CLOCK-controlled polyphonic regulation of circadian rhythms through canonical and noncanonical E-boxes.

CLOCK-Controlled Polyphonic Regulation of Circadian Rhythms through Canonical and Noncanonical E-Boxes Hikari Yoshitane,a Haruka Ozaki,b Hideki Terajima,a Ngoc-Hien Du,a Yutaka Suzuki,c Taihei Fujimori,a Naoki Kosaka,a Shigeki Shimba,d Sumio Sugano,c Toshihisa Takagi,b Wataru Iwasaki,b,e Yoshitaka Fukadaa

In mammalian circadian clockwork, the CLOCK-BMAL1 complex binds to DNA enhancers of target genes and drives circadian oscillation of transcription. Here we identified 7,978 CLOCK-binding sites in mouse liver by chromatin immunoprecipitationsequencing (ChIP-Seq), and a newly developed bioinformatics method, motif centrality analysis of ChIP-Seq (MOCCS), revealed a genome-wide distribution of previously unappreciated noncanonical E-boxes targeted by CLOCK. In vitro promoter assays showed that CACGNG, CACGTT, and CATG(T/C)G are functional CLOCK-binding motifs. Furthermore, we extensively revealed rhythmically expressed genes by poly(A)-tailed RNA-Seq and identified 1,629 CLOCK target genes within 11,926 genes expressed in the liver. Our analysis also revealed rhythmically expressed genes that have no apparent CLOCK-binding site, indicating the importance of indirect transcriptional and posttranscriptional regulations. Indirect transcriptional regulation is represented by rhythmic expression of CLOCK-regulated transcription factors, such as Krüppel-like factors (KLFs). Indirect posttranscriptional regulation involves rhythmic microRNAs that were identified by small-RNA-Seq. Collectively, CLOCKdependent direct transactivation through multiple E-boxes and indirect regulations polyphonically orchestrate dynamic circadian outputs.

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any aspects of behavior and physiology, including sleep/ awake cycles and hormone levels, keep a rhythm with about a 24-h period, even under constant conditions without any external time cues (1). Circadian rhythms are generated by a self-sustaining time-measuring system called the circadian clock. In mammals, the hypothalamic suprachiasmatic nucleus (SCN) functions as the master clock, and circadian clocks are also located in peripheral tissues such as the liver (2–5). In individual cells, clock genes and their products form transcriptional/translational feedback loops (6). The basic helix-loop-helix (bHLH)–PAS transcription factors CLOCK and BMAL1 play a role as positive factors in the loops, and the heterodimer of these proteins binds to the CACGTG E-box or related E-box-like sequences to transactivate a wide range of target genes, including Per and Cry (7–10). Translated PER and CRY proteins then bind to the CLOCKBMAL1 complex, leading to the suppression of E-box-dependent transactivation. This negative-feedback mechanism forms a molecular clock generating circadian rhythms. In addition to the Ebox element, the D-box element and the REV-ERB/ROR-binding element (RRE) form a regulatory network of gene expression, governing coordinately circadian transcriptional oscillations (11, 12). The D-box element is activated and repressed by DBP and E4BP4, respectively, while RRE is activated and repressed by RORs and REV-ERBs, respectively. During the circadian cycling of the transcriptional/translational steps, posttranslational modifications, such as phosphorylation, regulate the clock proteins, in terms of activity, stability, localization, and interaction (13). It was reported previously that CLOCK and BMAL1 are phosphorylated in a time-of-day-dependent manner (14–17). CLOCK phosphorylation at its DNA-binding domain (16, 18) may be important for rhythmic inhibition of the ability of the CLOCK-BMAL1 complex to bind to the E-box

element. This is consistent with the observation that the CLOCKBMAL1 complex rhythmically dissociates from the E-box in the locus of the Dbp gene (19). Here we found in vivo binding sites of CLOCK protein in the mouse liver in a genome-wide manner by chromatin immunoprecipitation-sequencing (ChIP-Seq) analysis. Previous ChIP-Seq studies of circadian clocks confirmed CLOCK-BMAL1 binding to canonical motifs instead of finding all potential binding motifs (20–23). In this study, significant CLOCK-binding motifs were comprehensively examined by developing a bioinformatics method, MOCCS (motif centrality analysis of ChIP-Seq), which analyzes the frequency distribution of DNA sequences centered at DNA-binding sites found by ChIPSeq analyses. In parallel, all the rhythmic transcripts in the liver were identified by circadian deep-sequencing analysis of poly(A)tailed RNA and small RNA. Based on these data, we demonstrate the functional importance of rhythmic posttranscriptional regulations, such as microRNA (miRNA)-mediated gene silencing, in dynamic circadian RNA rhythms.

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Received 1 November 2013 Returned for modification 4 December 2013 Accepted 22 February 2014 Published ahead of print 3 March 2014 Address correspondence to Wataru Iwasaki, [email protected], or Yoshitaka Fukada, [email protected]. H.Y., H.O., and H.T. contributed equally to this work. Supplemental material for this article may be found at http://dx.doi.org/10.1128 /MCB.01465-13. Copyright © 2014, American Society for Microbiology. All Rights Reserved. doi:10.1128/MCB.01465-13

May 2014 Volume 34 Number 10

Downloaded from http://mcb.asm.org/ on March 10, 2015 by UCSF Library & CKM

Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japana; Department of Computational Biologyb and Department of Medical Genome Sciences,c Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan; Department of Health Science, School of Pharmacy, Nihon University, Funabashi, Chiba, Japand; Genetic Research Section, Center for Earth Surface System Dynamics, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba, Japane

Heterogeneity of E-Boxes and Their Complex Outputs

MATERIALS AND METHODS


CCGCT), Dbp-Rv (GCTCC AGTAC TTCTC ATCCT TCTGT), Klf11-Fw (AAGCT CATCT TCGCA CTCAC), Klf11-Rv (AACTT CTTGT CACAG CCGTC), Klf13-Fw (GGGAA ATCTT CGCAC CTC), Klf13-Rv (ATAGT GCCGT GCCAG CTC), 0610005C13Rik-Fw (TCCAT CTATG ACACC GCTTG), 0610005C13Rik-Rv (GCGTT TACTG TTGTG CGTTC), primir-802-Fw (GATGA GAGGA CGCTG TTCGC), and pri-mir-802-Rv (CGCTT ATCCA CGAGA AACGC). Dual-luciferase reporter assay. HEK293T cells in 12-well plates were transiently transfected with 200 ng Myc-CLOCK/pSG5 and 200 ng BMAL1/pcDNA3.1 in combination with 20 ng of a firefly luciferase plasmid that harbors one of the various E/E=-box elements (underlined in sequences) as a reporter and 2 ng (see Fig. 3) or 1 ng (see Fig. 5 to 7) of renilla luciferase plasmid (pRL-SV40) as an internal control. Reporter plasmids were constructed as described previously (26). The inserted sequences were as follows: 5=-CCCGG ACCAA AACAC GTGCC GCCGC CCGCC-3= for Klf11, 5=-TACAG GCACC TGCTG CTCCA TGTGC TT CTG-3= for Klf13, 5=-CTGAG CCAGG TGTGG TGGCA CACGT GCCTG TC-3= for 0610005C13Rik, 5=-CCTCC TCAGG GTCAC GTGCG CGCTG GGCGG-3= for mir-148a, 5=-CGGGT TGGGA AACAC GTGCT GGGAT TCAGT-3= for mir-150, 5=-GGACG CTGTT CGCAC GTGCC TGGGG TGTCC-3= for mir-802-proximal, and 5=-TTATT TAGCT CTCAC GTGGA ATTTG TTAGA-3= for mir-802-distal. In Fig. 3, five nucleotides, CGCGT, at the 5= side of the NheI site in the multicloning site were deleted from the pGL3 promoter vector. This deletion eliminated CLOCK-BMAL1-dependent activation of the basal activity from the pGL3 vector. The modified vector was named pGL3N. The inserted sequ ences were as follows: 5=-GTTGC ATAAG CTCAT GCGGC ATTCT GC AAG-3= (in the Btbd19 locus) for CATGCG#1, 5=-CTGAT CACAG TGCAT GCGTG CCGTG TGCCA-3= (in the Fam176a locus) for CATGCG#2, and 5=-ACCCA CCTCC ACTAC GTATG TGCAC ACCA C-3= (in the Nfe2 locus) for TACGTA#1. The total amount of DNA was adjusted by addition of the empty expression plasmids. The transfected cells were collected 48 h after transfection and subjected to the dual-luc iferase assay by luminometry (Promega), according to the manufacturer’s protocol. An internal control was used to normalize the transfection effic iency. For testing posttranscriptional regulation of Cav1 by miR-802, HEK293T cells in 12-well plates were transiently transfected with 1,000 ng miR-802/pcDNA3.1 and 100 ng Cav1-3=UTR/pGL3 in combination with 20 ng of renilla luciferase plasmid (pRL-SV40) as an internal control. To construct a plasmid expressing miR-802, the genomic fragment containing the miR-802 precursor was amplified from the mouse genome and cloned into pcDNA3.1 digested by BamHI and XhoI. The Cav1 3= untranslated region (UTR) luciferase reporter vector was generated by inserting the Cav1 3=-UTR fragment into the pGL3-promoter vector (downstream of the firefly luciferase). The cloning primers were as follows: mir-802 cloning Fw (5=-CGGGA TCCGC AGAGA CGGAA GAGGA TAC-3=), mir-802 cloning Rv (5=-CCGCT CGAGT TACTA TGTCA GAAGG CAGCG-3=), Cav1-3=UTR cloning Fw (5=-GCCGT GTAAT TCTAG TCAAG GATGA AAGGT TTTTT TCCC-3=), and Cav13=UTR cloning Rv (5=-CCGCC CCGAC TCTAG TTATA GGACA TGCAG CATAA AAAAG TG-3=). Sequencing. For wild-type (WT) mice, immunoprecipitated DNA and purified RNA samples were sequenced on an Illumina GA IIx sequencer (36 bp, single end). For Bmal1-KO mice and WT littermates, purified RNA samples were sequenced on a HiSeq 2000 instrument (125 bp for Bmal1-KO mice and 101 bp for WT littermates, paired end), according to the manufacturer’s protocol. ChIP-Seq data analysis. The sequenced tags were mapped to the mouse genome by using Burrows-Wheeler Aligner version 0.5.9 (27) with default parameters. Genome Positioning System (GPS) version 1.0 (28) was used for peak calling with the options “⫺s 2100000000 ⫺nrf ⫺q 2” (q value of ⬍0.01). GPS accurately predicts protein-binding sites from ChIP-Seq data at single-base resolution by using the expectation-maximization algorithm. For each predicted binding site, GPS uses control data to

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Animals. The animal experiments were approved by the animal ethics committee of The University of Tokyo. C57BL/6J mice and Bmal1-knockout (KO) mice (C57BL/6J background) (24) were individually housed in cages with free access to food and water. Six-week-old male mice were entrained to 12-h light and 12-h dark (LD) cycles for at least 2 weeks in a light-tight chamber at a constant temperature (23°C ⫾ 1°C). In some experiments, the LD-entrained mice were transferred to constant darkness, and on the second day, the mice were sacrificed for isolation of liver. Chromatin immunoprecipitation. Nuclear extracts of mouse liver were prepared as described previously (16, 25), with minor modifications. Briefly, mouse livers rinsed with ice-cold phosphate-buffered saline (PBS) were homogenized with ice-cold buffer A (10 mM HEPES-NaOH, 10 mM KCl, 0.1 mM EDTA, 1 mM dithiothreitol [DTT], 1 mM phenylmethylsulfonyl fluoride [PMSF], 4 ␮g/ml aprotinin, 4 ␮g/ml leupeptin, 50 mM NaF, and 1 mM Na3VO4 [pH 7.8]). The homogenate was centrifuged (5 min at 700 ⫻ g, twice), and the precipitate (nuclear fraction) was crosslinked with 1% formaldehyde in buffer A for 10 min at 25°C. The crosslinking reaction was ceased by the addition of 125 mM glycine (final concentration). The sample was then centrifuged (5 min at 700 ⫻ g), and the nuclear pellet was washed twice with buffer A and resuspended in IPB2 buffer (20 mM HEPES-NaOH, 137 mM NaCl, 1 mM EDTA, 5% glycerol, 1% Triton X-100, 1.67 mM MgCl2, 1 mM DTT, 1 mM PMSF, 4 ␮g/ml aprotinin, 4 ␮g/ml leupeptin, 50 mM NaF, and 1 mM Na3VO4 [pH 7.8]) supplemented with 1% sodium dodecyl sulfate (SDS). The sample was then sonicated 16 times for 20 s each, with intervals of 40 s (Branson Sonifier 450, set at a 50% duty cycle, 5 output), and frozen with liquid nitrogen. After being thawed, the sample was centrifuged at 20,000 ⫻ g for 30 min at 4°C, and the supernatant was then diluted in IPB2 buffer (final concentration, 0.1% SDS). The sample was incubated with protein GSepharose 4 Fast Flow (Amersham Biosciences) for 30 min at 4°C with gentle rotation and then centrifuged for 5 min at 4,000 rpm. The precleared supernatant was mixed with CLSP4 anti-CLOCK antibody by gentle rotation for 2 h at 4°C. Protein G-Sepharose 4 Fast Flow was then added to the mixture, and it was mixed by gentle rotation for 1 h at 4°C. The beads were sequentially washed with the following buffers: once in IPB2 buffer, once in IPB2 buffer supplemented with 500 mM NaCl, once in LiCl buffer (0.25 M LiCl, 1% NP-40, 1% deoxycholate, 1 mM EDTA, 10 mM Tris-HCl [pH 8.0]), and twice in TE buffer (10 mM Tris-HCl, 1 mM EDTA [pH 8.0]). Finally, the beads were eluted twice with 250 ␮l of the elution buffer (1% SDS, 0.1 M NaHCO3) by rotation for 15 min at room temperature. The combined eluate (500 ␮l) was mixed with 20 ␮l of 5 M NaCl and incubated overnight at 65°C. The sample was then mixed with 10 ␮l of 0.5 M EDTA, 20 ␮l of 1 M Tris-HCl (pH 6.5), and 2 ␮l of 10 mg/ml proteinase K, and the mixture was incubated for 2 h at 45°C. The DNA was purified by extraction with phenol-chloroform-isoamyl alcohol (25:24:1) and ethanol precipitation. The final preparation was used as the ChIP sample. Quantitative PCR. ChIP DNA samples were subjected to real-time PCR (Applied Biosystems) using GoTaq master mix (Promega) with the following gene-specific primers: FU-Fw (CCTCC TTCTC CACGT CCTGA T), FU-Rv (GGTGA GAAAG GACAA GGGAT GT), Up-Fw (ACACC CGCAT CCGGT AGC), Up-Rv (CCACT TCGGG CCAAT GAG), I1-Fw (ATGCT CACAC GGTGC AGACA), I1-Rv (CTGCT CAGGC ACATT CCTCA T), I2-Fw (TGGGA CGCCT GGGTA CAC), I2-Rv (GGGAA TGTGC AGCAC TGGTT), E4-Fw (AAGAA CAATG AAGCA GCCAA GAG), E4-Rv (GGCAG CCCGC ACAGA TAT), CATGCG#1-Fw (CGTAG AGGAA TGATC GACTT GC), and CATGCG#1-Rv (GCTTG GGTGA TGTCC TGAG). For reverse transcription-quantitative PCR (qRT-PCR), the total RNA prepared at circadian time 2 (CT2), CT6, CT10, CT14, CT18, and CT22 (16) was reverse transcribed by using Go Script reverse transcriptase (Promega) with both an anchored (dT)15 primer and a random oligonucleotide primer. The cDNA was subjected to quantitative PCR (qPCR) analysis with the following gene-specific primers: Dbp-Fw (AATGA CCTTT GAACC TGATC

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FIG 1 CLOCK ChIP-PCR analysis of mouse liver. (A) Schematic structure of the mouse Dbp gene. The FU, UP, I1, I2, and E4 primer sets were designed to amplify five regions in the Dbp locus, as previously reported (19). (B) ChIP samples were prepared from mouse liver at ZT8 with the indicated antibodies. CLSP1 to CLSP12 and B1BH2 are anti-CLOCK and anti-BMAL1 antibodies, respectively (16). An antirhodopsin antibody, 1D4, was used as a control. (C) ChIP samples were prepared at 3-h intervals over a day and were subjected to PCR analysis with the indicated primer sets. The rhythmic DNA binding of CLOCK was reproducibly observed in three independent experiments. (D) ChIP samples prepared from wild-type and Bmal1-deficient mice at ZT8 were subjected to PCR analysis with the indicated primer sets. Filled and open bars indicate data with CLSP4 and 1D4 antibodies, respectively.

Nucleotide sequence accession numbers. Illumina sequencing data for the ChIP-Seq, RNA-Seq (WT at 8 time points), small-RNA-Seq, and RNA-Seq (Bmal1-KO mice and WT littermates at 4 time points) are available in the DDBJ/EBI/NCBI databases under accession numbers DRP001092, DRP001093, DRP001094, and DRP001349, respectively.

RESULTS

Circadian binding of CLOCK protein to the E-box. We previously generated a series of anti-CLOCK monoclonal antibodies (MAbs), CLSP1 to CLSP13, which are characterized by their epitopes and binding affinities for CLOCK (16). For ChIP analysis, these antibodies were examined for efficiency of precipitation of known E-box-containing DNA fragments in the Dbp locus (19). Among them, CLSP4 precipitated the largest amount of DNA fragments from mouse liver lysates (Fig. 1A and B). On the other hand, smaller amounts of the DNA fragments were precipitated by CLSP1 and CLSP8, which bind to the E19 region encoded



calculate the P value, which is adjusted for multiple testing by a Benjamini-Hochberg correction. In this study, ChIP-Seq data with 1D4 control IgG were used as the control data. The mapped tags were visualized by using Integrative Genomics Viewer (29). The CLOCK-binding sites were defined by using zeitgeber time 8 (ZT8) or CT8 results, because CLOCK binds to DNA most strongly at ZT8 or CT8. RNA preparation. Total RNA was prepared from mouse liver by using TRIzol reagent (Invitrogen), according to the manufacturer’s protocol, and was treated with DNase I before being used for RNA-Seq experiments. Poly(A)-tailed RNA and small RNA were isolated from total RNA according to the manufacturer’s protocol. Reference sequence and annotation data. The mouse genome sequence was obtained from UCSC Genome Browser (mm9) (http: //genome.ucsc.edu/). The annotated gene models (NCBIM37) and the annotations of snRNAs, snoRNAs, and rRNAs were taken from Ensembl (release 64) (http://www.ensembl.org/). The annotations of tRNAs were retrieved from GTRNADB (http://gtrnadb.ucsc.edu/) (30). The mouse precursor and mature miRNA sequences were downloaded from miRBase (release 18) (http://www.mirbase.org/) (31). RNA-Seq data analysis. MapSplice version 1.15.2 (32) was used for mapping of RNA-Seq data (WT at 8 time points), and Cufflinks version 2.0.0 (33) was used for quantifying the expression level of each gene as fragments per kilobase of exon per million fragments (FPKM). A gene was defined as an expressed one if the sum of its FPKM values across the eight time points was ⬎5. A gene was defined as a rhythmically expressed one if its maximal and minimal expression values were significantly different (q value of ⬍0.1), and its expression profile was fitted with cosine curves (P value of ⬍0.01). The q values were calculated by Cuffdiff (33). An inhouse R script was applied to estimate periods and phases of gene expression profiles by fitting curves using the equation Acos[2␲(t ⫹ t0)/T] ⫹ B, where A and B are standard deviations and means of the gene expression profiles, respectively, T ranges from 23 to 25 h with increments of 0.1 h, and t0 ranges from 0 to 23.9 h with increments of 0.1 h. The sequenced tags of RNA-Seq data of Bmal1-KO mice (4 time points) were trimmed from the 3= ends so that their lengths became the same as those of their WT littermates (i.e., 101 nucleotides [nt]) by using a Perl script. TopHat2 version 2.0.6 (34) was used to map RNA-Seq data of Bmal1-KO mice and WT littermates with default parameters. Cuffdiff 2 version 2.0.2 (33) was used to detect differentially expressed genes between Bmal1-KO mice and WT littermates at each time point (q value of ⬍0.1). Small-RNA-Seq data analysis. Because the lengths of the sequenced tags could be longer than the lengths of mature miRNAs, adaptor sequences were included on the 3= side of the sequenced tags, and hence, they were trimmed by cutadapt (35). Bowtie version 0.12.7 (36) was used for mapping, and unmapped tags were discarded. From different premiRNA sequences, the same sequences of mature miRNAs can be generated, and 1,141 sequences were unique among 1,157 mature miRNAs stored in miRBase (release 18). The tag numbers at various time points were normalized, as the total tag number mapped to the mouse genome at each time point was the same as the number at CT2. A miRNA was determined to be “expressed” if the sum of its normalized tag numbers across the 8 time points exceeded 100. Gene ontology analysis. Gene ontology (GO) annotations of the mouse genes were retrieved from Ensembl BioMart. For performing a hypergeometric test to identify overrepresented gene ontology terms, an R package, “GOstats,” was used with a P value cutoff of 0.01. Genes that were assigned to each of the following GO terms were designated “transcription factors”: GO:0000122, GO:0000978, GO:0000981, GO: 0000982, GO:0000983, GO:0000988, GO:0001010, GO:0001011, GO: 0001071, GO:0001074, GO:0001075, GO:0001077, GO:0001078, GO:0001133, GO:0001190, GO:0001200, GO:0001201, GO:0001205, GO: 0001206, GO:0001227, GO:0001228, GO:0003700, GO:0003705, GO: 0004879, GO:0006355, GO:0006357, GO:0038050, GO:0038052, GO:0044212, and GO:0045944.



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FIG 2 CLOCK ChIP-Seq analysis of mouse liver. (A) CLOCK ChIP-Seq data for the Dbp locus showing three rhythmic binding sites under LD (right) and DD (left) conditions. These sites correspond to UP, I1, and I2 in Fig. 1A. (B) Overlap of CLOCK-binding sites at CT8 in this study with those in a previous study (22). An overlap was called if CLOCK-binding sites at CT8 were within 120 bp of the peak summits in the previous study. (C) Heat maps of the sequenced tags around the identified CLOCK-binding sites under LD and DD conditions. The sites were ordered by the tag number for the data sets at ZT8 and CT8. For normalization, the numbers of sequenced tags were divided by the root mean square for each row in the maps. (D) Overrepresented CLOCKbinding motif determined by MEME. DNA sequences in the window of ⫾80 bp around each CLOCK-binding site were used for sequence analysis. (E) Frequency histogram of the spacer lengths of tandem CLOCK-binding motifs (CACGNG, CACGTT, and CATG[C/T]G) in the window of ⫾40 bp around each CLOCK-binding site.

This method takes advantage of the fact that significant DNAbinding motifs of transcription factors should frequently appear around their binding sites identified by ChIP-Seq. For example, a histogram of the appearance of CACGTG or CACGCG around the CLOCK-binding sites showed a sharp peak (Fig. 3A). To quantify the sharpness of the peak, a cumulative relative frequency curve was drawn for every 6-mer (Fig. 3B), and the area under the curve (AUC) was calculated. It should be noted that when a peak becomes sharper, the AUC becomes larger. MOCCS revealed motifs significantly concentrated in the CLOCK-binding sites (Fig. 3B and Table 1). Reasonably, the motif with the largest AUC was the canonical E-box motif CACGTG in both the CT8 and ZT8 data sets, and the second was CACGTT

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by CLOCK exon 19 (16), a domain that interacts with CIPC (37) and MLL1 (38). CLSP4-based ChIP analysis showed rhythmic DNA binding of CLOCK protein under constant dark (DD) conditions to the following E-box-containing regions of the Dbp gene: upstream (UP), the first intron (I1), and the second intron (I2) (Fig. 1C). On the other hand, faint binding signals were detected in the region distant from the E-box sequences, far upstream (FU) (Fig. 1C). An irrelevant monoclonal antibody, 1D4, to rhodopsin (39) displayed constantly minimal signals (Fig. 1B and C). Importantly, the CLOCK-binding signals in the Dbp locus were minimized in the ChIP analysis using Bmal1-KO mice (Fig. 1D), supporting the quantitative nature of the analysis. Genome-wide analysis of CLOCK-binding sites in mouse liver. To obtain a genome-wide map of CLOCK target sites, CLOCK-ChIP DNA fragments were prepared from mouse liver isolated at ZT8 and ZT20 (ZT is zeitgeber time, a term that represents biological time in the 12-h-light/12-h-dark [LD] cycle, in which ZT0 and ZT12 correspond to the lights-on and lights-off times, respectively) and were subjected to analysis using the Genome Analyzer IIx deep sequencer (Illumina), which yielded ⬎20 million tags for each time point (see Table S1 in the supplemental material). These tags were mapped onto the mouse genome, allowing at most two mismatches, which identified 5,801 CLOCKbinding sites at ZT8 (see Table S1 in the supplemental material). CLSP4-based CLOCK-ChIP samples prepared at 8 time points across the day under DD conditions were subjected to ChIP-Seq analysis, which identified 7,978 CLOCK-binding sites at CT8 (CT is circadian time, a term that represents time under DD conditions, with CT0 corresponding to the lights-on time in the previous LD cycle) (see Table S2 in the supplemental material). Among them, 2,400 sites of CLOCK binding were also detected in a previous study (22). Typically, strong peaks of CLOCK-ChIP tags were detected at the three positions UP, I1, and I2 in the Dbp locus (Fig. 2A). In addition to these established sites, the strictly selected anti-CLOCK monoclonal antibody (Fig. 1B) allowed us to identify many novel CLOCK-binding sites in the present study (Fig. 2B). Overall, the heat maps showed that almost all the binding sites exhibited a day-night variation as well as a circadian change in terms of CLOCK occupancy (Fig. 2C). These results confirmed that CLOCK protein is a highly phase-specific circadian transcription factor. Comprehensive and unbiased analysis of CLOCK-binding motifs. The CLOCK-BMAL1 complex binds to the canonical palindromic E-box, CACGTG (7), and to its related motifs called E=-box and E-box-like sequences (8–10, 40). These previous studies focused on selected genes, and therefore, present knowledge on the CLOCK-binding sequences might have underestimated and/or overestimated the binding motifs. High-quality ChIP-Seq data in this study made it possible to quantitatively and comprehensively investigate CLOCK-binding motifs in vivo. MEME is a widely used tool for determining DNA-binding motifs (41), and BMAL1-binding motifs were defined by using MEME in previous studies (20, 21). Here we should consider the feature of MEME, which aims at determining representative sequence motifs (Fig. 2D) rather than explicitly evaluating to what extent each of the related motifs is used in a genome-wide manner. In order to extract detailed characteristics of the CLOCK-binding motifs, we developed a bioinformatics method termed motif centrality analysis of ChIP-Seq (MOCCS), which enumerates and evaluates all the significant DNA-binding motifs based on ChIP-Seq data sets.

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FIG 3 Determination of CLOCK-binding motifs by MOCCS. (A) Frequency distribution of the indicated sequences around the CLOCK-binding sites observed at CT8. The bin size is set for 10 bp. (B) Cumulative relative frequency curves of all significant motifs around the CLOCK-binding sites observed at CT8. The x axis represents absolute values of distance from the CLOCK-binding sites. (C) Each of the 6-nucleotide sequences was inserted at the 5= side of the NheI site in the multicloning site of the pGL3N vector (see Materials and Methods) for the firefly luciferase plasmid. The data are ordered by normalized AUCs in the MOCCS analysis (CT8) (Table 1). The values for luciferase activities are shown as ratios of bioluminescence signals from firefly luciferase to those from renilla luciferase (internal control). Means of the signal ratios without expression of CLOCK and BMAL1 were set to 1. Data are means with standard errors of the means from three independent experiments. (D) CATGCG#1 (Btbd19)-luc/pGL3N, CATGCG#2 (Fam176a)-luc/pGL3N, and TACGTA#1 (Nfe2)-luc/pGL3N were used as firefly luciferase plasmids. (E) ChIP samples were prepared at 3-h intervals over a day and were subjected to PCR analysis with primer sets for CATGCG#1 (Btbd19). Filled and open bars indicate the data with CLSP4 and 1D4 antibodies, respectively.

(AACGTG) (nucleotides mismatched with CACGTG are underlined), which is another established CLOCK-binding motif (8). The other significant motifs included CACATG (CATGTG) and CACGCG (CGCGTG): the former is known to function in the promoter region of the Dbp gene (9), and the latter is evolutionarily conserved in the Per1 locus (10). Overall, the fifth (second) position of the CACGTG sequence was less selective, and all CA CGNG (CNCGTG) motifs had large AUCs. More importantly, MOCCS detected two novel potential motifs, CATGCG (CGC ATG) and a palindromic motif, TACGTA. In order to confirm the MOCCS data, we investigated CLOCK-BMAL1-dependent transcription via all the one-mismatch sequences and the two potential motifs with two mismatches (CATGCG and TACGTA) by promoter assays. CLOCK and BMAL1 activated transcription through not only the CACGTG canonical E-box but also six noncanonical sequences, CACGTT, CACGCG, CACGGG, CACGAG, CACATG, and CATGCG (Fig. 3C). All seven sequences were predicted to be CLOCK-binding motifs by MOCCS. The enhancer activity of the two-mismatch sequence CATGCG was also shown in the promoter assay with the 30-bp genomic sequences around the CLOCK-binding sites observed by ChIP-Seq (Fig. 3D). ChIPPCR analysis revealed rhythmic CLOCK binding to the CATGC G-including region (Fig. 3E). These observations corroborate the MOCCS results indicating that the two-mismatch sequence CA TGCG functions as a CLOCK-binding motif. In contrast, the other two-mismatch potential motif, TACGTA, showed no enhancer activity in the promoter assay (Fig. 3C and D). Note that TACGTA appeared far less frequently around the CLOCK-bind-

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ing sites than the other motifs (Table 1). Collectively, MOCCS and subsequent promoter assays revealed that the noncanonical E-box motifs CACGNG, CACGTT, and CATG(T/C)G were targeted by CLOCK in a genome-wide manner, rather than being limited exceptions. The bioinformatics method MOCCS is a powerful tool for determining all DNA-binding motifs from ChIP-Seq data sets. Circadian transcriptome analysis of mouse liver. The rhythmic DNA binding of transcription factors is expected to cause circadian transcription of their target genes. To assess the relationship between rhythmic CLOCK binding and RNA rhythms, we analyzed the circadian transcriptome in mouse liver. Poly(A)tailed RNAs were prepared from liver isolated at the 8 time points under DD conditions and were subjected to deep sequencing. The sequenced tags were mapped onto the mouse genome allowing one mismatch, and this analysis yielded about 20 million mapped tags for each sample (see Table S3 in the supplemental material). Among 37,314 genes, including noncoding RNAs (Ensembl, release 64), 11,926 genes were found to be expressed ones, while 1,126 genes (9.4% of expressed genes) were rhythmic in mouse liver (see Table S3 in the supplemental material). The rhythmic expression profiles of clock genes such as Dbp in the sequencing data are consistent with quantitative RT-PCR data (Fig. 4A to C), indicating high reliability of this RNA-Seq analysis. Among the 1,126 rhythmic genes identified, ⬎60% of genes were also reported to be rhythmically expressed genes in previous studies (Fig. 4D) (22, 23). The heat map showed a great diversity in circadian phases of the rhythmic genes (Fig. 4E), indicating



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The AUC (see the text and Fig. 3B) was normalized by dividing the value by the standard deviation of the AUC of all 6-mers within sequences 501 bp upstream of the transcription start sites of all protein-coding genes. Listed are the 6-mer motifs that (i) had a normalized AUC of ⬎5, (ii) did not overlap more overrepresented motifs, and (iii) had at most two mismatches to the canonical E-box motif CACGTG. Sequences in parentheses are complementary sequences. The normalized AUC represents their significance. b The frequency at which the motif appears in regions within 250 bp of the CLOCKbinding sites.

cooperative actions of the E-box, D-box, and RRE, as reported previously (11, 40). To understand how strongly each gene is regulated by CLOCK, all the genes were given “ChIP scores” based on the CLOCK ChIPSeq data at CT8 (see Table S4 in the supplemental material). The ChIP score was defined as the total number of sequenced tags that were mapped to all CLOCK-binding sites within ⫾10 kb from the transcription start site of each gene or in the gene body. A total of 2,234 genes with a ChIP score of ⬎60 were then designated CLOCK targets. Comparative analysis of the ChIP-Seq and RNASeq data revealed 324 rhythmic genes among 1,629 CLOCK targets expressed in mouse liver (see Table S4 in the supplemental material). In order to strengthen our results, we performed RNASeq using livers of Bmal1-KO mice and their WT littermates (see Table S5 in the supplemental material). We detected genes whose expression levels were significantly changed between Bmal1-KO and WT mice at at least one time point (CT2, -8, -14, and -20). These differentially expressed genes were 5.7 times more enriched in the rhythmic CLOCK targets than all the expressed genes, which indicates the validity of the rhythmic CLOCK targets defined in this study. The Krüppel-like factor family as circadian transcription factors. Comparison of CLOCK ChIP-Seq and RNA-Seq data revealed that 802 rhythmic genes were not included in the CLOCK targets, suggesting the importance of indirect regulation for dynamic circadian outputs. Such indirect regulations would be partly mediated by the actions of transcription factors that affect the expression of these target genes. In fact, gene ontology (GO) analysis identified 250 transcription factors as CLOCK targets, some of which were rhythmically expressed in mouse liver (see


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Table S6 in the supplemental material). In particular, we focused our attention on circadian expression of Krüppel-like factor (KLF) family members. Our previous studies demonstrated that KLF10/TIEG1 binds to the GC-box in the Bmal1 locus and suppresses its transcription (42, 43). Consistent with the circadian expression of Klf10/Tieg1, rhythmic binding of CLOCK was detected in its promoter region (see Table S4 in the supplemental material). Importantly, CLOCK binding was also detected in the loci of some KLF family genes, including Klf9 (ChIP score at CT8, 857), Klf13 (score, 826), Klf15 (score, 501), Klf16 (score, 215), and Klf11 (score, 114.7). The ChIP scores of these genes were all higher than the value for Klf10 (score, 64.4). Interestingly, KLF9, -10, -11, -13, and -16 are categorized as belonging to group 3 of the KLF family based on their functional and phylogenetic similarities (44)

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FIG 4 RNA-Seq analysis of mouse liver. (A) RNA-Seq data at the Dbp locus showing a robust change in its expression level in a time-of-day-dependent manner. (B) Circadian expression profiles of Dbp transcript in mouse liver revealed by RNA-Seq analysis. The RNA-Seq signals are shown as FPKM (fragments per kilobase of exon per million fragments) values. (C) Circadian expression profiles of Dbp transcript in mouse liver revealed by qRT-PCR analysis. The signals were normalized to Tbp for each sample, and the mean value at the peak time was set to 1. Data are means with standard errors of the means from three independent experiments (P ⬍ 0.0001 by analysis of variance). (D) Overlaps of rhythmically expressed genes in this study with those in previous RNA-Seq studies using mouse liver (22, 23). The genes whose identifiers were correctly mapped to the Ensembl gene identification are compared. (E) Heat map of the expression level of each rhythmic gene in the RNA-Seq analysis. Genes were ordered by the peak phases from early subjective day to late subjective night. The FPKM values were normalized so that the mean and the variance were 0 and 1, respectively, for each row of the maps.

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TABLE 1 List of CLOCK-binding motifs revealed by MOCCSa

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miRNAs (pre-miRNAs), which are further processed to ⬃22-nucleotide mature miRNAs. To search for rhythmic mature miRNAs, small RNAs were isolated from mouse liver total RNAs that were prepared at 8 time points under DD conditions and were subjected to deep sequencing (see Table S7 in the supplemental material). Approximately 70 to 75% of 20 to 30 million tags in each sample were mapped onto the mouse genome with no mismatches, among which 1,141 unique sequences were identical to mature miRNAs stored in miRBase (release 18). We then summed the number of mapped tags across all time points for each miRNA and found 270 miRNAs with ⬎100 tags (see Table S7 in the supplemental material). The heat map of the relative abundance revealed predominant enrichment of the rhythmic miRNAs peaking during the subjective day (Fig. 7A), consistent with data from a previous study (49). Circadian profiles of typical rhythmic miRNAs are shown in Fig. 7B. miRNAs have been predicted to target ⬎30% of the protein-coding mRNAs (50), and the contribution of miRNA expression toward regulating the circadian clockwork in several organisms has been shown (51–53). miRNAs recognize 3=-UTR sequences of their targets, and thereby, protein synthesis is inhibited, or deadenylation/degradation of the target mRNA is triggered. Application of a miRNA target prediction tool, TargetScan (54), actually predicted Clock to be one of the targets of miR-148a-3p. On the other hand, Caveolin-1 (Cav1) is a predicted target of miR-802, and as reported previously (55), we confirmed that overexpression of miR-802 in HEK293T cells significantly decreased the luciferase reporter activity with a 3= UTR of the Cav1 gene (Fig. 7D). Importantly, mRNA levels of Cav1 exhibited a circadian rhythm with a phase shifted largely from those of pri-mir-802 (Fig. 7C) and miR-802 (Fig. 7B), whereas circadian pol2-ChIP analysis demonstrated that Cav1 transcription itself exhibits no significant daily variation (56). It is probable that the rhythmic expression of miRNAs has a spillover effect on the circadian rhythms of an array of their target genes. DISCUSSION

Recent works have explored the genome-wide profiling of DNA binding patterns of circadian transcription factors such as REVERB␣/␤ (57–59), BMAL1 (20–23), and CLOCK (22, 23; this study). The Takahashi laboratory demonstrated 1,444 genomic sites as common targets of six clock proteins: BMAL1, CLOCK, PER1, PER2, CRY1, and CRY2 (22). In the present study, we developed a novel bioinformatics method, MOCCS, to generate a comprehensive list of DNA-binding motifs of CLOCK protein (Fig. 3 and Table 1). MOCCS can enumerate and evaluate signif-

FIG 5 E-box-dependent transcription of Krüppel-like factor family members. (A) Molecular phylogeny of Krüppel-like factors and their circadian regulation. Sequence alignment and a neighbor-joining tree were created by using the ClustalX tool version 2.1. Analysis was conducted on the full-length protein sequences of the KLF family members and mouse SP1 as outgroups (h, human; r, rat; m, mouse; c, chicken; z, zebrafish). They were divided into three distinct groups according to their functional and phylogenetic similarities, as described previously (44). ChIP-Seq scores, mRNA expression rhythmicities (P values), and the significance of peak-trough differences (q values) of Klf genes are shown. (B and F) CLOCK ChIP-Seq profiles for sequences around the Klf11 (B) and Klf13 (F) loci in mouse liver at CT8. Closed circles marked with LD and DD indicate the peak positions of ChIP-Seq tags at ZT8 and CT8, respectively. Closed and shaded boxes indicate the CACGTG-type E-box and the one-mismatch sequences, respectively. Underlined DNA sequences were examined for transactivation activity in dual-luciferase reporter assays in HEK293T cells. (C and D) Circadian expression profiles of Klf11 transcript in mouse liver revealed by RNA-Seq analysis (C) and by qRT-PCR analysis (D). (G and H) Circadian expression profiles of Klf13 revealed by RNA-Seq analysis (G) and by qRT-PCR analysis (H). In the qRT-PCR analysis, the signals were normalized to Tbp signals for each sample, and the mean value at the peak time was set to 1. Data are means with standard errors of the means from three independent experiments (P ⬍ 0.001 for Klf11 and P ⫽ 0.001 for Klf13 by analysis of variance). (E and I) Klf11-luc/pGL3 (E) and Klf13-luc/ pGL3 (I) were used as firefly luciferase plasmids. The values for luciferase activities are shown as ratios of bioluminescence signals from firefly luciferase to those from renilla luciferase (internal control). Means of the signal ratios without expression of CLOCK and BMAL1 were set to 1. Data are means with standard errors of the means from three independent experiments. Triple asterisks indicate a P value of ⬍0.001 (versus without CLOCK and BMAL1; determined by Student’s t test).


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(Fig. 5A). For example, rhythmically expressed Klf11/Tieg2 has a CLOCK-binding site in its promoter region, and the CACGTG E-box was found near the CLOCK-binding site (Fig. 5B to D). The rhythmically expressed Klf13 gene also has several CLOCK-binding sites in its first intronic region and has a noncanonical E-box, CACATG (CATGTG), near its largest peak (Fig. 5F to H). Functionally, the promoter assay showed CLOCK-BMAL1-dependent transactivation through these CLOCK-binding sites in the Klf11 and Klf13 loci (Fig. 4E and I). Recently, it was reported that KLF15 transcriptionally regulates circadian expression of the KChIP2 gene and controls the rhythmic duration of myocardial repolarization (45). KLF15 also coordinates circadian expression of multiple enzyme genes such as Alt and Otc for nitrogen homeostasis rhythms in the liver (46). On the other hand, KLF9 was identified as an important regulator of keratinocyte proliferation through controlling the expression of its target genes in a daytime-dependent manner (47). Collectively, circadian expression of KLF family members contributes to the rhythmic regulation of their target genes and potentially leads to daily variation of physiological functions. Circadian output through long and small noncoding RNAs. Genome-wide transcriptome studies revealed a huge number of noncoding RNAs, which are divided into two classes based on a 200-nucleotide-length cutoff; longer ones are long noncoding RNAs (lncRNAs), and smaller ones are small noncoding RNAs, including microRNAs (miRNAs) (48). In the present study, a subset of CLOCK-binding sites was detected in the loci of lncRNA genes (see Table S4 in the supplemental material), some of which were rhythmically expressed in the liver (see Table S3 in the supplemental material). 0610005C13Rik is one of the rhythmic lncRNAs, and CLOCK binding was detected at its third intron, which harbors at least one functional E-box (Fig. 6A to D). It is evident that lncRNA transcriptions are directly regulated by the circadian clock, suggesting the functional importance of lncRNAs in rhythmic events. In addition to lncRNAs, miRNAs are also the targets of CLOCK-dependent transcription. A subset of CLOCK-binding sites was detected near miRNA genes, including the mir-148a and mir-802 genes, and no other genes were found within 100 kb of each of the two miRNA genes (Fig. 6E and F). The CACGTG E-box was found in close proximity to these CLOCK-binding sites, and the promoter assay demonstrated CLOCK-BMAL1-dependent transactivation in these regions (Fig. 6G). miRNA genes are first transcribed as primary miRNAs (pri-miRNAs), and they are cleaved into ⬃70-nucleotide stem-loops called precursor

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FIG 6 Circadian transcription of noncoding RNAs. (A, E, and F) CLOCK ChIP-Seq data for sequences around the 0610005C13Rik (A), mir-148a (E), and mir-802 (F) loci at CT8. LD and DD indicate peak positions of ChIP-Seq tags in ZT8 and CT8, respectively. Black and gray shading indicates the CACGTG-type E-box and one-mismatch sequences, respectively. Underlined DNA sequences were used for dual-luciferase reporter assays in HEK293T cells. (B and C) Circadian expression profiles of 0610005C13Rik transcript in mouse liver revealed by RNA-Seq analysis (B) and by qRT-PCR analysis (C). In the qRT-PCR analysis, the signals were normalized to Actb for each sample, and the mean value at the peak time was set to 1. Data are means with standard errors of the means from three independent experiments (P ⬍ 0.001 by analysis of variance). (D and G) In the reporter assays, 0610005C13Rik-luc/pGL3 (D) and mir-148a-luc/ pGL3, mir-150-luc/pGL3, mir-802-proximal-luc/pGL3, and mir-802-distal-luc/pGL3 (G) were used as firefly luciferase plasmids. The values of luciferase activities are shown as ratios of bioluminescence signals from firefly luciferase to those from renilla luciferase (internal control). Means of the signal ratios without expression of CLOCK and BMAL1 were set to 1 for each firefly reporter. Data are means with standard errors of the means from three independent experiments. Single, double, and triple asterisks indicate P values of ⬍0.05, ⬍0.01, and ⬍0.001, respectively (versus without CLOCK and BMAL1; determined by Student’s t test).

icant motifs exhaustively, whereas previous analyses may have underestimated/overestimated the binding motifs. The MOCCS analysis and the subsequent promoter assay demonstrated that the CLOCK-BMAL1 complex recognizes CACGNG, CACGTT, and

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CATG(T/C)G in a genome-wide manner. We also showed that the CLOCK-binding motifs found in tandem frequently have a 6- to 7-bp spacer (Fig. 2E), consistent with the results of BMAL1 ChIPSeq (21). It should also be noted that the adjoining sequences



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around E-boxes would affect CLOCK-BMAL1-dependent transcription. For example, among the CLOCK-binding sites with the same motif, the tag numbers in the ChIP-Seq analysis and the transactivational activity in the promoter assay were obviously different from each other. Furthermore, the adjoining sequences around the CLOCK-binding motifs tended to be GC rich, and this is consistent with the CLOCK-binding region model predicted in a previous study (10). In the presence study, we also showed circadian profiles of all the transcripts by poly(A)-tailed RNA-Seq (Fig. 4) and smallRNA-Seq (Fig. 7). Intriguingly, the peak time of rhythmic miRNAs concentrates at the daytime (Fig. 7A), suggesting that E-box-dependent transcription plays a pivotal role in the regulation of miRNA biogenesis. On the other hand, the presence of the rhythmically expressed genes having no significant CLOCK-binding sites nearby indicates the importance of indirect transcription and posttranscriptional regulation in the genome-wide circadian network (Fig. 8). In fact, we found rhythmic transcription by rhythmically expressed transcription factors such as KLF subfamily members (Fig. 5) and gene silencing by rhythmic miRNAs such as miR-802 (Fig. 7). In this way, these rhythmic events that are regulated indirectly by E-box-dependent transcription should play key roles as functional hubs for secondary circadian regulation to generate robust rhythms. In this study, we extensively analyzed how the CLOCKBMAL1 heterodimer regulates gene expression rhythms in mouse liver. Significant DNA-binding motifs of CLOCK protein were comprehensively extracted from the ChIP-Seq data with the use of our best anti-CLOCK MAb, CLSP4, and by developing a powerful motif-finding algorithm, MOCCS. This bioinformatics method should be adopted by a wide range of studies using ChIP-Seq, a very popular technique today. Based on MOCCS, we have shown that CLOCK-BMAL1 directly governs the cyclic expression of thousands of output genes via atypical DNA sequence motifs. The present study further provides convincing evidence for the emerging concept that indirect regulation via transcriptional/posttranscriptional events plays a key role in generating diverse phases of genes expression. Such a multilayered genome-wide regulation should provide the basis for a systematic understanding of the molecular network of the circadian clock.

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ACKNOWLEDGMENTS We thank Kiyomi Imamura, Terumi Horiuchi, and Makiko Tosaka for their help with the experiments and data analysis. The supercomputing resource was provided by the Human Genome Center (The University of Tokyo). This work was supported in part by grants-in-aid for scientific research (to H.Y., W.I., and Y.F.) and by Innovative Areas Genome Science from MEXT of Japan. H.T. is supported by a JSPS research fellowship for young scientists.

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