50,373 TSSs and 26,047
mouse genes with 43,863 TSSs. Next, we determined 16,268 human-mouse orthologues (65.7% of human genes and 62.5% of mousegenes) by selecting the reciprocal best match of all genes using the BLAST program.
As a next step, we sought to define orthologous genes across species. Using positional information of adjacent orthologues, we

1were able to determine 434 human-mouse syntenic regions. These regions were then compared using the unit of the blocks performedby the LAGAN program (7). We determined a total of 750,043 human-mouse genome conserved regions (173 Mb and 5.6% coverage ofhuman genome, and 172 Mb and 6.5% of mouse genome). Information regarding the coding regions was obtained from RefSeq,
UniGene, Ensembl Genes and Vega Genes, allowing the non-coding genome conserved regions to be defined. Through this process, wewere able to determine 893,798 human and 892,128 mouse human-mouse conserved non-coding regions (145 Mb and 4.7% coverage ofhuman genome and 144 Mb and 5.5% of mouse genome). Next, we sought to define the transcription factor binding sites (TFBSs) bysearching the known 862 consensus sequences obtained from TRANSFAC (8) at conserved regions of both genomes. In total, 7,804,559
human-mouse non-coding conserved TFBSs were predicted. Finally, data were integrated as a mammalian genome-wide
promoter/enhancer database.
Determination of Genes and TSSs. Here we describe the detailed methods and conditions used in mapping RefSeq, UniGene and 5'end sequences, determination of gene structures and TSSs, and determination of orthologues. All mRNA, EST and 5'-end sequenceswere mapped and selected by using blat, pslSort and pslReps programs of BLAT suit with the following parameter: "-q=rna minIdentity=95" for blat and "-minCover=0.2 –minAli=0.98 –nearTop=0.002" for pslReps, this is determined based on the parameters
used for constructing the UCSC genome browser database (9). We used repeat-masked genome sequences obtained from Ensembl(ftp://ftp.ensembl.org/) as the subject of mapping. Redundant mRNA sequences of UniGene, which contained part of the RefSeq

2sequences, were removed. If UniGene or the 5'-end sequence was mapped at a single continuous region, i.e. not spliced, it would not be
used for further analysis in order to decrease genomic sequence contamination, transcripts of repetitive sequences, or pseudo genes,
respectively. Determination of gene structures and TSSs was performed by the following five steps. First, we made transcriptional
clusters by clustering overlapping chromosomal positions at the same strand of the results of mapping of RefSeq, UniGene and 5'-endsequences, and information of Ensembl genes, Ensembl EST genes and Vega genes. Second, we determined exons from thetranscriptional cluster by selecting the regions with at least one or more sets of reliable information (RefSeq, Ensembl known gene orVega gene). Third, we also determined exon-exon connections—i.e. the splicing junctions—by selecting the connections with at least
one reliable set of information or two additional sets of information. Fourth, we determined the TSS or transcription termination site
(TTS) containing exons by selecting exons with at least one or more sets of reliable information of 5'-end or 3'-end (5'-EST and mRNA
sequence of UniGene and 5'-end sequence for TSS, and 3'-EST and mRNA sequence of UniGene for TTS). Lastly, we determined theposition of TSS by selecting the most 5'-end position of reliable information or second most 5'-end position of other information. To
determine human TSSs, we used 1,394,825 5'-end sequences from human full-length cDNA libraries generated by the oligo-cap method(3). In an attempt to determine mouse TSSs, we used UniGene sequences which contained 496,856 5'-end sequences from mouse fulllength cDNA libraries that were generated by the FANTOM project using the cap-trapper method (10). To determine orthologues, we
performed all genes to all genes BLAST homology search between the two species. Due to many of the genes having alternative3

splicing variants and alternative promoter variants, we used a merged gene sequence, which we were able to generate by joining
determined exon sequences of the gene and using this for homology search. Following a nucleotide BLAST search with parameter "-E 1–G 1", which reduced opening and extension of the alignment gap, and with a cutoff of E-value as 1.0e-4.0, we selected the reciprocal
best match as orthologues.
Determination of Non-coding Genome Conserved Regions. Here we describe the detailed methods and conditions for determining
genome conserved regions and coding regions. After the determination of syntenic regions by use of positional information of theadjacent orthologues, we used the following two steps to determine conserved regions in the genome was due to the size of the syntenic
region being too long for comparing genome sequences between the two species. First, we divided the syntenic region into blocks bypairs of conserved exons between two species, which we determined by comparing the nucleotide sequence of exon regions oforthologous gene pairs using nucleotide BLAST. We then determined the conserved regions by comparing the unit of the blocks ofsynteny region of the two species. Genome comparisons were performed using the LAGAN program, and conserved regions wereselected with 75% identity over 100 bp, as almost all of the evolutionally conserved elements previously determined (11) were detectedusing this cutoff value. These alignments were used for further analysis. Genome positions of the coding regions were determined by thefollowing procedures. First, we compared the coding regions of RefSeq sequences with the mapped position of genome sequences.
Second, we then compared the coding regions of mRNA and HTC sequences of UniGene with these mapped positions. Thirdly, we used4the information from the coding regions of Ensembl genes and Vega genes. Finally, the genome position of all coding regions weremerged and used for further analysis. In an attempt to determine the non-coding genome conserved regions, overlapping regions were
removed.
Prediction of Evolutionally Conserved putative TFBSs. In this section we describe the prediction of evolutionally conserved putative
TFBSs. We used 1,002 known consensus sequences of TFBS that were obtained from TRANSFAC. We searched the consensus
sequences at non-coding genome conserved regions of the two species, then selected evolutionally conserved putative TFBSs that exist
in the corresponding positions of the two species using alignments of sequence of genome conserved regions determined above. The
search results of 140 consensus sequences of human-mouse conserved putative were not used as no conserved putative TFBS wasdetected at non-coding genome conserved regions or excess number (> 250,000) of conserved TFBSs were detected.
Annotation and Integration. Here we describe the annotation, integration, and interface to the database. All determined data, including
genes, TSSs, non-coding genome conserved regions, and putative TFBSs, were related to gene annotations obtained from the Entrez
Gene (2). Additionally, we performed a rpsblast search of the conserved domain database (CDD) (12) to estimate the molecular functionof these genes. All data were integrated in a MySQL database. Finally, we constructed a web user interface for use with the mammalianpromoter/enhancer database. This meant that a large amount of data, including the alignment of genome conserved regions, sequenceand conservation of putative TFBSs, was made viewable by using the interface. Visualized genome conservation of genome conserved

5regions, which were outputted from the VISTA program (13), were also viewable. Additionally, to browse genome information
annotated with determined genes, TSSs, non-coding genome conserved regions and putative TFBSs, we introduced the Ensembl
Genome Browser (5) and the distributed annotation system (DAS) (14).

Determination of the distributions of distance from TSSs for natural and randomly positioned elements. To determine the
distributions of distance from TSSs for clock-controlled elements, we used predicted 1,108 E-boxes, 2,314 D-boxes and 3,288 RREs
(available on the circadian section of the mammalian promoter/enhancer database: http://promoter.cdb.riken.jp/circadian.html) andcalculate the distance from nearest TSS. The distributions of distance from TSSs for random positioned clock-control elements were
determined by randomly distribute the same number elements (1,108 for E-box, 2,314 for D-box and 3,288 for RRE) within conserved
non-coding regions. The distributions of randomly positioned elements were generated 100 times and the averaged distributions were
used.

Determination of FDR of putative elements. The false discovery rate (FDR) was determined for predicted elements. We used theresult of predictions of a randomized genome as a background distribution of false positives. Profile HMM searches were performed inthree conditions, (I) searching for conserved elements within conserved non-coding regions ("Conserved element"), (II) searching for6mouse elements within the conserved non-coding regions by relaxing the requirement of element conservation ("Non-coding region")and (III) searching for mouse elements within the entire mouse genome by relaxing both element conservation and search space("Whole genome"). The mouse genome sequence was randomized for these three conditions preserving the frequency of sequential
nucleotide pair ("dinucleotide"), because a relatively small number of "CG" dinucleotides are existed in mammalian genomes.
Otherwise (i.e. without preservation of dinucleotide), it will affect on the FDR for E-box, which contains "CG" dinucleotide. For the"Whole genome", a random mouse genome sequence was generated to keep the frequency of dinucleotide of the entire original mouse
genome. For the "Non-coding region", a random genome sequence was generated to keep the frequency of each dinucleotide of the noncoding genome conserved regions of the original mouse genome. After randomization, genome sequence was masked except in thecorresponding regions of the conserved non-coding genome regions of the original mouse genome. To reflect conservation between
mouse and human among the "Conserved element", non-coding genome conserved regions of mouse and human genomes weregenerated to maintain the dinucleotide frequency at the parallel positions of the conserved genome region between mouse and human,
including the case of a mismatch and or gap. For each condition, 200 randomized genome sequences were generated, and then HMM
searches were performed. The averaged hit counts of 200 searches of these models on randomized genome sequence were used to obtaina background distribution and estimate the false discovery rate for each model taking into consideration the total number of base pairs
searched. If the simulated value of FDR accidentally exceeds 1.0, the value is set to 1.0.

7

Animals. Male Balb/c mice (JAPS, Osaka, Japan) were purchased 5 weeks after birth. Mice were on a 12 hr light (400 lux): 12h dark
(LD12:12) cycle for at least 2 weeks and were given food and water ad lib. Then animals were transferred to constant darkness
conditions (DD) and, during the second DD cycle starting at CT0, animals were sacrificed every four hours under deep anesthesia andtissue samples were removed and frozen in liquid nitrogen for RNA extraction. This study was performed in compliance with the Rules
and Regulations of the Animal Care and Use Committee, Kinki University School of Medicine, and followed the Guide for the Care andUse of Laboratory Animals, Kinki University School of Medicine.

Genome sequences. Genome sequences of NCBI Human genome build 35 and NCBI Mouse genome build 33 were downloaded from
FTP site of Ensembl Project (5) (ftp://ftp.ensembl.org/) and used in this study.

Oligonucleotide sequences.
Primer sequence for quantitative PCR of mRNA.
Tbp-forward: 5'-GTTGTGCAGAAGTTGGGCTTC-3'

8

Tbp-reverse: 5'-TCACAGCTCCCCACCATGTT-3'
Per2-forward: 5'-TGTGCGATGATGATTCGTGA-3'
Per2-reverse: 5'-GGTGAAGGTACGTTTGGTTTGC-3'
Cry1-forward: 5'-TGAGGCAAGCAGACTGAATATTG-3'
Cry1-reverse: 5'-CCTCTGTACCGGGAAAGCTG-3'
Arntl-forward: 5'-CCACCTCAGAGCCATTGATACA-3'
Arntl-reverse: 5'-GAGCAGGTTTAGTTCCACTTTGTCT-3'
MGI:1926224-forward: 5'-GACCTGGCGGTGGATGG-3'
MGI:1926224-reverse: 5'-AACACATTTGCGTCCTGCC-3'
Lrrc35-forward: 5'-TGCTGCAGGCCTAATCCTTT-3'
Lrrc35-reverse: 5'-CGGTTGGGTTGGATGAGACT-3'
1300001I01Rik-forward: 5'-AAGATGGTTTTGCACTGGTTCA-3'
9

1300001I01Rik-reverse: 5'-TTTCGTGTCTTCAATTAGGCCTC-3'
Cpne7-forward: 5'-ATGGAAAGGGTGGTGAAGGG-3'
Cpne7-reverse: 5'-TCTCCACACGATCAAATGGC-3'
Gria2-forward: 5'-CTGAGTGCCTTACACAATGGTTTC-3'
Gria2-reverse: 5'-CGGATGCCTCTCACCACTTT-3'
Etv4-forward: 5'-GCCTCTGCCTAGGTCTTGCTC-3'
Etv4-reverse: 5'-ACACTGGATCTCTGTGGTGGG-3'
Pogz-forward: 5'-TTTATGCCACCACTCCCAGC-3'
Pogz-reverse: 5'-CGGCGTTCCTAATAACCCAC-3'
Plcb1-forward: 5'-AGTGCACGCCTTGCAACTC-3'
Plcb1-reverse: 5'-CTTCTTGAGGCTGTCGGACAC-3'
Irf2bp1-forward: 5'-TCGTGGCTTGCCTTTTCC-3'
10

Irf2bp1-reverse: 5'-CTTCCCCGCCCCCTG-3'
Trim8-forward: 5'-ACCCTCTTTCTAGCCGGAAGTT-3'
Trim8-reverse: 5'-GGTTTGAAGATGCCAAAGGC-3'
Tspan7-forward: 5'-GTATGGCATCGAGGAGAATGG-3'
Tspan7-reverse: 5'-ATGAGGAGGGTTTTGAGACAGG-3'
Atp1b2-forward: 5'-CTCGAATTTTGGAGCCGTCT-3'
Atp1b2-reverse: 5'-CACACACCGCCTAGAAGCAA-3'
Spsb3-forward: 5'-CAGGGACATCTCTGGTTCATTCA-3'
Spsb3-reverse: 5'-GGCTGAGCGCCGTATAAGAA-3'
Mgea5-forward: 5'-CCTTAATAGCAGATCCGCATGTG-3'
Mgea5-reverse: 5'-CAGTCCCCTTACCCTTACTTAACAAT-3'
Adam12-forward: 5'-CACTGTCCAGCCAATGTGTACC-3'
11Adam12-reverse: 5'-AGTAACCATCCACGCCCTGA-3'
Myo1b-forward: 5'-ACGAGTGTTTGTCTCTCTCTCCCT-3'
Myo1b-reverse: 5'-CAGACTTCAGCAGCCCTTTAGC-3'
D13Ertd150e-forward: 5'-CGCTTTTGCAACCAGGTGTT-3'
D13Ertd150e-reverse: 5'-GGTGGGAGCGAACGTGG-3'
The oligonucleotide sequence for competitive binding assay.
Canonical consensus sequences for E-box (CACGTG), D-box (TTATGTAA) and RRE ([A/T]A[A/T]NT[A/G]GGTCA) are indicated inbold while mutated core sequences for each element are indicated in bold and italics.
Per1 E-box-forward: 5'-CAAGTCCACGTGCAGGGACAAGTCCACGTGCAGGGACAAGTCCACGTGCAGGGA-3'
Per1 E-box-reverse: 5'-TCCCTGCACGTGGACTTGTCCCTGCACGTGGACTTGTCCCTGCACGTGGACTTG-3'
Mutated Per1 E-box-forward: 5'-CAAGTCACCGGTCAGGGACAAGTCACCGGTCAGGGACAAGTCACCGGTCAGGGA-3'
Mutated Per1 E-box-reverse: 5'-TCCCTGACCGGTGACTTGTCCCTGACCGGTGACTTGTCCCTGACCGGTGACTTG-3'

12

High-scoring E-box-forward: 5'-CGGGGCCACGTGCAGGCGCGGGGCCACGTGCAGGCGCGGGGCCACGTGCAGGCG-3'
High-scoring E-box-reverse: 5'-CGCCTGCACGTGGCCCCGCGCCTGCACGTGGCCCCGCGCCTGCACGTGGCCCCG-3'
Low-scoring E-box-forward: 5'-GTTAAACACGTGTTTTACGTTAAACACGTGTTTTACGTTAAACACGTGTTTTAC-3'
Low-scoring E-box-reverse: 5'-GTAAAACACGTGTTTAACGTAAAACACGTGTTTAACGTAAAACACGTGTTTAAC-3'
Per3 D-box-forward: 5'CCCGCGCGTTATGTAAGGTACTCGCCCGCGCGTTATGTAAGGTACTCGCCCGCGCGTTATGTAAGGTACTCG-3'
Per3 D-box-reverse: 5'CGAGTACCTTACATAACGCGCGGGCGAGTACCTTACATAACGCGCGGGCGAGTACCTTACATAACGCGCGGG-3'
Mutated Per3 D-box-forward: 5'CCCGCGCGCACCCGGCGGTACTCGCCCGCGCGCACCCGGCGGTACTCGCCCGCGCGCACCCGGCGGTACTCG-3'
Mutated Per3 D-box-reverse: 5'CGAGTACCGCCGGGTGCGCGCGGGCGAGTACCGCCGGGTGCGCGCGGGCGAGTACCGCCGGGTGCGCGCGGG-3'

13

High-scoring D-box-forward: 5'CCCGCGCGTTATGTAACGAGCCCGCCCGCGCGTTATGTAACGAGCCCGCCCGCGCGTTATGTAACGAGCCCG-3'
High-scoring D-box-reverse: 5'CGGGCTCGTTACATAACGCGCGGGCGGGCTCGTTACATAACGCGCGGGCGGGCTCGTTACATAACGCGCGGG-3'
Low-scoring D-box-forward: 5'ATGAAAATTTATGTAAGTTTAAACATGAAAATTTATGTAAGTTTAAACATGAAAATTTATGTAAGTTTAAAC-3'
Low-scoring D-box-reverse: 5'GTTTAAACTTACATAAATTTTCATGTTTAAACTTACATAAATTTTCATGTTTAAACTTACATAAATTTTCAT-3'
Arntl RRE-forward: 5'AGGCAGAAAGTAGGTCAGGGACGAGGCAGAAAGTAGGTCAGGGACGAGGCAGAAAGTAGGTCAGGGACG-3'
Arntl RRE-reverse: 5'CGTCCCTGACCTACTTTCTGCCTCGTCCCTGACCTACTTTCTGCCTCGTCCCTGACCTACTTTCTGCCT-3'
Mutated Arntl RRE-forward: 5'AGGCAGAAAGTCCTAGCGGGACGAGGCAGAAAGTCCTAGCGGGACGAGGCAGAAAGTCCTAGCGGGACG-3'

14

Mutated Arntl RRE-reverse: 5'CGTCCCGCTAGGACTTTCTGCCTCGTCCCGCTAGGACTTTCTGCCTCGTCCCGCTAGGACTTTCTGCCT-3'
High-scoring RRE-forward: 5'AGAAAGAAAGTAGGTCAGTGCGGAGAAAGAAAGTAGGTCAGTGCGGAGAAAGAAAGTAGGTCAGTGCGG-3'
High-scoring RRE-reverse: 5'CCGCACTGACCTACTTTCTTTCTCCGCACTGACCTACTTTCTTTCTCCGCACTGACCTACTTTCTTTCT-3'
Low-scoring RRE-forward: 5'CTCCCCTATTTGGGTCAACCGACCTCCCCTATTTGGGTCAACCGACCTCCCCTATTTGGGTCAACCGAC-3'
Low-scoring RRE-reverse: 5'GTCGGTTGACCCAAATAGGGGAGGTCGGTTGACCCAAATAGGGGAGGTCGGTTGACCCAAATAGGGGAG-3'
The oligonucleotide sequence for construction of expression plasmids.
Underlines indicate linker sequence, which incorporated the recognition sequence for a restriction enzyme.
Arntl-forward: 5'-ATTACCCTGTTATCCCTAATGCGGACCAGAGAATGGAC -3'

15

Arntl-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACTACAGCGGCCATGGCAAGTC-3'
Clock-forward: 5'-ATTACCCTGTTATCCCTAATGTGTTTACCGTAAGCTGTAG -3'
Clock-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACTGTGGCTGGACCTTGG -3'
Bhlhb2-forward: 5'-ATTACCCTGTTATCCCTAATGAACGGATCCCCAGCGC -3'
Bhlhb2-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCAGTCTTTGGTTTCTAAG -3'
Dbp-forward: 5'-ATTACCCTGTTATCCCTAATGCGCGGCCTCTGAGCGAC -3'
Dbp-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACAGTGTCCCATGCTGG -3'
Nfil3-forward: 5'-ATTACCCTGTTATCCCTAATCAGCTGAGAAAAATGCAG -3'
Nfil3-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACCTGGAGTCCGAAGCCG -3'
Nr1d1-forward: 5'-ATTACCCTGTTATCCCTAATACGACCCTGGACTCCAATAAC -3'
Nr1d1-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACTGGGCGTCCACCCGG -3'
Rora-forward: 5'-ATTACCCTGTTATCCCTAATTATTTTGTGATCGCAGCG -3'
16

Rora-reverse: 5'-ACCCATAATACCCATAATAGCTGTTTGCCACCCATCGATTTGCATGGCTG-3'

Plasmid constructions. The SV40-dLuc (15) and Per2-dLuc (16) reporters are described elsewhere. The sequences containing threetandem repeats of putative cis-acting elements were inserted into MluI/BglII site of the SV40-dLuc vector. Detailed information on
putative cis-acting elements is available in Tables S1 and S3. The DNA sequences of all constructs generated in this study were verified
by standard methods. For construction of expression plasmids, we amplified the full length coding sequence of mouse Arntl, Clock,Bhlhb2, Dbp, Nfil3, Nr1d1 and Rora from pCI-Arntl (17), pCI-Clock (17), pSPORT6-Bhlhb2 (MGC clone # 3707474, Invitrogen,
Carlsbad, CA), pSPORT6-Rora (MGC clone # 3592667, Invitrogen) or NIH3T3 cDNA library by PCR with forward primers containing
I-SceI recognition sequence and reverse primers containing PI-PspI recognition sequence (Hokkaido System Science, Hokkaido, Japan).
PCR product was digested with I-SceI (NEW ENGLAND BioLabs, Ipswich, MA) and PI-PspI (NEW ENGLAND BioLabs), and cloned
into pMU2 vector (18) and termed as pMU2-Arntl, pMU2-Clock, pMU2-Bhlhb2, pMU2-Dbp, pMU2-Nfil3, pMU2-Nr1d1 and pMU2Rora. Those genes were fused in-frame with 1 × Flag Tag at N-terminal by I-SceI recognition site, and regulated by T7 promoter inpMU2.

Quantitative PCR. Quantitative PCR was performed with the ABI PRISM 7900HT and SYBR Green Reagents (Applied Biosystems,

17Foster City, CA). cDNAs were synthesized from 0.25 μg of total RNA using SuperScript II Reverse Transcriptase (Invitrogen) andRandom Primers (Promega). Samples contained 1 × SYBR Green PCR Master mix, 0.8 μM primers and 1/50 synthesized cDNA in a 10
μl volume. The PCR conditions were as follows: 10min at 95°C, then 45 cycles of 15 sec at 95°C and 1min at 59°C. Absolute cDNA
abundance was calculated using the standard curve obtained from mouse genomic DNAs. Tbp expression levels were quantified andused as the internal control. Detailed data of quantitative PCR is available at the circadian section of the mammalian promoter/enhancer
database.

Rhythmicity analysis of real-time bioluminescence data. Bioluminescence time-series data beginning 21 h after forskolin stimulation
were used for analysis to distinguish endogenous circadian oscillations from acute effects of stimulation. Bioluminescence data weredetrended by using the trend curve calculated by the smoothing spline method, and statistical significance and the period of circadianoscillation in the detrended data was evaluated as previously described (p < 0.01) (16). To visualize the normalized bioluminescencedata (i.e. the oscillatory component of the bioluminescence data) shown in Fig. 2A, the moving average of the absolute value of thedetrended bioluminescence data was calculated first. The window size of the moving average was set to half of the period calculated
above. Then, the oscillatory component of the detrended data was calculated by dividing the data by the moving average of the data at
each time point.

18

Amplitude analysis of real-time bioluminescence data. Bioluminescence time-series data of 21–96 h after forskolin stimulation werenormalized so that the average bioluminescence is 1.0, and then detrended as described above. Detrended and normalized time-series
data were used in further analysis. To determine the amplitude of each sample, 151 "reference" time-series data were generated for Ebox, D-box and RRE, respectively, by multiplying time-series data of positive controls (Per1 E-box, Per3 D-box and Arntl RRE) (11)with the value of "relative amplitude" from 0 to 1.5 with 0.01 step. Time-series data for each sample were compared with reference datausing the least-squares method to determine the best fit for reference data, the "relative amplitude" of which was used as the control
amplitude and used for comparisons.

Rhythmicity analysis of quantitative PCR data. Two statistical tests, cosine fitting and analysis of variance (ANOVA), werecombined to identify circadian expression profiles with high-amplitude. To evaluate the wave form of expression profiles, statisticalanalysis (cosine fitting test) was performed in parallel on two independent expression profiles (two days each). 2-cycle cosine waves of
24-h period with a different phase were generated by shifting phase with 0.4-h interval (total 60 waves). A two day expression profile
was compared with 60 cosine test waves by calculating the correlation coefficient to determine the most correlated cosine wave, thecorrelation coefficient of which was used as "maximum correlation coefficient" for the expression profile. Statistical significance of the19

maximum correlation coefficient for the expression profile was evaluated by calculating the maximum correlation coefficient for
100,000 random expression profiles. Two P-values calculated on two independent expression profiles (two days each) were combinedby Fisher's probability combination method (19) to calculate the synthetic P-value for cosine fitting test. To evaluate the amplitude ofexpression profile, statistical analysis (one-way ANOVA test) was also performed on experimental duplicates. P-values for cosine
fitting and ANOVA tests were combined by Fisher's probability combination method to calculate the synthetic P-value, which was used
to identify circadian expression profiles with high-amplitude (p < 0.03). The peak time of an expression profile was estimated by thepeak time of the best correlated cosine wave.

Over representation analysis of clock-controlled genes. To determine the significance of the enrichment of clock-controlled genes
within the genes having predicted clock-controlled elements, we first selected the 100 most significant predicted sequences for eachclock-controlled elements (E-box, D-box and RRE) that were mapped to 98 genes on U74 microarray after removing the 21 clockcontrolled genes used for HMM generation and training. As the dataset of clock-controlled genes, we used previously identified clockcontrolled genes in the SCN and liver by using mouse U74 microarray (15, 20). After removing the 21 clock-controlled genes in theabove, we found additional 19 putative clock-controlled out of the 6,195 genes common in our mammalian promoter/enhancer databaseand U74 mouse microarray, which is significantly higher than the expected 10.67 genes that would have arisen from chance ( p = 0.009).

20

Estimation of the number of high-amplitude E-boxes. To estimate the number of clock-controlled conserved E-boxes that likely
confer circadian rhythmicity, we used 1,108 E-boxes (minimum HMM score = 11.56; available on
http://promoter.cdb.riken.jp/circadian.html) except E-boxes used for HMM generation and training. Linear interpolation from the true
positive rate (40%) of high-scoring conserved E-boxes (mean HMM score = 16.15) and the false negative rate (7.1%) of low- scoring
conserved E-boxes (mean HMM score = 2.5143) was used to estimate the probability of rhythmicity for each E-box. The sum of theprobability of 1,108 E-boxes was calculated to estimate the number of high-amplitude E-boxes.

Microarray expression data analysis of genes with predicted clock-controlled elements. To investigate the averaged expression ofgenes with predicted clock-controlled elements, we used time-series microarray expression data of mouse liver in our previous study(15). Averaged expression value under LD and DD conditions were used for this analysis. We selected 100 most significant sequences
for each clock-controlled elements (E-box, D-box and RRE). 21 genes, which were used for training data set of HMMs, were excludedin the selection. We then related them to microarray probe set data (36 genes for E-box, 29 genes for D-box and 34 genes for RRE
respectively). In case that more than one probe sets exists for a single gene, averaged expression data was used. Then, averaged
expression value for each time points were determined for each clock-controlled element. Data were then normalized so that the average

21expression value over 2-day 12-point time courses is 1.0. To statistical tests, cosine fitting was performed. 2-cycle cosine waves of 24-h
period with a different phase were generated by shifting phase with 0.4-h interval (total 60 waves). A 2-day expression profile wascompared with 60 cosine test waves by calculating the correlation coefficient to determine the most correlated cosine wave, thecorrelation coefficient of which was used as "maximum correlation coefficient" for the expression profile. Statistical significance of the
maximum correlation coefficient for the expression profile was evaluated by calculating the maximum correlation coefficient for
100,000 random expression profiles. P-values for cosine fitting were used to evaluate circadian oscillation of averaged expression
profiles. The peak times of the averaged expression profile were estimated by the peak time of the best correlated cosine wave.

Sequence logos. Sequence logos shown in Fig. S4 and Table S2 were created by WebLogo (http://weblogo.berkeley.edu/) (21).

Calculation of relative affinity from competitive DNA binding assay data. A relative binding affinity between a regulator ( R ) andan unlabeled competitive DNA element ( Du ) in comparison with that to the labeled control DNA element ( Dl ) was determined bycompetitive DNA binding assays. In each competitive DNA binding assay, the concentration of bound regulator to the labeled element( [ RDl ] ) can be described as follows;22[ RDl ] = ([ R] all − [ R])[ Dl ]A[ Dl ] + u [ Du ]Al

(Eq. 1), where [ R]all ≡ [ RDl ] + [ RDu ] + [ R] is a total concentration of a regulator,[ RDl ] is a concentration of the regulator-labeled element complex,[ RDu ] is a concentration of the regulator-unlabeled element complex,[R ] is a concentration of a free regulator,[ Dl ] is a concentration of a free labeled element,[ Du ] is a concentration of a free unlabeled element,
Al ≡[ RDl ]
is an affinity constant between a regulator and a labeled element, and[ R ][ Dl ]Au ≡[ RDu ]
is an affinity constant between a regulator and an unlabeled element.
[ R ][ Du ]Since an excessive amount of a labeled or unlabeled element is usually applied in the competitive DNA binding assay, the concentrationof a labeled ( [ Dl ] ) or unlabeled ( [ Du ] ) element is much greater than the concentration of bound regulator to the labeled ( [ RDl ] ) or23

unlabeled ( [ RDu ] ) element. Thus, the total concentration of the labeled ( [ Dl ] all ≡ [ Dl ] + [ RDl ] ) or unlabeled ( [ Du ]all ≡ [ Du ] + [ RDu ] )element can be approximated as follows;[ Dl ]all ≅ [ Dl ][ Du ]all ≅ [ Du ]Since an amount of a labeled or unlabeled element vastly exceeds the amount of regulator, the amount of a free regulator ( [R ] ) is muchless than the total amount of a regulator ( [ R]all ) in the competitive DNA binding assay. Thus, [ R]all − [ R] can be also approximated asfollows;[ R]all − [ R] ≅ [ R]all
In addition, since [ RDl ] is proportional to the measured value of competitive DNA binding assay ( M 450 ), Equation 1 can be rewritten as
follows;
M 450 ∝ [ RDl ] ≅ [ R]all[ Dl ]allA[ Dl ]all + u [ Du ]allAl

Furthermore, as [ R]all is constant in the assay, we can derive the following equation;24

M 450 = C[ Dl ]all[ Dl ]all + A ∗ [ Du ]all

, where C is a proportional constant, and A ≡(Eq. 2)Au
is an relative affinity constant of the unlabeled element in comparison with that ofAl

the labeled element.
To determine the relative affinity constant from the measured value of competitive DNA binding assay, we first determined thevalue of [ Dl ]all and C from series of data for unlabeled known clock-controlled element (positive control) where A was defined as 1.0.
In details, by changing the value of [ Dl ]all and C , the most fitting [ Dl ]all and C values were determined using the least-square method
applied to series of measured values and model data calculated from Equation 2. We then determined the values of A for unlabeledelements including the "high-scoring", "low-scoring" and "negative control" by changing the value of A .

In silico analysis of affinity to amplitude mechanism. By modifying the previous described formula (11), we formulatedtranscriptional activity T (t ) at time t regulated by competition between a clock-controlled activator and a repressor as follows:A(t ) n)Ka
T (t ) ≡+αA(t ) nR(t ) n
1+ (
) +(
)
Ka
Kb
(

25

where 1 / K a and 1 / K b represent the affinity of an activator and a repressor, respectively. α represents transcriptional activity that does
not depend on the circadian clock. n represents the Hill coefficient at competitive regulation. A(t ) and R(t ) represent expression of aclock-controlled activator and repressor, which are defined as follows:A(t ) ≡ β a (1 + Cos ( 2πt−a)) + γ a24

and
R (t ) ≡ β b (1 + Cos ( 2πt −b)) + γ b24

where β a and β b represent half amplitude of expression of an activator and a repressor. γ a and γ b represent expression of an activatorand a repressor that does not depend on the circadian clock. a(−12 ≤ a ≤ 12) and b(−12 ≤ b ≤ 12) represent phases of expression of anactivator and a repressor. Then, we formulated output P(t ) at time t which depended on transcriptional activity T (t ) as follows:dP (t ) = T (t ) − λP (t )dtwhere λ represent decay constant and defined as follows:26λ ≡ log 2 / T1 / 2
where T1 / 2 represent half life. For simplicity, we used a = 0 , b = 12 , α = 0.2 , β a = β b = 1 , γ a = γ b = 0.2 , T1 / 2 = 3 , P(0) = 0 and
48 ≤ t ≤ 144 in the analysis. We used n = 1 and K b = 1 for the analysis of activator affinity to amplitude mechanism (Fig. 4 B and C).

Output time-series data were normalized so that the center value of maximum and minimum value of time-series data is 100%. The
difference between maximum and minimum values of normalized output time-series data was used to define amplitude.

27

SI Discussion

Utility of the Promoter/Enhancer Database. As a tool for understanding systems-level transcriptional regulation in mammals, weconstructed the mammalian promoter/enhancer database (http://promoter.cdb.riken.jp) by integrating information of conserved noncoding regions, TSSs, and TFBSs. Users can input a gene name or symbol, or UniGene or RefSeq identifiers, and get back a page thatsummarizes promoter information with additional links to outside databases such as SymAtlas and NCBI. The promoter sequences areavailable in a default view 1000 bp 5' of the TSS; this default view can be changed arbitrarily by the user 5' or 3' of the TSS, and the
DNA sequence information can be downloaded in FASTA format. Users can also highlight conserved sequence regions between
humans, mice, and rats, as well as TSSs, exons and TFBSs. Where known, links to alternative promoters are available. Although weapplied this database to the understanding of circadian transcriptional regulation, it is generically useful and can be applied to any aspectof mammalian transcriptional regulation.

In silico modeling of affinity amplitude mechanism. From competitive DNA binding assays, the "high-scoring" D-box and RRE show

approximately the same affinity as positive control whereas the "low-scoring" D-box and RRE show relatively weak affinity (Fig. 4A
and Fig. S5A). These results can be reasonably explained by using in silico model that has been extended from our previous model for28

gene expression of transcriptional activators and repressors to generate high-amplitude circadian output (11) by introducing expression
dynamics of an activator and a repressor, affinity of a DNA element to an activator and a repressor, and half life of output (see also SI
Materials and Methods). This in silico analysis shows that, if a DNA element has a weak affinity to both an activator and a repressor,

then the transcription system exhibits low-amplitude of output oscillations, which can be intuitively elucidated (Fig. S5B). In this insilico analysis also shows that the amplitude of output oscillations depend not only on the strength of affinity but also on a hillcoefficient (i.e. a parameter for nonlinearity in transcriptional response) (Fig. S5B).
On the other hand, the "high-scoring" E-box shows approximately the same affinity as positive control, whereas the "low-scoring"E-box shows, surprisingly, 4.8-times higher affinity only to Arntl/Clock activator (Fig. 4A and Fig. S5A). Since "low-scoring" E-box
shows approximately the same affinity to Bhlhb2 repressor as positive control, this result suggests that the "low-scoring" E-box has an
unbalanced affinity stronger for Arntl/Clock activator than Bhlhb2 repressor. In order to interpret this seemingly complicated result, we
also performed in silico analysis of affinity-amplitude relationship especially in the case that a DNA element has an unbalanced affinity
between an activator and a repressor. This in silico analysis shows that, if a DNA element has a 5-times higher affinity to an activator
than a repressor, then the transcription system exhibits less than half amplitude of output oscillations (Fig. 4B). Further in silico analysis
also shows that, if a DNA element has a lower affinity to an activator than a repressor, then the transcription system exhibits the reduced
amplitude of output oscillations (Fig. 4C). Collectively, these results suggest that not only the strength of affinity to regulators but also29the balance of affinity between an activator and a repressor are important in generating high-amplitude outputs. In this study, we
supposed the same level of expression for a clock-controlled activator and repressor for simplicity in the modeling, and drew theconclusion that appropriate affinity balance between activators and repressors is important. We can easily generalize it into different
levels of expression between activator and repressor. In such a case, the conclusion also holds for the product of affinity andconcentration instead (i.e. appropriate balance in the product of concentration and affinity is important).

30

SI References

1.

Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of

genomes, transcripts and proteins. Nucleic Acids Res 35:D61-65.
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Wheeler DL, et al. (2008) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res

36:D13-D21.
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Kimura K, et al. (2006) Diversification of transcriptional modulation: Large-scale identification and characterization of putative

alternative promoters of human genes. Genome Res 16:55-65.
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Kent WJ (2002) BLAT--the BLAST-like alignment tool. Genome Res 12:656-664.

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Flicek P, et al. (2008) Ensembl 2008. Nucleic Acids Res 36:D707-D714.

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Wilming LG, et al. (2008) The vertebrate genome annotation (Vega) database. Nucleic Acids Res 36:D753-D760.

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Brudno M, et al. (2003) LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA.

Genome Res 13:721-731.

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Matys V, et al. (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic

Acids Res 34:D108-110.
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Karolchik D, et al. (2008) The UCSC Genome Browser Database: 2008 update. Nucleic Acids Res 36.

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Okazaki Y, et al. (2002) Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs.

Nature 420:563-573.
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Ueda HR, et al. (2005) System-level identification of transcriptional circuits underlying mammalian circadian clocks. Nat

Genet 37:187-192.
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Marchler-Bauer A, et al. (2007) CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res

35:D237-240.
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Mayor C, et al. (2000) VISTA : visualizing global DNA sequence alignments of arbitrary length. Bioinformatics 16:1046-1047.

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Dowell RD, Jokerst RM, Day A, Eddy SR, Stein L (2001) The distributed annotation system. BMC Bioinformatics 2:7.

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Ueda HR, et al. (2002) A transcription factor response element for gene expression during circadian night. Nature 418:534-539.

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Sato TK, et al. (2006) Feedback repression is required for mammalian circadian clock function. Nat Genet 38:312-319.

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Hida A, et al. (2000) The human and mouse Period1 genes: five well-conserved E-boxes additively contribute to theenhancement of mPer1 transcription. Genomics 65:224-233.
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Ukai H, et al. (2007) Melanopsin-dependent photo-perturbation reveals desynchronization underlying the singularity of

mammalian circadian clocks. Nat Cell Biol 9:1327-1334.
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Fisher RA (1970) Statistical methods for research workers.

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Panda S, et al. (2002) Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109:307-320.

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33

Supporting Information
Kumaki et al. 10.1073/pnas.0802636105

1. Identify gene structure and determine TSS

Genes TSSs
human 24,749 50,373
mouse 26,047 43,863

2. Determine genome conserved regions
750,043 regions
consists of
173 Mb (5.6%) of
human genome
172 Mb (6.5%) ofmouse genome

3. Predict putative TFBSs
coserved TFBSs
7,804,559

4. Integrate into promoter/enhancer database

Mammalian Genome-wide
Promoter/Enhancer Database
Fig. S1. Construction of the mammalian promoter/enhancer database. Mammalian full-length cDNA and EST sequences were mapped onto mammalian
genome sequences to identify 24,749 human genes with 50,373 TSSs and 26,047 mouse genes with 43,863 TSSs. These mammalian genes were then comparedto identify 16,268 human-mouse orthologs (65.7% of human genes and 62.5% of mouse genes). The positional information of adjacent orthologs was used todetermine 434 human-mouse syntenic regions, which contained 750,043 human-mouse conserved genomic regions. The 862 consensus TFBSs from TRANSFAC
were then mapped on these conserved genomic regions to identify the 7,804,559 sites conserved between human and mouse in non-coding regions. Human-rat
comparisons were performed using the same procedure. Finally, visualization of putative promoter/enhancer and TFBSs data, and curation of current genes were
integrated into a free and publicly accessible website (Mammalian Promoter/Enhancer Database; http://promoter.cdb.riken.jp/).

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

1 of 6

A-1

Normalizedbioluminescence0

1

Per2 promoter
SV40 promoter
Nfil3 RREACAGAAAAAGTGGGTCAGTTTGT

Elovl5 RRE
Col4a1 RRE2
Atf5 RRE
Macf1 RRE
D19Ertd721e RRE

TCTTGTAAATTGGGTCATGGCGTTCAGCCAAACTAGGTCAAAACCTCAAGTAAAACTGGGTCACGAAGG
CCCTGAAAAGTAGGTCAGTGCCTAGGAAGAAAATAGGTCAGACATG

BC004004 RRE

AGTCTGAATATAGGTCAATGTGAGGCAGGAAAATGGGTCAGTGCTG

Col4a1 RRE1
1

2

3

4

Day

B 800

100
0

Per2 promoter

2
3
Elovl5 RRE

42
3
Macf1 RRE

4

1
2
3
Col4a1 RRE1

4

1

4

1

Bioluminescence (counts/sec)

C 1800

500
0

1

1350

300
0
550

180
0

350

2
Day

3

100
0
1150

500
0
450

150
0

SV40 promoter

1
2
3
Col4a1 RRE2

4

1
2
3
D19Ertd721e RRE

4

1

2
Day

3

Nfil3 RRE

1150

300
0

2
3
Atf5 RRE

4

1
2
3
BC004004 RRE

4

1

4

1

800

250
0
600

4

150
0

2
Day

3

Fig. S2. Functional clock-controlled elements used to train HMMs. (A) Bioluminescence from functional clock-controlled elements fused to the SV40 basic
promoter driving a dLuc reporter (SV40-dLuc) in NIH 3T3 fibroblasts. Known clock-controlled promoter (Per2 promoter), known clock-controlled element fusedto the SV40 basic promoter (Nfil3 RRE), or the SV40 basic promoter alone (SV40 promoter) driving luciferase were used as controls. The colors in descending orderfrom magenta to black to green represent the detrended bioluminescence. Columns represent time points, and rows represent the predicted elements on thedesignated genes. (B and C) Raw bioluminescence data of positive and negative controls (B), or functional clock-controlled elements inserted into SV40-dLuc
reporters (C). Error bars indicate the standard error of mean (SEM) determined from independent experimental duplicates for each condition.

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

2 of 6

Per2 promoter

A 110

20
0

B 190

40

0

1
2
3
MGI:1926224 E-box

130
0

2
3
Spsb3 RRE

4

1
2
3
Rps19 E-box

4

1
2
3
Spry4 D-box

4

1

2
3
Lasp1 RRE

4

1
2
3
Low-score E-box 1

4

1
2
3
Low-score E-box 8

4

1

Bioluminescence (counts/sec)

190
0

15
0

F 130

15
0

G 95

35
0

H 52

16

0

36

11

0

1

2
Day

3

15
0
160

4

D 580

E 70

4

2
3
Etv4 D-box

1

C 420

SV40 promoter

60

35
0
600

200
0
230

70
0
90

15
0
240

120
0
180

35
0
235

80

0

34

4

11

0

4
1
2
3
1300001I01Rik E-box

1
2
3
Irf2bp1 D-box

4

1
2
3
Myo1b RRE

4

1

2
3
Esrra E-box

4

1
2
3
Slc22a20 D-box

4

1
2
3
Zfp318 RRE

4

1
2
3
Low-score E-box 2

4

1
2
3
Low-score E-box 9

4

1

2
Day

3

Per1 E-box

75

20
0

1
2
3
Cpne7 E-box

4

1

2
3
Pogz D-box

4

1
2
3
Atp1b2 RRE

4

1
2
3
AK122525 E-box

4

1
2
3
Fmn2 D-box

4

1

2
3
Ryr1 RRE

4

1
2
3
Low-score E-box 3

4

190

65
0
375

90
0
650

140
0
155

45
0
85

25
0
1500

200
0
57

11

0

55

4

10

1

2
Day

3

95
0
75

25
0

500
0
80

15
0
285

130
0

4

1

2
3
Gria2 D-box

4

1
2
3
Adam12 RRE

4

1
2
3
Foxd3 E-box

4

1

4

50
0
155

4

0

43

15

3

15
0

1

2

3

4

Plcb1 D-box

470

260
0
70

10
0

1
2
3
D13Ertd150e RRE

1

110

30
0

1

4

170
0
190

2
3
Jeg2 E-box

4

2

4

3

Tspan7 D-box

390

50
0
110

25
0

1
2
3
Mgea5 RRE

41
2
3
Pprc1 E-box

4

1

4

2

3

Trim8 D-box

175

75
0

1

2

3

4

A730009L09Rik RRE

145

20

2

Arntl RRE

85

1
2
3
Lrrc35 E-box

1500

1
2
3
4
Low-score E-box 10

0Per3 D-box

290

0

1
2
3
Low-score E-box 4

4

1
2
3
4
Low-score E-box 11

1

2
Day

3

Low-score E-box 5

115

4

30

0

51

9

0

1
2
3
4
Low-score E-box 12

1

2
Day

3

Low-score E-box 6

335

4

105

0

27

7

0

1
2
3
4
Low-score E-box 13

1

2
Day

3

Low-score E-box 7

125

4

45

0

51

13

0

1
2
3
4
Low-score E-box 15

1

2
Day

3

4

Fig. S3. Detailed information on experimental validation of HMM-based predictions at the cellular level. Raw bioluminescence data from positive and negative
controls (A), and predicted clock-controlled elements inserted into the SV40-dLuc reporter that generate strong circadian transcriptional activity with highamplitude of the (B) E-box, (C) D-box, and (D) RRE, and weak circadian transcriptional activity with low amplitude or arrhythmic transcriptional activity of the(E) E-box, (F) D-box, and (G) RRE. Raw bioluminescence data from 14 low-score E-boxes inserted into the SV40-dLuc reporter (H). Only the low-score E-box No.
8 generated strong circadian transcriptional activity with high amplitude. The low-score E-box No. 14 was not measured due to the difficulty of reporter
construction. Error bars indicate the standard error of mean (SEM) determined from independent experimental replicates (n ⫽ 2 or 3) for each element tested.

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

3 of 6

Pattern1
Per1 E-box2
Per1 E-box3
Per1 E-box5
Nr1d1 E-box1
Nr1d1 E-box2
Nr1d2 E-box1
Dbp E-box
Bhlhb2 E-box
Rorc E-box
High-scoring E-box

TAGAGCCACGTGAGGGCG
TTTAGCCACGTGACAGTG
CAAGTCCACGTGCAGGGACGGGCCCACGTGCTGCATGGGTGCCACGTGCGAGGG
ACTGGCCACGTGCACGGT
CCTCGCCACGTGAGTCCG
CCGGGCCACGTGAAGCGT
AGGTGCCACGTGCACCAGCGGGGCCACGTGCAGGCG

Pattern2
Per1 E-box1
Per1 E-box4
Nr1d2 E-box2
MGI:1926224 E-box
Lrrc35 E-box
1300001I01Rik E-box
Cpne7 E-box

AGGGAACACGTGCAGGCTTAACGACACGTGGGCCCTCGGAGACACGTGAGGCCGAAGAGACACGTGCTGGGG
ACGTGACACGTGCGGCGG
CGGGGACACGTGCGCGCA
CCGAGACACGTGTGCCCG

Fig. S4. Two patterns revealed in the adjacent sequence of high-amplitude E-boxes. High-amplitude E-box sequences classified into two patterns were shown.
Information contents of the relative frequency of each nucleic acid at the position of the pattern were shown by sequence logos.

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

4 of 6

Positive control
E-box

A

High-scoring

Low-scoring
D-box

Negative control

Fitting

RRE

Repressor

Activator

Repressor

Activator

Repressor

Affinity = 1.00

Affinity = 1.00

Affinity = 1.00

Affinity = 1.00

Affinity = 1.00

Affinity = 1.00

Affinity = 1.23

Affinity = 0.74

Affinity = 2.12

Affinity = 3.07

Affinity = 1.38

Affinity = 1.21

Affinity = 4.80

Affinity = 0.81

Affinity = 0.21

Affinity = 0.24

Affinity = 0.41

Affinity = 0.22

Affinity = 0.02

Affinity = 0.00

Affinity = 0.00

Affinity = 0.00

Affinity = 0.00

Affinity = 0.01

Positive
control

Activator

1.0
0.5

Highscoring

0

0.5
0

Lowscoring

Relative signal

1.0

1.0
0.5

Negative
control

0

1.0
0.5
0

50

0

Relative amplitude of output (%)

B

100 0

50

100 0

50

100 0
50
Competitor (pmol)

150

n=4
n=3
n=2

100

n=1

100 0

50

100 0

50

100

50

100
10-3

101
102
10-1
1
10-2
Affinity of both regulators (1/Ka = 1/Kb)

103

Fig. S5. Analysis of affinity to amplitude mechanism. (A) Affinity analysis of competitive binding data. The relative affinities of regulators for known clock
controlled elements vs. synthetic elements were determined from competitive binding assay data shown in Fig. 4. A series of signal of binding between labeledoligonucleotide of positive control element and regulators, which were challenged with unlabeled oligonucleotides of positive control element (blue),high-scoring element (red), low-scoring element (green), or negative control element (black) in competition assays. This data were fitted by the model data
(purple) using the least-squares method and the affinities relative to that of positive control were determined from fitting data. The value of relative affinities(positive control is 1.0) are indicated. See also SI Appendix for more detail. (B) In silico analysis of affinity to amplitude mechanism when affinity of activator andrepressor are balanced. The relative amplitudes of oscillation of output plotted against strength of regulators binding affinity when the affinity of an activatorand a repressor are the same; n indicates the Hill coefficient at competitive regulation. Amplitude was normalized so that the maximum value at n ⫽ 1 is 100%.

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

5 of 6Other Supporting Information FilesSI Appendix

Kumaki et al. www.pnas.org/cgi/content/short/0802636105

6 of 6

Table S1. Functional RREs from known clock-controlled genes used in this study
Gene
Elov15BC004004

Macf1

Atf5

Col4a1Col4a1

D19Ertd721e

GeneID
68801

80748

11426

107503

12826

12826

225896

Element

Organism

chr

Start

End

Sequence

Mouse

978317303

78317325 Mouse TCTTGTAAATTGGGTCATGGCGT

Human

6

53273932

53273910 Human TTGTGCAAAGTAGGTCATGCCGT

Mouse

17

27851244

27851266 Mouse AGTCTGAATATAGGTCAATGTGA

Human

6

3692712636927148 Human TTTGGGAATCTAGGTCATTCTGA

Mouse

4

121994185

121994207 Mouse CCCTGAAAAGTAGGTCAGTGCCT

Human

1

39277738

39277716 Human CCCTGAAAAGTAGGTCAGCGCCT

Mouse

7

32213618

32213640 Mouse CAAGTAAAACTGGGTCACGAAGG

Human

19

55118372

55118350 Human GGAGGGTAACTGGGTCACGCAGG

Mouse

8

11309356

11309378 Mouse GGCAGGAAAATGGGTCAGTGCTG

Human

13

109748860

109748882 Human AGCAGGAAAATGGGTCAGTGATG

Mouse

8

11337102

11337124 Mouse TCAGCCAAACTAGGTCAAAACCT

Human

13

109781193

109781215 Human TCAGCCAAAATAGGTCAAAACAG

Mouse

19

79728407972862 Mouse AGGAAGAAAATAGGTCAGACATG

Human

11

62201916

62201894 Human AAGAAGAAATTAGGTCAGACATC|

RRE|| ||| | ||||||| ||||RRE|||| ||||||| | ||||||||||||||||||||| ||||

RRE

||

RRE

|||||||||||| |||||||||||||||||||||| ||RRE1||||||||| |||||||||||

RRE2| ||||||| ||||||||||||

RRE

References
1. Ueda HR, et al. (2002) A transcription factor response element for gene expression during circadian night. Nature 418:534-539.
2. Panda S, et al. (2002) Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109:307-320.

1

Evidence

Affymetrix Probe ID

ref. 1, ref. 293496_atref. 1, ref. 2

95517_i_atref. 298402_atref. 1, ref. 2103006_atref. 1, ref. 2101093_atref. 1, ref. 2101093_atref. 1, ref. 297240_g_at, 97241_at

Element information on the gene symbol ('Gene'), NCBI GeneID ('GeneID'), element type ('Element'), the position ('Start' and 'End') ona chromosome ('chr') of mouse and human ('Organism') and the sequence alignment of human-mouse RREs ('Sequence') are indicated.
Experimental evidence on clock-controlled genes ('Evidence') and Affymetrix probe ID detecting circadian expression of the genes('Affymetrix Probe ID') are also indicated. The canonical consensus sequences for the RRE ([A/T]A[A/T]NT[A/G]GGTCA) is indicatedin red. The function of these mouse RREs sequences in the context of a luciferase reporter was experimentally determined in Fig. S2.

2

Table S2. Functional clock-controlled elements used as train HMMs

Score of
HMMER

Gene

GeneID Element Organism chr

Start

End

Per1

18626

E-box 1Mouse

1168707450

68707433

15.39 AGGGAACACGTGCAGGCT

ref. 1

Per1

18626

E-box 2Mouse

1168707634

68707617

17.87 TAGAGCCACGTGAGGGCG

ref. 1

Per1

18626

E-box 3Mouse

1168710224

6871024111.48 TTTAGCCACGTGACAGTG

ref. 1

Per1

18626

E-box 4Mouse

1168710970

68710987

12.10 TAACGACACGTGGGCCCT

ref. 1

Per1

18626

E-box 5Mouse

1168711333

68711350

13.45 CAAGTCCACGTGCAGGGA

ref. 2Nr1d1217166

E-box 1Mouse

1198445288

98445271

14.71 CGGGCCCACGTGCTGCAT

ref. 2Nr1d1217166

E-box 2Mouse

1198445008

98444991

15.77 GGGTGCCACGTGCGAGGG

ref. 2

Nr1d2

353187

E-box 1Mouse

1414904545

14904528

16.38 ACTGGCCACGTGCACGGT

ref. 2

Nr1d2

353187

E-box 2Mouse

1414904442

14904425

17.71 CGGAGACACGTGAGGCCG

ref. 2

Dbp

13170

E-box

Mouse

7

33110149

33110166

15.23 CCTCGCCACGTGAGTCCG

ref. 2Bhlhb2

20893

E-box

Mouse

6

109049680 109049697

18.55 CCGGGCCACGTGAAGCGT

ref. 2

Rorc

19885

E-box

Mouse

3

94357502

94357519

16.45 AGGTGCCACGTGCACCAG

ref. 2

Sequence

Evidence

E-box
Per1

18626

D-box

Mouse

11

68711473

68711496

17.30 GCCTGGCATTATGCAACCCGCCTC

ref. 2

Per2

18627

D-box

Mouse

1

91377818

91377795

16.48 TGTGCGTCTTATGTAAAGAGAGCG

ref. 2

Per3

18628

D-box 1Mouse

4148930820 148930797

18.62 CCCGCGCGTTATGTAAGGTACTCG

ref. 2

Per3

18628

D-box 2Mouse

4148930773 148930750

16.10 GCCCGCGGTTATGTAACCCCCGCC

ref. 2Nr1d1217166

D-box

Mouse

11

9844519519.41 GGAGCTCATTATGTAACGAGGCCG

ref. 2984452181

Nr1d2353187

D-box

Mouse

14

14904456

14904479

17.41 AGCTCGCATTATGTAATGCTGCGT

ref. 2

Rora

19883

D-box 1Mouse

969195245

69195222

11.78 AAGCTGTTTTATGTAATAGCTTTG

ref. 2

Rora

19883

D-box 2Mouse

968926158

68926135

16.84 CACTGCTGTTATGTAACCAAACGT

ref. 2

Rora

19883

D-box 3Mouse

968894279

68894302

17.42 CGAGCGGGTTATGTAACAGGGTTA

ref. 2

Rorb

225998

D-box

Mouse

19

18304970

18304993

14.84 TCCAGTTCTTATGTAATGAATATA

ref. 2

D-box
Arntl

11865

RRE

Mouse

7

100478066 100478088

19.72 AGGCAGAAAGTAGGTCAGGGACG

ref. 2

Npas2

18143

RRE

Mouse

1

39516271

39516293

14.66 GAAAAATATGTAGGTCAGTGGAA

ref. 2

Nfil3

114519

RRE 1

Rat

17

18094293

18094271

15.64 AGTGTGTTAGTAGGTCAGTTCCG

ref. 2

Nfil3

18030

RRE 2

Mouse

13

52011542

52011520

21.79 ACAGAAAAAGTGGGTCAGTTTGT

ref. 2

Clock

12753

RRE

Mouse

5

75037420

75037442

15.91 AGGAATAAAGTGGGTCACAAGGC

ref. 2

Cry1

12952

RRE 1

Mouse

10

84829678

84829656

20.81 GACTAGAAAGTAGGTCATTGTGA

ref. 2

Cry1

12952

RRE 2

Mouse

10

84829601

84829623

16.47 GTTTCTAAAGTAGGTCATCGCTA

ref. 2

Rorc

19885

RRE

Mouse

3

94352279

94352301

19.40 GGAATAAAAGTGGGTCATCTTGT

ref. 2

Elov15

68801

RRE

Mouse

9

78317303

78317325

17.19 TCTTGTAAATTGGGTCATGGCGTThis study

BC004004

80748

RRE

Mouse

17

27851266

27851244

17.53 AGTCTGAATATAGGTCAATGTGAThis study

Macf1

11426

RRE 2

Mouse

4

121994185 121994207

20.05 CCCTGAAAAGTAGGTCAGTGCCT

This study

Atf5

107503

RRE

Mouse

7

32213618

32213640

14.65 CAAGTAAAACTGGGTCACGAAGGThis study

Col4a1

12826

RRE 1

Mouse

8

11309378

11309356

19.92 GGCAGGAAAATGGGTCAGTGCTG

This study

Col4a1

12826

RRE 2

Mouse

8

11337124

11337102

14.10 TCAGCCAAACTAGGTCAAAACCTThis study

D19Ertd721e

225896

RRE

Mouse

19

79728627972840

18.30 AGGAAGAAAATAGGTCAGACATG

This study

2

RRE
References
1. Hida A, et al. (2000) The human and mouse Period1 genes: five well-conserved E-boxes additively contribute to the enhancement of mPer1
transcription. Genomics 65:224-233.
and unpabsihed data of Ueda, H.R. et al.
2. Ueda HR, et al. (2005) System-level identification of transcriptional circuits underlying mammalian circadian clocks. Nat Genet 37:187-192.

Element information on the gene symbol ('Gene'), NCBI GeneID ('GeneID'), element type ('Element'), the position ('Start' and 'End') ona chromosome ('chr') of mouse and rat ('Organism') and the sequence ('Sequence') are indicated. Experimental evidence on functional
clock-controlled elements ('Evidence') and match score of HMMER search performed using the constructed HMMs ('Score of
HMMER') is also indicated. Canonical consensus sequences for the E-box (CACGTG), D-box (TTATG[T/C]AA) and RRE([A/T]A[A/T]NT[A/G]GGTCA) are indicated in red. Information contents of the relative frequency of each nucleic acid at the positionof the pattern for each element were shown by sequence logos.

3

Table S3. Predicted clock-controlled elements used for experimental validation

No.

Gene

GeneID

Element

FDR

-

Per1

18626

E-box

0.043

1

Esrra

26379

E-box

0.002

2

Foxd3

15221

E-box

0.014

3

MGI:1926224

268859

E-box

0.017

4

AK122525

331623

E-box

0.021

5

Lrrc35272589

E-box

0.021

6

1300001I01Rik

74148

E-box

0.021

7

Cpne7

102278

E-box

0.023

8

Pprc1

226169

E-box

0.024

Organism chr

Start

End

Mouse

11

68711333

68711350

Human

17

7996626

7996609

Mouse

1966358266635809Human

1163828852

63828869

Mouse

4

97986368

97986351

Human

1

63500197

64500214

Mouse

16

60322796032296Human

1664730136473030Mouse

1043388228

43388211Human

6107543034 107543051

Mouse

9

42544300

Human

11

120399989 120399972

Mouse

11

74262482

74262499

Human

17

2561349

2561332Mouse

8122590893 122590910

Human

16

88169660

88169677

Mouse

19

45397616

45397633

Human

10

103883430 103883447

142544317

Score of
HMMER

Sequence

13.45 Mouse CAAGTCCACGTGCAGGGA|| ||||||||||
16.13 Human CAGGTCCACGTGCGCCCG
18.03 Mouse CGGACGCACGTGGCCCCG||||||||||||||||||
18.03 Human CGGACGCACGTGGCCCCG
17.23 Mouse CGCGGTCACGTGGCCCCG| ||||||||||||||||
15.64 Human CACGGTCACGTGGCCCCG
14.92 Mouse AAGAGACACGTGCTGGGG||| ||||||||| || |17.35 Human AAGGGACACGTGCGGGCG
17.00 Mouse CCCCCGCACGTGGCGCCC||||| ||||||||||||
15.19 Human CCCCCCCACGTGGCGCCC
16.09 Mouse ACGTGACACGTGCGGCGG||||||||||||||||||
16.09 Human ACGTGACACGTGCGGCGG
15.91 Mouse CGGGGACACGTGCGCGCA||||||||||||||||||
15.91 Human CGGGGACACGTGCGCGCA
13.74 Mouse CCGAGACACGTGTGCCCG||||| |||||| |||||
17.87 Human CCGAGCCACGTGCGCCCG
15.71 Mouse CGGGTCCACGTGGGGGCG||||||||||||||||||
15.71 Human CGGGTCCACGTGGGGGCG

9

Jag2

16450

E-box

0.024

10

Rps19

20085

E-box

0.024

-

Per3

18628

D-box

0

1

Gria2

14800

D-box

0.051

2

Spry4

24066

D-box

0.122

3

Etv4

18612

D-box

0.181

4

Pogz

229584

D-box

0.181

5

Fmn2

54418

D-box

0.209

6

Plcb1

18795

D-box

0.325

7

Irf2bp1

272359

D-box

0.363

8

Slc22a20

381203

D-box

0.363

9

Trim8

93679

D-box

0.387

Mouse

12

107682267 107682284

Human

14

104704954 104704971

Mouse

7

13623927

13623944

Human

19

47056319

47056336

Mouse

4148930797 148930820

Human

1

7778455

7778432

Mouse

381194005

81193982

Human

4158501155 158501178

Mouse

18

39043332

Human

5141683774 141683751

Mouse

11

101455967 101455990

Human

17

38978809

38978832

Mouse

394832639

94832662

Human

1

148244037 148244014

Mouse

1

174628879 174628902

Human

1

236581260 236581283

Mouse

2

134535724 134535747

Human

20

80614738061496Mouse

710526613

10526636

Human

19

51081280

51081257

Mouse

19

5775101

5775078Human

1164737873

64737896

Mouse

19

45844359

45844336

239043309

15.64 Mouse CCTGGCCACGTGGGCGCG||||||||||||||||||
15.64 Human CCTGGCCACGTGGGCGCG
15.62 Mouse CGCGGCCACGTGCGAGCG||||||||||||||||||
15.62 Human CGCGGCCACGTGCGAGCG
18.62 Mouse CCCGCGCGTTATGTAAGGTACTCG|||||||||||||||| | | |21.11 Human CCCGCGCGTTATGTAACGCGCCCC
17.31 Mouse CGGGGCTGTTACATAATGCCCACC|||||||||||||||| |||||||
17.63 Human CGGGGCTGTTACATAACGCCCACC
16.38 Mouse AGGTGCGTTTACATAACGCCGGGC||||||||||||||||| || |||16.80 Human AGGTGCGTTTACATAACACCAGGC
17.09 Mouse CACACGTCTTATGTAACCCAGTTC|||||||||||| || |15.20 Human ACACGTTCTTATGTAACCGAGCCC
16.89 Mouse CCCCTGTGTTATGTAATCCCGCTC||| |||||||||||||| |15.15 Human CCCTTGTGTTATGTAATCTCTGCT
14.89 Mouse ACCGCGCATTATGCAAAGCGGCAG||||||||||||||||||||| |16.89 Human GCCGCGCATTATGCAAAGCGGCGG
15.25 Mouse GGGGCGCGTTATGCAATGGGGCGC||||||||||||||||||||
15.25 Human GGGGCGCGTTATGCAATGGGCGCA
16.21 Mouse CCCGCGCGTTATGTAACTTTCCCT| ||| |||||||||||||||||
13.67 Human CAGGCGTGTTATGTAACTTTCCCT
14.95 Mouse CTGCCTTTTTACATAAGGCCTGGG| |||| | ||||||| ||| |||14.81 Human CGGCCTCTCTACATAAAGCCGGGG
14.62 Mouse GACACTCATTACATAAACAGCAGC||||||||||||||||||||||||
Human GACACTCATTACATAAACAGCAGC

10Tspan7

21912

D-box

0.388

-

Arntl

11865

RRE

0.066

1

Atp1b2

11932

RRE

0.066

2

Spsb3

79043

RRE

0.083

3

Zfp318

57908

RRE

0.104

4

Mgea5

76055

RRE

0.112

5

Lasp1

16796

RRE

0.139

6

Ryr1

20190

RRE

0.148

7

Adam12

11489

RRE

0.148

8 A730009L09Rik

402727

RRE

0.168

9

17912

RRE

0.179

Myo1b

Human

10

104395331 104395308

Mouse

X

88175838817560

Human

X

38177725

38177702

Mouse

7100478066 100478088

Human

11

13255933

13255955

Mouse

11

69217609

69217631

Human

17

74958787495856

Mouse

17

23484948

23484970

Human

16

1772556

1772534Mouse

1744564473

44564495

Human

643417992

43417970

Mouse

19

45123691

45123713

Human

10

103568543 103568565

Mouse

11

97476169

97476191

Human

17

34284865

34284887

Mouse

7

18096374

18096352

Human

19

43616749

43616771

Mouse

7121856769 121856791

Human

10

128077170 128077148

Mouse

16

84900535

84900513

Human

21

25632353

25632331

Mouse

152270010

52269988

3

14.62
13.68 Mouse AGAGGGTCTTACATAAGCCAGGGG||||||||||||||||||| ||||
15.27 Human AGAGGGTCTTACATAAGCCGGGGG
20.38 Mouse AGGCAGAAAGTAGGTCAGGGACG|||||||||||||||||||||||
20.38 Human AGGCAGAAAGTAGGTCAGGGACG
20.61 Mouse GCGGAGAAAGTAGGTCACTGCCG|||||||||||||||||| ||||
19.61 Human GCGGAGAAAGTAGGTCACAGCCG
18.81 Mouse GAACGGAAAGTGGGTCAGCGCCG| ||||||||||||||| ||||
19.64 Human GGGCGGAAAGTGGGTCAGGGCCG
19.35 Mouse GAGAGAAAAGTGGGTCATTGAGA|||||||||||||||||||||
18.25 Human AAGAGAAAAGTGGGTCATTGAGC
17.41 Mouse ATCTCCAAAGTAGGTCAGTGTCT| ||||||||||||||
20.12 Human CCATAGAAAGTAGGTCAGTTCTT
16.54 Mouse TTCGTGAAAGTGGGTCATGGTCT| | ||||||||||||||||||
19.34 Human TGTGAGAAAGTGGGTCATGGTCT
17.94 Mouse AGGCTCTGACCTATTTAAATTCT|| ||||||||||||||||||||
17.45 Human AGACTCTGACCTATTTAAATTCT
15.44 Mouse TACTTAAAAGTAGGTCAGAAAAA|||| |||||||||||| || |19.88 Human AACTTGAAAGTAGGTCAGTAAGA
16.17 Mouse TCTTAATGACCCAATTTCTAAAT||||||||||||| |||||||||
18.49 Human TCTTAATGACCCACTTTCTAAAT
18.02 Mouse TGGTGCTGACCCACTTTCCTCTT| | ||||| ||||||| |||Human GGATATTGACCTACTTTCCCCTT

10

D13Ertd150e

52548

RRE

0.203

1

Shox2

20429

low-score
E-box-

2 1700057H15Rik

78460

low-score
E-box

-

3

-

435253

low-score
E-box

-

4

Tbx15

21384

low-score
E-box

-

5

Stk4

58231

low-score
E-box

-

6

Ube1x

22201

low-score
E-box

-

7

-

211823

low-score
E-box

-

8

Rnf44

105239low-score
E-box

-

9

Pabpc1

18458

low-score
E-box

-

10

-

-

low-score
E-box

-

Human

2

191953461 191953483

16.47

Mouse

13

37218548

37218570

Human

669978556997877

Mouse

367055808

67055791

Human

3159121615 159121598

Mouse

4

122900755 122900738

Human

1

38322547

38322564

Mouse

11

35821433

35821450Human

5167485618 167485601

Mouse

3

99126851

Human

1119301655 119301672

Mouse

2

164147516 164147533

Human

20

43041498

43041515

Mouse

X

19009993

19009976

Human

X

46825349

46825332

Mouse

3

129597003 129596986

Human

4

112078186 112078203

Mouse

13

53770222

Human

5

175887078 175887095

Mouse

15

36663158Human

8101802352 101802335

Mouse

16

74421611

74421628

Human

377581572

77581555

16.99 Mouse GATGCGAAAGTGGGTCAGGAATG|| ||||||||||||||||||||
16.67 Human GACGCGAAAGTGGGTCAGGAATG
0.64 Mouse GATAAACACGTGTGTATC||||||||||||| ||||
0.64 Human GATAAACACGTGTATATC
1.39 Mouse TATTCTCACGTGATAAAC||||||||||||||||||
1.39 Human TATTCTCACGTGATAAAC
1.83 Mouse GACAAGCACGTGCCAGAC||||||||||||||||||
1.83 Human GACAAGCACGTGCCAGAC
1.83 Mouse GTTTTTCACGTGCTTGAC|||||||||||| ||||
2.39 Human GTTTTTCACGTGTTTGAT
2.12 Mouse GAAGAGCACGTGATCTGC||| | |||||| | |||2.28 Human GAAAACCACGTGGTTTGC
2.61 Mouse GTTACTCACGTGAGGTAC||||||||||||||||||
2.61 Human GTTACTCACGTGAGGTAC
2.80 Mouse TGCAAACACGTGATTTCC||||||||||||||||||
2.80 Human TGCAAACACGTGATTTCC
2.80 Mouse GTAAAACACGTGGATTTT||||||||||||| ||||
2.80 Human GTAAAACACGTGGGTTTT
2.93 Mouse AAATACCACGTGTTGAAC||||||||||||||||||
2.93 Human AAATACCACGTGTTGAAC
3.03 Mouse GCTAAGCACGTGGAAGTC||||||||||||||||||
3.03 Human GCTAAGCACGTGGAAGTC

499126834

53770239

36663141

11

C330011F03

328837

low-score
E-box

--

12

Prkce

18754

low-score
E-box

-

13

Map3k9338372

low-score
E-box

-

14

Zfhx1b24136

low-score
E-box

-

15

Ppfibp2

19024

low-score
E-box

-

Mouse

17

49541248

49541231

Human

317954468

17954451

Mouse

17

85008129

85008146

Human

246068028

46068045

Mouse

12

77032273

77032290

Human

14

70276648

70276665

Mouse

245057246

45057229

Human

2145095987 145095970

Mouse

7

94860226

94860243

Human

11

74893627489379

3.08 Mouse GTTTCCCACGTGTTTGGC||||||||||||||||||
3.08 Human GTTTCCCACGTGTTTGGC
3.20 Mouse ATATAACACGTGCTAAAA||||||||||||||||||
3.20 Human ATATAACACGTGCTAAAA
2.44 Mouse GACAAACACGTGTGCGTC||||||||||||| |4.03 Human GACAAACACGTGTATGCA
3.29 Mouse GTATTACACGTGAAAAGC||||||||||||||||||
3.29 Human GTATTACACGTGAAAAGC
4.93 Mouse GTTTCCCACGTGTGTCCC||||||||||||| | |1.76 Human GTTTCCCACGTGTTTGTCThe element information including the gene symbol ('Gene'), NCBI GeneID ('GeneID'), element type ('Element'), the position ('Start'and 'End') on a chromosome ('chr') of mouse and human ('Organism'), the sequence alignments of human and mouse ('Sequence') andthe match score of HMMER search ('Score of HMMER') are indicated. The estimated accuracy of the HMM-based prediction isindicated as false discovery rate ('FDR'). The mouse elements were used for experimental validation. The element type ‘low-score Ebox’ indicate evolutionary conserved 15 most low-score E-boxes with the core consensus sequence ‘CACGTG’ in non-coding regions.
The function of these element sequences in the context of a luciferase reporter was experimentally determined in SI Fig. S3.

5

Table S4. Similarity between DNA binding domains of DNA binding activators and repressors

Element

E-box

Type

Homology

NP_031515.1
NP_031741.1
NP_035628.1

Domain Domain
position
name71-12632-86 HLH
50-112

Activator
Activator
Repressor

Identity 30%, Similarity 57% to Bhlhb2
Identity 22%, Similarity 55% to Bhlhb2-

NP_058670.1
NP_059069.1

254-307bZIP_272-123

Activator
Repressor

Identity 44%, Similarity 69% to Nfil3-

NP_038674.1
NP_663409.2

71-146132-207

Activator
Repressor

Identity 65%, Similarity 81% to Nr1d1

Gene

Alignment

Accession

Arntl
Clock
Bhlhb2-KNAREAHSQIEKRRRDKMNSFIDELASLVPTCNAM--SRKLDKLTVLRMAVQHMKTLR---DKAKRVSRNKSEKKRRDQFNVLIKELGSMLPGN-----ARKMDKSTVLQKSIDFLRKHKE--KETYKLPHRLIEKKRRDRINECIAQLKDLLPEHLKLTTLGHLEKAVVLELTLKHVKALTNLID: : .:**:***::* * :* .::*:::* .**. ::..::
KDEKYWSRRYKNNEAAKRSRDARRLKENQISVRAAFLEKENALLRQEVVAVRQE
KDAMYWEKRRKNNEAAKRSREKRRLNDLVLENKLIALGEENATLKAELLSLK-** **.:* **********: ***:: :. :* :*** *: *:::::
IPCKICGDKSSGIHYGVITCEGCKGFFRRSQQSNATYS-CPRQKNCLIDRTSRNRCQHCRLQKCLAVGMSRDAVKFG-LCKVCGDVASGFHYGVHACEGCKGFFRRSIQQNIQYKRCLKNENCSIVRINRNRCQQCRFKKCLSVGMSRDAVRFG**:*** :**:**** :*********** *.* *. * :::** * * .*****:** ::***:********:**

D-box

Dbp
Nfil3

RRE

Rora
Nr1d1

zf-C4

The gene symbol ('Gene') of DNA binding activators and repressors, target element ('Element'), alignment ('Alignment') of DNA binding domain, accession No. ('Accession') ofprotein sequence, position of DNA binding domain ('Domain position') at the protein sequence and name of the DNA binding domain ('Domain name') are indicated. Homology('Homology') between DNA binding domains of DNA binding activators and repressors is also indicated.