Nascent-Seq reveals novel features of mouse circadian transcriptional regulation

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Menet et al. eLife 2012;1:e00011. DOI: 10.7554/eLife.00011 1 of 25

Nascent-Seq reveals novel eatures o mouse circadian transcriptional regulation

Jerome S Menet*, Joseph Rodriguez, Katharine C Abruzzi, Michael Rosbash*

Howard Hughes Medical Institute, National Center or Behavioral Genomics,and Department o Biology Brandeis University, Waltham, United States

Abstract A substantial raction o the metazoan transcriptome undergoes circadian oscillationsin many cells and tissues. Based on the transcription eedback loops important or circadiantimekeeping, it is commonly assumed that this mRNA cycling re ects widespread transcriptionalregulation. To address this issue, we directly measured the circadian dynamics o mouse livertranscription using Nascent-Seq (genome-wide sequencing o nascent RNA). Although many genes

are rhythmically transcribed, many rhythmic mRNAs mani est poor transcriptional rhythms,indicating a prominent contribution o post-transcriptional regulation to circadian mRNA expression.This analysis o rhythmic transcription also showed that the rhythmic DNA binding profle o thetranscription actors CLOCK and BMAL1 does not determine the transcriptional phase o mosttarget genes. This likely re ects gene-specifc collaborations o CLK:BMAL1 with other transcription

actors. These insights rom Nascent-Seq indicate that it should have broad applicability to manyother gene expression regulatory issues.DOI: 10.7554/eLi e.00011.001

IntroductionMost organisms rom bacteria to humans possess circadian rhythms, which generate oscillations in

biochemistry, physiology and behavior. The circadian system in eukaryotes is based on cell-autonomousmolecular oscillators, which rely on transcriptional eedback loops. In mammals, the transcription actorBMAL1 acts as a dimer with either CLOCK (CLK) or Neuronal PAS domain protein 2 (NPAS2) to activatethe transcription o many genes, including the transcriptional repressors Period (Per1 , Per2 and Per3 and Cryptochrome (Cry1 and Cry2 ). The PERs and CRYs are expressed, post-translationally modi ed,

eedback to inhibit their own transcription and are then rhythmically degraded to lead to a new roundo BMAL1:CLK or BMAL1:NPAS2 -mediated transcription (reviewed in Ko and Takahashi, 2006 Dardente and Cermakian, 2007 ). This temporal regulation o clock gene transcription cycles with aperiod o about 24 hr and probably underlies much o circadian biology.

Over the past decade, clock gene transcriptional regulation has been described in many speciesand tissues, where it drives the rhythmic expression o a large raction o the mRNA population (up to1015% o all mRNAs in a single mammalian tissue; Lowrey and Takahashi, 2004 ; Vollmers et al.,2009 ). Rhythmic mRNA expression has mostly been characterized by analyzing temporal changes o

steady-state mRNA levels, using techniques such as microarrays (e.g., McDonald and Rosbash, 2001Panda et al., 2002 ; Storch et al., 2002 ) and more recently high-throughput sequencing ( Hughes et al.,2012 ). It is generally assumed that these rhythms in mRNA expression directly result rom temporalchanges in transcription. There are, however, a ew reports indicating that post-transcriptional regula-tion contributes to rhythmic mRNA expression o several genes, including core clock genes (reviewedin Kojima et al., 2011 ; Staiger and Green, 2011 ; Staiger and Koster, 2011 ; Zhang et al., 2011 )but this has never been studied in detail at the genome-wide level. Circadian post-transcriptionalregulation may impact rhythmic mRNA expression at many di erent levels, such as mRNA splicing,stability and translation. For example, post-transcriptional events rhythmically regulate the mRNA hal -li eo the mammalian clock genes Per2 and Cry1 and the Drosophila clock gene per (So and Rosbash,

*For correspondence: [email protected] (MR); [email protected] (JSM)

Competing interests: The authors have declared thatno competing interests exist

Funding: See page 2 3

Received: 22 June 2012Accepted: 16 September 2012Published: 13 November 2012

Reviewing editor : Todd CMockler, Donald Dan orth PlantScience Center, United States

Copyright Menet et al. Thisarticle is distributed under theterms o the Creative CommonsAttribution License , whichpermits unrestricted use andredistribution provided that theoriginal author and source arecredited.

RESEARCH ARTICLE
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1997 ; Woo et al., 2009 ; Woo et al., 2010 ). Moreover, several RNA-binding proteins such as LARK,hnRNP I, hnRNP P, hnRNP Q or the circadian deadenylase NOCTURNIN have been shown to regulatecircadian gene expression and/or circadian behavior (reviewed in Kojima et al., 2011 ). These di erenmodes o post-transcriptional regulation are not restricted to circadian biology ( Keene, 2007 ) andhave been shown in other systems to regulate cellular mRNA levels independent o transcriptionalregulation ( Giege et al., 2000 ; Cheadle et al., 2005 ).

To address the genome-wide contribution o transcriptional and post-transcriptional regulation tomammalian mRNA rhythms, we used Nascent-Seq (high-throughput sequencing o nascent RNA;

Carrillo Oesterreich et al., 2010 ; Khodor et al., 2011 ) to assay global rhythmic transcription inmouse liver. We per ormed a parallel analysis o rhythmic mRNA expression with RNA-Seq and com-pared the two sequencing datasets. Although many genes are rhythmically transcribed in the mouseliver (15% o all detected genes), only 42% o these rhythmically transcribed genes show mRNAoscillations. More importantly, about 70% o the genes that exhibit rhythmic mRNA expression donot show transcriptional rhythms, suggesting that post-transcriptional regulation plays a major role inde ning the rhythmic mRNA landscape. To assess the contribution o the core molecular clock togenome-wide transcriptional rhythms, we also examined how rhythmic CLK:BMAL1 DNA bindingdirectly a ects the transcription o its target genes. Although maximal binding occurs at an apparentlyuni orm phase, the peak transcriptional phases o CLK:BMAL1 target genes are heterogeneous, which

eLi e digest Many biological processes oscillate with a period o roughly 24 hr, and the abilityo organisms as diverse as bacteria and humans to maintain such circadian rhythms, even underconditions o continuous darkness, in uences a range o phenomena, including sleep, migration andreproduction. One characteristic o circadian rhythms is that they can adjust to local time (withhumans su ering rom jet lag as they wait or this to happen).

Experiments have shown that the circadian system in mammals relies on eedback loops that

operate at the level o individual cells. These loops are controlled by two particular proteins, whichcomprise the transcription actor complex called BMAL1:CLK. Transcription actors cause particularsequences o bases in the DNA o cells to be transcribed into messenger RNA, thus starting theprocess by which target genes are expressed as proteins. In the case o BMAL1:CLK, these proteinsare then modifed, which inhibits any urther transcription o the target genes. A reversal o thesemodifcations is then ollowed by the synthesis o new proteins, which allows a new cycle o thetranscription process to begin.

The amounts o many messenger RNAs (mRNAs) in a cell also increases and decreases with aperiod o 24 hr, and it was generally assumed that this was due to the changes in the level otranscription. More recently, however, it was suggested that other processes, such as splicing andtranslation, might also contribute to rhythmic changes in the amount o mRNA associated withparticular genes. Such post-transcriptional processes are known to have a role in other areas o cellbiology, including aspects o the circadian system, but until very recently this had not been studied

in detail or all genes.Now Menet et al. have directly assayed rhythmic transcription by measuring the amount o

nascent mRNA being produced at a given time, six times a day, across all the genes in mouse livercells using a high-throughput sequencing approach called Nascent-Seq. They compared this withthe amount o liver mRNA expressed at six time points o the day. Although the authors ound thatmany genes exhibit rhythmic mRNA expression in the mouse liver, about 70% o them did not showcomparable transcriptional rhythms. Post-transcriptional regulation must, there ore, have a majorrole in the circadian system o mice and, presumably, other mammals.

Menet et al. also ound that the in uence o CLK:BMAL1 di ered rom what was expected,which suggests that it collaborates with a number o other transcription actors to e ecttranscription o most target genes. In addition to showing that circadian systems o mammals aremore complex than previously believed, the results also illustrate the potential o Nascent-Seq as agenome-wide assay technique or exploring a range o questions related to gene expression andgene regulation.DOI: 10.7554/eLi e.00011.002
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Figure 1 . Genome-wide assay o transcription in the mouse liver using Nascent-Seq. ( A): Distribution o high-throughput sequencing signal withinintrons (green), exons (blue) and intergenic regions (grey) or Nascent-Seq and RNA-Seq datasets. ( B): Visualization o Nascent-Seq and RNA-Seq signalat chr4: 40,730,00041,002,500. Genes above the scale bar are transcribed rom le t to right and those below the scale bar are transcribed rom right tole t. Nascent-Seq signal exhibits increased intron signal and a 5

to 3

gradient signal (arrow). Moreover, di erences between Nascent-Seq signal andRNA-Seq signal are observed or many genes (e.g., Bag1 and B4galt1 ). (C): Nascent-Seq signal (brown), but not RNA-Seq signal ( red), extends past theannotated 3 end o the genes B4galt1 and Nfx1. (D): Gene ontology o genes with high Nascent-Seq and low RNA-Seq signals (and inversely) isindicative o RNA with short or long hal -lives, respectively (see Materials and methods or details). ( E): Distribution o the Nascent-Seq/RNA-Seqsignal ratio or the classes o genes enriched in ( D). (F): Nascent-Seq/RNA-Seq signal ratio signi cantly correlates with mRNA hal -lives (values rom
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Transcriptional rhythms overlap poorly with rhythms in mRNAexpressionTo address the relationship o cycling transcription to cycling mRNAs, we assayed two independent sixtime-points pro les o pA RNA by RNA-Seq. The comparison with Nascent-Seq was restricted to genesthat were su ciently expressed in both datasets (n = 5454 genes; see Materials and methods or details).Using the identical statistical analysis and cut-o s to determine rhythmicity, the raction o rhythmic mRNAwas higher than the raction o rhythmic nascent RNA (22.1% and 15.1%, respectively; Figure 3A ).

There was also a notably poor overlap between the two rhythmic gene sets: only 41.6% o rhythmicallytranscribed genes also mani est rhythmic mRNA expression (R-R gene set; 342/822; Figure 3AC )However, the mRNA phase o these R-R genes was highly correlated to the nascent RNA phase (r = 0.92;Figure 3C ), and more than hal (57%) o these genes exhibit a phase di erence o less than 2 hr

(195/342; Figure 3D ). The amplitudes o the nascent RNA rhythms were also correlated with those o mRNA (r = 0.76; Figure 3E ), indicating that transcriptional regulation dominates these R-R rhythms.Not surprisingly, almost all clock genes ( Figure 4 ) and well-characterized clock-controlled genes(e.g., Nocturnin , Por , Alas1, Upp2 , Usp2 , Inmt , Nfl3 , etc; Figure 3source data 1 ) are in this R-Rgene set.

The other 58.4% o rhythmically transcribed genes (480/822) do not show robust mRNA expression(R-AR gene set; Figure 3A,F ). A simple explanation is that the mRNA hal -lives o these R-AR genesare relatively long and there ore mask the transcriptional oscillations. However, these genes do nothave altered nascent RNA to mRNA ratios compared to the whole genome ( Figure 5A ) or reportedlonger mRNA hal -lives (assessed using the dataset rom Sharova et al., 2009 ; Figure 5B ). Theseconsiderations suggest that other mechanisms account or the poor mRNA oscillations o this gene set(e.g., the rhythmic Nascent-Seq signal o 25 R-AR genes results rom rhythmic transcription o an adjacentgene that reads into the R-AR gene; Figure 5C ).

The opposite comparison is based on genes with rhythmic mRNA expression, o which only 28.4%(342/1204) have rhythmic transcription that meets the cycling criteria ( Figure 3A,G ). This surprisingconclusion was similar when the analysis was restricted to genes with the strongest mRNA rhythms(121/435) and indicates that most cycling mRNAs (862/1204) likely undergo post-transcriptional regu-lation. This might include the circadian regulation o nuclear RNA processing, export, translationalregulation and/or mRNA turnover, as described or the ew circadian genes shown to be regulatedpost-transcriptionally ( Kojima et al., 2011 ; Staiger and Koster, 2011 ; Zhang et al., 2011 ). Geneontology analysis o this arrhythmic transcription-rhythmic mRNA (AR-R) gene set did not reveal anystriking enrichment o particular gene unctions ( Figure 5D ).

Transcriptional variability contributes to rhythmic mRNA expressionThere is a peculiar eature o the large number o genes within this AR-R category: visualization o RNAexpression with heatmap indicates that many o these transcriptionally arrhythmic genes mani est

elevated transcription at times that match their cycling mRNA peaks ( Figure 3G ; note that the heatmapsshow that the transcription peak o many genes matches the peak phase o mRNA cycling). Furtherinspection o all individual expression pro les con rmed this correlation: many genes have matchingpeak phases despite large variations in nascent RNA expression ( Figure 6A ).

To substantiate this variability in nascent RNA expression, we calculated the standard deviationnormalized to the mean (SD) or every gene using the two 6 time-points pro les as 12 independentsamples. The reasoning was that low variability expression between time points should result in a lowSD. The SD was noticeably higher in the Nascent-Seq dataset than in the RNA-Seq dataset, indicatingthat the transcription o most AR-R genes is indeed variable compared to the mRNA-Seq dataset(Figure 6C ; note that most points are to the right o the diagonal line). Higher variability o nascent

Sharova et al., 2009 ), and genes with high ratio display shorter hal -lives and inversely. ( G) and (H): Strategy used to determine the gene signal cut-o threshold used in our analysis. Variation o gene signal coming rom the sequencing o a Nascent-Seq library (G; ZT8, replicate 1) sequenced in twoIllumina fow-cell lanes was assessed by calculating the z-score ( H). Less than 5% o the genes with a read per base pair superior to three exhibit a1.3- old gene signal variation. See Materials and methods or more details.DOI: 10.7554/eLi e.00011.003

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Figure 2 . Genome-wide analysis o rhythmic transcription in the mouse liver. ( A): Visualization o Npas2 Nascent-Seqsignal at six time points o the light:dark cycle ( rst replicate). Npas2 Nascent-Seq signal is rhythmic and peaks atZT20-ZT0, contrary to the signal within the adjacent gene Rpl31. (B): Quanti cation o the number o genes that arerhythmically transcribed in the mouse liver. Genes with more than three reads per base pair or at least one timepoint were included or the analysis. Genes are considered to be rhythmically transcribed i signal amplitude (Amp)is greater than 1.5, i signals or the 12 time points ollow a sinusoid curve (F 24 > 0.45) and i the F 24 value is in thetop 5% o all F24 values calculated a ter time points were permutated 10,000 times (p 0.6 and Ampl > 1.75. ( C): Heatmap representation o Nascent-Seq signal or the963 genes that are rhythmically transcribed in the mouse liver. High expression is displayed in yellow (z-score > 1),low expression in blue (z-score < 1). ( D): Expression phase o rhythmically expressed nascent RNA (n = 936) wasseparated by bins o 2 hr. Analysis o their distribution reveals that ewer genes are transcribed at ZT16-20.


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RNA expression is also observed or other categories, including the AR-AR set (data not shown), sug-gesting that it is a common eature o transcription vs mRNA comparisons independent o circadianconsiderations.

Yet a higher variability o transcription (higher SD) correlates with rhythmic mRNA expression withinall arrhythmically transcribed genes (n = 4632), suggesting that transcriptional variability generallycontributes to the generation o rhythmic mRNA expression ( Figure 6D ). This relationship is independento nascent RNA expression levels, indicating that the correlation is not due to sequencing depth(Figure 6E ). More variable transcription is also associated with higher amplitudes o rhythmic mRNAexpression ( Figure 6F ). This correlation is valid or most AR-R genes ( 80%, Figure 6F ), suggestingthat this transcriptional variability or noise has a signi cant role in the emergence o rhythmic mRNAexpression rom arrhythmic transcription.

A comparison between the two replicates o rhythmic mRNAs indicates a better overlap thanbetween one replicate and one replicate o rhythmic nascent RNAs. In contrast, the overlap betweenthe two replicates o rhythmic nascent RNAs was no better than a single replicate rhythmic nascentRNA-rhythmic mRNA comparison (data not shown). Although the two comparisons cannot be de nitivebecause o the limited six time point temporal resolution and resultant noise, they also support morepronounced variation at the transcriptional level than at the mRNA level.

Manual inspection o AR-R genes with high variability revealed a set o genes with high transcrip-tion at only one time point (10% o the AR-R gene set; 86/862). This was observed in both replicatesand also correlates with rhythmic mRNA expression ( Figure 6G ). mRNA stability (hal -li e) regulationmay contribute to the generation o rhythmic mRNA expression rom what is likely a short bursto transcription. This ability o post-transcriptional regulation to generate rhythmic mRNA oscilla-tions is selective, as not all arrhythmically transcribed genes with variable transcription exhibit rhyth-mic mRNA expression ( Figure 6H ). Moreover, not all genes exhibit variable transcriptional pro les

(Figure 6B , see below).About 20% o the AR-R genes are exceptions and exhibit higher variability at the mRNA than at the

nascent level ( Figure 6B,F ). Because there is evidence that miRNAs can regulate mRNA levels inde-pendently o transcription, we examined whether those genes could be pre erentially linked to miRNAregulation. Indeed, a higher number o predicted miRNA target sites was ound or these genescompared to genes with higher transcriptional variability (using MirTarget2; p


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Figure 3 . Post-transcriptional events account or a signi cant raction o rhythmic gene expression in the mouse liver. ( A): Rhythmic gene expression wasassessed as in Figure 2B or genes su ciently expressed in both Nascent-Seq and RNA-Seq datasets. Four categories o rhythmically expressed geneswere determined by comparing the Nascent-Seq and RNA-Seq datasets: rhythmic nascent RNA and mRNA (R-R), rhythmic nascent RNA only (R-AR),rhythmic mRNA only (AR-R) and arrhythmic nascent RNA and mRNA (AR-AR). ( B): Heatmap representation o genes with rhythmic nascent RNA andmRNA expression (n = 342). Classi cation is based on the phase o nascent RNA oscillations, and each lane corresponds to one gene. ( C): Double-plotted phase distribution o rhythmic nascent RNA expression (brown) and rhythmic mRNA expression (red) or genes o the R-R gene set. Both phasesare highly correlated (r = 0.92). ( D): Distribution o the di erence between the phase o mRNA expression rhythm and the phase o nascent RNAexpression rhythm or the 342 R-R genes. ( E): Amplitude o mRNA expression rhythms are correlated with nascent RNA expression rhythms (r = 0.76).(F) and (G): Similar representation to ( B) or rhythmically transcribed genes with no mRNA expression rhythms ( C, n = 480), and genes that exhibit mRNAoscillations but no rhythms o transcription ( D, n = 862). For all three heatmaps, high expression is displayed in yellow (z-score > 1), low expression inblue (z-score < 1).DOI: 10.7554/eLi e.00011.012The ollowing source data are available or gure 3.Source data 1. Gene expression values rom our Nascent-Seq and RNA-Seq datasetDOI: 10.7554/eLi e.00011.013Source data 2. Gene expression values or all UCSC genes rom our mouse liver RNA-Seq datasetDOI: 10.7554/eLi e.00011.014
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Figure 4 . Clock genes nascent RNA and mRNA expression in the mouse liver. Clock genes nascent RNA levels (brown; time points every 4 hr startingat ZT0) and mRNA levels (red; time points every 4 hr starting at ZT2) rom the Nascent-Seq and RNA-Seq datasets. Relative levels between nascent RNAand mRNA expression pro les are identical or all genes to allow direct comparison.DOI: 10.7554/eLi e.00011.015

were enriched or responsive genes (GO: response to stimulus) and metabolic genes ( Figure 6J )indicating that their intrinsic transcriptional responsiveness is linked to their variable transcrip-tional pro les.

Regulation o transcription by CLK:BMAL1Genes within the R-R gene set include clock genes and many well-characterized clock controlled genes(see above). Because a large raction o them are directly targeted by the core clock, we asked howCLK:BMAL1 regulates the transcription o its target genes at the genome-wide level. We also tookadvantage o our Nascent-Seq dataset to establish whether the phase di erences between rhythmic


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Figure 5 . Analysis o the di erent classes o rhythmically expressed genes in the mouse liver. ( A): Nascent-Seq/RNA-Seq signal ratio (used as in erredhal -li e) is similar or the our categories o rhythmically expressed genes: rhythmic nascent RNA and mRNA (R-R), rhythmic nascent RNA only (R-AR),rhythmic mRNA only (AR-R) and arrhythmic nascent RNA and mRNA (AR-AR). ( B): Similar as (A), using the RNA hal -li e values romSharova et al., 2009 (C): Nascent-Seq rhythms o 25 o the 480 R-AR genes can be attributed to the rhythmic transcription o an adjacent gene. This applies to Sphk2 Nascent-Seq rhythm, which likely results rom rhythmic Dbp nascent RNA signal that extend the 3 end o Dbp gene and read through Sphk2 . Genesabove the scale bar are transcribed rom le t to right and those below the scale bar are transcribed rom right to le t. ( D): Gene ontology o threecategories o rhythmically expressed genes: rhythmic nascent RNA and mRNA (R-R), rhythmic nascent RNA only (R-AR), rhythmic mRNA only (AR-R).

DOI: 10.7554/eLi e.00011.016

CLK:BMAL1 DNA binding and rhythmic target gene mRNA expression ( Rey et al., 2011 ) refect tran-scriptional or post-transcriptional regulation.

To this end, we rst per ormed a ChIP-Seq analysis o CLK and BMAL1 at a time o high DNA bind-ing (ZT8). As expected, CLK and BMAL1 target many DNA binding sites in mouse liver (759 and 1579,respectively) and signi cantly overlap on 211 o these peaks ( Figure 7A ). Although highly signi cant(chi-square test, p


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Figure 6 . Transcriptional variability o AR-R genes contributes to rhythmic mRNA expression. ( A) and (B): Nascent RNA levels (brown; time points every4 hr starting at ZT0) and mRNA levels (red; time points every 4 hr starting at ZT2) rom the Nascent-Seq and RNA-Seq datasets or six genes o the AR-R


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Figure 6. Continued

gene set. While the majority o the AR-R genes exhibit variable nascent RNA expression ( A), some o them exhibit a relatively constant transcriptionwhen compared to mRNA expression ( B). (C): Standard deviation (SD; calculated using the 12 time points and normalized to the mean) o nascent RNAexpression is higher than the SD normalized to the mean o mRNA levels or most AR-R genes. ( D) and (E): Higher transcriptional variability (SD) o arrhythmically transcribed genes is associated with higher occurrence o rhythmic mRNA expression ( D), but not to nascent RNA expression levels ( E).(F): Higher variability o transcription or the genes o the AR-R group is associated with increase amplitude o rhythms at both Nascent RNA (brown)and mRNA (red) level. Genes o the AR-R group (n = 862) were binned into ve quintiles o equal size (q1q5). ( G): Heatmap representation o 86 AR-Rgenes that exhibit high level o transcription at only one time point, and with rhythmic mRNA expression. High expression is displayed in yellow

(z-score > 1), low expression in blue (z-score < 1). ( H): Nascent RNA levels (brown) and mRNA levels (red) or our AR-AR genes with variable nascentRNA expression that is not associated to rhythmic mRNA expression. ( I): Number o predicted miRNA target sites o AR-R genes with high transcriptionalvariability (q1, top 20% o the 826 AR-R genes) and low transcriptional variability (q5, bottom 20%). ( J): Gene ontology o AR-R genes with high transcrip-tional variability (top 25%) when compared to all AR-R genes. Signi cant enrichment (top) and depletion (bottom) o biological unctions or these genesare displayed. Values correspond to the number o genes within this top 25% o genes, when compared to all AR-R genes.DOI: 10.7554/eLi e.00011.017The ollowing source data are available or gure 6.Source data 1. Peak coordinates or CLK:BMAL1, BMAL1 only and CLK only DNA binding sitesDOI: 10.7554/eLi e.00011.018

Remarkably, this is also true or the core clock genes. Whereas a small number have the expectedphase similar to that o CLK:BMAL1 DNA binding ( Rev-Erb , Dbp ; Figure 7E ), the transcription o most target genes is signi cantly delayed. They include Per1 (Figure 7F ), Cry1 (Figure 7G ) and Per2(8E). As recently proposed or Cry1 (Ukai-Tadenuma et al., 2011 ), this general delay in the peak o rhythmic transcription may be due to a collaboration o CLK:BMAL1 with other transcription actors(Rev-erb and Dbp in the case o Cry 1).

To validate this interpretation, we assayed the pre-mRNA levels in Bmal1/ mice o our clockgenes that exhibit di erent phases o transcription in wild-type mice ( Figure 8D ). Only the levels oRev-Erb pre-mRNA in the mutant mice con orm to expectation and are at trough levels o wild-typemice. The levels o Per1 , Per2 and Cry1 pre-mRNAs in Bmal1/ mice are higher than the trough o expression in wild-type mice, indicating that other transcriptional regulators are indeed important orthe transcription o these three clock genes ( Figure 8D ). This notion is also supported by the enrichmento other transcription actor moti s such as HNF3/FOXA1, SP1 and E4BP4 adjacent to CLK:BMAL1binding sites ( Figure 7D ).

Notably, Per2 has a prominent anti-sense transcript at times o low Per2 sense transcription ( Figure 8E )Moreover, the 5 end o this transcript coincides with peaks o Pol II ( Figure 8E ). This suggests thatantisense transcription could be an additional mechanism responsible or the disconnect between thephase o CLK:BMAL1 DNA binding and the phase o rhythmic transcription.

DiscussionRhythmic mRNA expression is a hallmark o circadian biology and commonly assumed to be a conse-quence o rhythmic transcription. However, the application here o Nascent-Seq to genome-wide mouseliver transcriptional rhythms indicates that about 70% o the genes that exhibit rhythmic mRNA expressiondo not have robust transcriptional rhythms (AR-R category), suggesting that post-transcriptional mech-anisms are important or the generation o robust mRNA rhythms. Yet the transcription o most AR-R genesis variable, with elevated levels coinciding with the peak o the rhythmic mRNA pro le. This suggests thatpost-transcriptional events bu er variable transcriptional output to generate robust and reproducible

rhythms o mRNA expression ( Figure 9 ). A similar Nascent-Seq vs RNA-Seq strategy or Drosophila headRNA (Joe Rodriguez and Michael Rosbash, personal communication) and a very recently published paperbased on a di erent strategy or assessing liver transcriptional rhythms ( Koike et al., 2012 ) come to agenerally similar conclusion, namely, a widespread contribution o post-transcriptional regulation tocircadian mRNA cycling.

Relevant mechanisms likely include RNA stability as well as other RNA processing events, as suggestedin other systems such as the regulation o gene expression in Arabidopsis mitochondria ( Giege et al.,2000 ) and a ter T-cell activation ( Cheadle et al., 2005 ). Because the hal -lives o nascent transcriptsare generally much shorter than those o mRNA ( Gri fths-Jones, 2007 ; Mattick, 2009 ; Mercer et al.,2009 ; Wang and Chang, 2011 ), a short burst o transcription can result in elevated mRNA expression
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Figure 7 . Characterization o CLK and BMAL1 target genes in the mouse liver. ( A) and (B): Visualization (A) and quanti cation ( B) o BMAL1 ChIP-Seq,CLK ChIP-Seq and input signal at BMAL1 and CLK signi cant peaks (analysis using MACS algorithm). BMAL1 ChIP-Seq, CLK ChIP-Seq and Input signals


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Figure 7. Continued

were retrieved based on the location o the BMAL1 peaks (center 1kb, or CLK:BMAL1 peaks and BMAL1 only peaks) or the CLK peaks (center 1kb,or CLK only peaks). Normalization was per ormed on the entire datasets by calculating the z-score ((x mean)/SD). Heatmap displays high expression in

red and low expression in blue. Quanti cation ( B) was per ormed by averaging the z-score by bins o 25 bp or all CLK:BMAL1 peaks (n = 211), BMAL1only peaks (n = 1368) and CLK only peaks (n = 548). ( C): Enrichment o e-boxes (per ect CACGTG in red, degenerated e-boxes [one nucleotide mismatch, inorange]) within 500 bp o CLK:BMAL1, BMAL1 only and CLK only peak centers. ( D): Moti s enriched within CLK:BMAL1 peaks, BMAL1 only peaks andCLK only peaks, as revealed by MEME analysis. ( E)(H): Visualization o BMAL1 ChIP-Seq (blue), CLK ChIP-Seq (green) and Nascent-Seq (brown; six timepoints o replicate 1) signals or Rev-Erb (E), Per1 (F), Cry1 (G) and a cluster o 4 lncRNA ( AK079377 , AK007907 , AK036974 , AK087624 ) (H) targeted by

CLK:BMAL1. Genes above the scale bar are transcribed rom le t to right and those below the scale bar are transcribed rom right to le t.DOI: 10.7554/eLi e.00011.019

that lasts several hours, as has been shown in systems involving an acute infammatory response(Cheadle et al., 2005 ; Hao and Baltimore, 2009 ). However, generic mRNA stability cannot account

or this bu ering, as many genes with arrhythmic but variable transcription do not exhibit rhythmicmRNA expression ( Figure 6H ). This suggests that the post-transcriptional bu ering is clock-controlledand selective or speci c genes.

Because our experiments were done under LD conditions, it is possible that some cycling RNAs andmechanisms are not circadian but driven by the LD cycle. Nonetheless, it is likely that many o themalso occur under DD conditions and that speci c and perhaps multiple post-transcriptional mechanismscontribute to rhythmic mRNA expression. They may include 3 end ormation and coupled polyade-nylation, splicing, mRNA export as well as cytoplasmic events involving translation, RNA binding proteins(RBP) and miRNAs (Joshi et al., 2012 ). Interestingly, a ew recent reports highlight the tight couplingbetween transcriptional regulation and post-transcriptional events that govern mRNA stability ( Bregmanet al., 2011 ; Trcek et al., 2011 ). In these examples, RBPs are recruited by speci c transcription actors,which then help load the RBPs onto nascent RNA; they then control cytoplasmic mRNA stability(Bregman et al., 2011 ; Trcek et al., 2011 ). A mechanism o this nature could account or the post-transcriptional generation o rhythmic mRNA expression.

The AR-R genes with high transcriptional variability are enriched or metabolic unctions as well asthose involved in response to stimulus. The transcription o many metabolic genes is regulatedby metabolites and/or hormones (e.g., transcription o Sds is induced by glucagon and CREB, Haasand Pitot, 1999 ). A large raction o rhythmic mRNAs may there ore result rom a transcriptionalresponse, dependent on the cellular environment, as well as post-transcriptional events that stabilize

mRNA at an appropriate time o the day. This scenario could also explain the R-AR gene set: despiterhythmic transcription, the lack o rhythmic post-transcriptional regulation would negate the transcrip-tional oscillations.

Importantly, many genes with rhythmic mRNA expression also exhibit robust transcriptional rhythms.They include all well-known clock genes and many well-characterized clock-controlled genes (seeabove). Their transcriptional pro les suggest that they are under more stringent transcriptional control(Figure 9 ), due perhaps to direct regulation by the core clock in combination with additional transcrip-tion actors.

Our genome-wide characterization o rhythmic transcription also allowed us to directly assayhow the rhythmic binding o CLK:BMAL1 to its target gene promoters correlates with transcrip-tion. The transcriptional phase o these target genes is heterogeneous and distributed throughoutthe day, despite a more discrete phase o BMAL1 DNA binding at the beginning o the lightphase. This indicates that transcriptional output is not identical or all target genes and suggests

that CLK:BMAL1 cooperates with other transcription actors to establish the phase o transcrip-tion, as previously shown only or the Cry1 gene ( Ukai-Tadenuma et al., 2011 ). In addition, tran-scription o most o these target genes is arrhythmic but not absent without BMAL1. This eatureo target gene expression as well as the heterogeneity o phase is unlike what is observed in fies:core CLK:CYC target genes exhibit a discrete phase o expression that matches the phase o DNAbinding ( Abruzzi et al., 2011 ).

In summary, the application o Nascent-Seq and RNA-Seq to mammalian circadian gene expressionregulation challenges two assumptions o the mammalian circadian eld. The rst is that rhythmictranscriptional regulation is su cient to describe the cycling gene expression landscape. The secondis that CLK:BMAL1 DNA binding alone sets the phase o , and is essential or, core clock gene transcription.


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Figure 8 . Disconnect between rhythmic BMAL1 DNA binding and its transcriptional output. ( A): Heatmaps representing BMAL1 ChIP-Seq signal ( romRey et al., 2011 ), Nascent-Seq and RNA-Seq signal or CLK:BMAL1 target genes (six time points in duplicate). Genes were classi ed in our categories:rhythmic nascent RNA and mRNA (R-R), rhythmic nascent RNA only (R-AR), rhythmic mRNA only (AR-R) and arrhythmic nascent RNA and mRNA (AR-AR).High expression is displayed in yellow, low expression in blue. ( B): Peak phase distribution o rhythmic BMAL1 DNA binding (blue, rom Rey et al.,2011 ), o nascent RNA (black) and o mRNA (red) or the direct target genes that are rhythmically expressed at both the nascent RNA and mRNA levels.(C): Distribution o CLK:BMAL1 target genes within the 4 di erent classes o rhythmically expressed genes and its comparison to the genome-widedistribution. Rhythmic nascent RNA and mRNA: R-R; rhythmic nascent RNA only: R-AR; rhythmic mRNA only: AR-R; arrhythmic nascent RNA and mRNA:AR-AR. (D): qPCR quanti cation o Rev-Erb , Per1, Per2 and Cry1 pre-mRNA every 4 hr throughout the day in wild-type (black, n = 4 per time points) andBmal1/ mice (blue, n = 3 per time points). Error bar: s.e.m. ( F): Visualization o BMAL1 ChIP-Seq (blue), CLK ChIP-Seq (green), Nascent-Seq (brown; sixtime points o replicate 1), Pol II ChIP-Seq signal (purple) at ZT10 and ZT22 ( rom Feng et al., 2011 ) and strand-speci c Nascent-Seq signal or Per2 (plus strand, top; minus strand, bottom). Per2 is rhythmically transcribed (minus strand) with a peak at ZT16. An antisense transcript is rhythmicallytranscribed to Per2 RNA (plus strand), peaking at ZT4.DOI: 10.7554/eLi e.00011.020


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The dramatic, genome-wide disconnect between the phases o rhythmic CLK:BMAL1 DNA bindingand rhythmic target gene transcription suggests that other transcription actors and/or mechanismscollaborate with CLK:BMAL1 binding and are critical to determine the phase o clock gene transcrip-tion. We anticipate that Nascent-Seq will impact gene expression regulation ar beyond the circadianapplications shown here.

Materials and methods Animals3- to 6-month-old male mice housed in a 12 hr-light:12 hr-darkness (LD12:12) schedule were used.Wild-type mice (C57BL/6 strain) and Bmal1/ mice (originally rom Christopher A Brad eld; Bunger et al., 2000 ) were used. All experiments were per ormed in accordance with the National Institutes o Health Guide or the Care and Use o Laboratory Animals and approved by the Brandeis InstitutionalAnimal Care and Use Committee (IACUC protocol #0809-03).

Isolation o nascent RNAMice housed in LD12:12 were sacri ced every 4 hr (ZT0, 4, 8, 12, 16 and 20) by isofurane anesthesia

ollowed by decapitation. Mouse liver was then quickly removed and homogenized in 3.5 ml o 1 PBSand 3.5 ml o homogenization bu er (2.2 M sucrose, 10 mM Hepes pH 7.6, 15 mM KCl, 2 mM EDTA, 1protease inhibitor cocktail [Roche, Basel, Switzerland], 0.15 mM spermine, 0.5 M spermidine, 0.5 mMDTT) with a dounce homogenizer (six strokes loose pestle, our strokes tight pestle). The liver homogen-ate was then mixed with 21.5 ml o homogenization solution and layered on the top o a 10 ml ice-coldcushion solution (2.05 M sucrose, 10 mM Hepes pH 7.6, 10% glycerol, 15 mM KCl, 2 mM EDTA, 1protease inhibitor cocktail [Roche, Basel, Switzerland], 0.15 mM spermine, 0.5 M spermidine, 0.5 mMDTT) and centri uged or 45 min at 2C at 24,000 rpm (100,000 g) using a Bechmann SW27 rotor.Nuclei were resuspended in 1 ml o 20 mM Hepes pH 7.6, 150 mM NaCl, 2 mM EDTA, 1 protease

Figure 9 . Post-transcriptional events contribute to rhythmic mRNA expression in the mouse liver. Althoughrhythmic transcription plays a major role or approximately 30% o the genes that exhibit rhythmic mRNA expres-sion, post-transcriptional events signi cantly contribute to the generation o mRNA rhythms or the majority o genes ( 70%). Many post-transcriptional cyclers exhibit highly variable transcription that is bu ered to generaterobust rhythmic mRNA expression. Few genes exhibit a relatively constant transcription when compared to mRNAexpression. These post-transcriptional events may include roles or RNA binding proteins and miRNAs to regulateRNA stability, 3 end ormation and nuclei export.DOI: 10.7554/eLi e.00011.021


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inhibitor cocktail (Roche, Basel, Switzerland), 0.5 mM PMSF, 1 mM DTT, 0.5 U/ml o RNAseOUT/SUPERase-In (Invitrogen/Ambion), homogenized using a 1 ml dounce homogenizer (three times withloose pestle, two times with tight pestle), and divided into three samples o equal volume. One vol-ume o 2 NUN bu er (50 mM Hepes pH 7.6, 2 M Urea, 2% NP-40, 600 mM NaCl, 2 mM DTT, 1protease inhibitor cocktail [Roche, Basel, Switzerland], 0.5 mM PMSF, 0.5 U/ml o SUPERase-In [Ambion,Carlsbad, Cali ornia]) was then added drop-by-drop while gently vortexing (level 2). Samples were le ton ice or 20 min, then centri uged at 24,000 rpm or 10 min at 4C. The supernatant was removed and1 ml o Trizol (Invitrogen, Carlsbad, Cali ornia) was added to the DNA pellet. Samples were then incu-

bated at 65C or 15 min, and DNA pellet was then resuspended by gentle pipetting. Nascent RNAwas then extracted using standard Trizol RNA extraction (Invitrogen, Carlsbad, Cali ornia).

Isolation o total RNAMice housed in LD12:12 were sacri ced by isofurane anesthesia ollowed by decapitation every4 hr (ZT2, 6, 10, 14, 18 and 22). Mouse liver was then quickly removed and cut into small pieces thatwere rozen on dry ice. Total RNA was extracted using standard Trizol extraction (Invitrogen, Carlsbad,Cali ornia).

Generation o Nascent-Seq and mRNA-Seq librariesNascent RNA was rst DNase-treated with TURBO DNase (Ambion, Carlsbad, Cali ornia) usingmanu acturers recommendation. Polyadenylated RNA was then removed rom the nascent RNA usingDynabeads mRNA direct kit (Invitrogen, Carlsbad, Cali ornia) ollowing manu acturers recommend-ations, and nascent RNA was precipitated (ethanol precipitation).

Sequencing libraries have been made using standard protocols. Briefy, 100 ng o puri ed nascentRNA were used to generate Illumina libraries. Nascent RNA was rst ragmented using Fragmentationreagents (AM8740; Ambion, Carlsbad, Cali ornia) by heating at 70C or 5 min. Fragmented nascent RNAwere then puri ed and used or standard Illumina library preparation. Following adaptor ligation, librar-ies o 200300 bp length were size-selected on a 2% TAE agarose gel, and ampli ed by PCR or 15cycles.

Strand-speci c libraries were processed as above except or the ollowing modi cations that havealso been described elsewhere ( Levin et al., 2010 ). Briefy, a ter the rst strand cDNA synthesis,dNTPs were removed by size-exclusion chromatography columns (G-50 columns; Amersham,Amersham, UK) and by ethanol precipitation using ammonium acetate. Second strand synthesis wasthen per ormed using a dNTP mixture containing dUTP instead o dTTP. A ter adaptor ligation and

size selection (i.e., prior to the ampli cation), libraries were digested using Uracil-Speci c ExcisionReagent (USER Enzyme, NEB, Ipswich, Massachusetts) by incubating 2 units (2 l) o USER Enzymwith 18 l o libraries at 37C or 30 min. Reaction was then heat-inactivated, the libraries were puri edand PCR-ampli ed.

RNA-Seq libraries (e.g., mRNA) were made using Truseq RNA sample kit (Illumina, San Diego,Cali ornia) ollowing manu acturers recommendations.

CLK and BMAL1 chromatin immunoprecipitationAdult mice housed in LD12:12 were sacri ced at ZT8 by isofurane anesthesia ollowed by decapitation.Mouse liver was then quickly removed and homogenized in 3.5 ml o 1 PBS supplemented with 1%

ormaldehyde. A ter 10 min incubation at room temperature, cross-linking was stopped by mixing liverhomogenate with 25 ml o ice-cold quenching solution (2.2 M sucrose, 150 mM glycine, 10 mM HepespH 7.6, 15 mM KCl, 2 mM EDTA, 1 protease inhibitor cocktail [Roche, Basel, Switzerland], 0.15 mM

spermine, 0.5 M spermidine, 0.5 mM DTT). Homogenate was then layered on top o a 10 ml ice-coldsucrose cushion (2.05 M sucrose, 125 mM glycine, 10 mM Hepes pH 7.6, 10% glycerol, 15 mM KCl,2 mM EDTA, 1 protease inhibitor cocktail [Roche, Basel, Switzerland], 0.15 mM spermine, 0.5 M spermi-dine, 0.5 mM DTT) and centri uged or 30 min at 2C and 24,000 rpm (100,000 g) using a BechmannSW27 rotor. Nuclei were resuspended in 1 ml o 20 mM Hepes pH 7.6, 150 mM NaCl, 2 mM EDTA andsedimented at 1500 g or 1 min. Washed nuclei were resuspended in 1.2 ml sonication bu er (20 mMHepes pH 7.6, 1% SDS, 150 mM NaCl, 2 mM EDTA) and sonicated on ice using a Fisherbrand SonicDismembranator at setting 2 (57 W) or 5 15 s to obtain chromatin ragments o about 5001000 bp inlength. The resulting chromatin was centri uged at 15,000 g or 10 min and the resulting supernatantwas aliquoted in 200 l samples or immunoprecipitation and 25 l samples or input.


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Immunoprecipitation o chromatin was per ormed by mixing 200 l o sonicated chromatin with1.8 ml o IP bu er (10 mM Hepes pH 7.6, 150 mM NaCl, 2 mM EDTA, 0.1% NaDeoxycholate, 1% Triton

X-100). Antibodies were added and samples were incubated overnight (Rabbit anti-BMAL1 antibody:10 l, ab3350; Abcam; Rabbit anti-CLK antibody: 10 l, NB100-126; Novus Biologicals). Dynabeadsprotein G (100 l per sample; Invitrogen, Carlsbad, Cali ornia) were blocked in parallel overnight in0.1 mg/ml yeast tRNA and 1 mg/ml BSA in IP bu er.

Following the overnight incubation, beads were washed once with IP bu er. The chromatin/antibodiesmixture was then added to the beads and incubated at 4C or an additional 2 hr. Beads were then

washed once or 10 min with HSE I (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Hepes KOH,pH 7.6, 150 mM NaCl) and twice or 10 min with HSE II (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mMHepes KOH, pH 7.6, 500 mM NaCl). Beads were then briefy washed with ice-cold TE and eluted with200 l o ChIP Elution bu er (50 mM TrisHCl pH 8.0, 10 mM EDTA, 1% SDS, 1 mM DTT). 175 l o ChIPElution bu er was also added to the 25 l input samples. Elution was per ormed at 65C or 618 hr.

The resulting supernatant was removed, supplemented with 200 l o TE and 8 l o 1 mg/mlRNAse A (Ambion Cat #2271) and incubated at 37C or 30 min. Then, 4 l o 10 mg/ml proteinase K was added and samples were incubated at 55C or another 2 hr. DNA was then isolated with PCRpuri cation kit (Qiagen) and eluted with 40 l o elution bu er.

Generation o Illumina ChIP-seq librariesBMAL1, CLK and input libraries were made rom ChIPs per ormed rom the same mouse liver extract.ChIP-seq libraries were made as described by Schmidt et al. (2009) . Size-selected libraries o 200300 bplength were used or Illumina deep-sequencing, whereas libraries with a 300650 bp length were used

or qPCR validation o the quality o the ChIP-seq libraries.

High-throughput sequencing o Illumina librariesHigh-throughput sequencing has been per ormed as ollow:

- ChIP-seq libraries: BMAL1, CLK and Input ChIP-seq libraries were sequenced using anIllumina Genome Analyzer (GAII) with a sequencing length o 36 nt. To increase depth coverage,libraries were sequenced on multiple lanes (BMAL1: our lanes, CLK: ve lanes and Input:three lanes).- Nascent-Seq libraries: libraries (12 samples corresponding to two independent six-time pointsrhythms) were sequenced using an Illumina Genome Analyzer (GAII) with a sequencing length o 80 nt. Both replicates o the ZT8 and ZT20 time points were sequenced on two lanes and all

other samples on one lane.- Nascent-Seq libraries, strand-speci c: libraries (six samples corresponding to the rst replicateo the six-time points rhythm) were generated using bar-coded adaptors, mixed in anequimolar ratio and sequenced on two lanes using a HiSeq2000 (Illumina) with a sequencinglength o 101 nt.- RNA-Seq libraries: libraries (12 samples corresponding to two independent six-time pointsrhythms) were generated using bar-coded adaptors, mixed in an equimolar ratio and sequencedon two lanes using a HiSeq2000 (Illumina) with a sequencing length o 101 nt.

High-throughput sequencing has been per ormed ollowing manu acturer recommendations and

812 pmol o libraries were hybridized to each lane o the fow-cells. Data were extracted and pro-cessed ollowing Illumina recommendations. Sequences were aligned to the mouse genome (UCSCversion mm9 database). Number o the sequences obtained or each library can be ound in Table 1

Datasets are deposited on the Gene Expression Omnibus database under the accession numberGSE36916 (GEO, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916 ).

Analysis o Nascent-Seq and RNA-Seq datasetsAlignment to the mouse genome (mm9 version)Sequences ( astq ormat) were rst mapped with tophat ( Trapnell et al., 2009 ) using the ollowingcriteria: -m 1 -g 1 --microexonsearch --no-closure-search -I 500000 (command line: tophat -m 1 -F0 -g 1 --microexon-search --no-closure-search -G ../exon20110528mm.gt --phred64-quals -I500000 -o ZT4_2_tophat.out /data/analysis/ asta/mm9 /data/sequence/Nascent_ZT4_2. astq).About 6570% o the Nascent-Seq sequences uniquely mapped to the mouse genome, even
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Table 1. Number o sequences and statistics or the di erent sequencing datasets

Index number Barcode

Number o sequences( astq fle)

Numbero uniquelymappedsequences

Percentageo uniquelymappedsequences

Normaliz.actor

ChIP-Seq libraries

Input 39,214,696 18,846,303 48.1%

CLK 75,944,495 37,371,047 49.2% BMAL1 60,952,293 28,920,754 47.5%

Nascent-Seq libraries

Norm. 40 m

Rep1_ZT0 27,845,320 18,319,011 65.8% 2.184

Rep1_ZT4 30,088,981 20,931,038 69.6% 1.911

Rep1_ZT8 57,719,174 39,567,609 68.6% 1.011

Rep1_ZT12 29,442,244 19,485,102 66.2% 2.053

Rep1_ZT16 27,645,102 18,385,668 66.5% 2.176

Rep1_ZT20 50,331,242 34,703,727 69.0% 1.152

Rep2_ZT0 30,243,856 21,014,087 69.5% 1.903

Rep2_ZT4 30,162,514 21,082,498 69.9% 1.897Rep2_ZT8 51,471,477 36,118,068 70.2% 1.107

Rep2_ZT12 27,304,921 17,815,971 65.3% 2.245

Rep2_ZT16 27,196,805 19,077,433 70.2% 2.097

Rep2_ZT20 51,105,236 33,547,439 65.7% 1.192

RNA-Seq libraries

Norm. 40 m

Rep1_ZT2 2 CGATGT 13,031,496 8,693,555 66.7% 4.601

Rep1_ZT6 4 TGACCA 13,197,078 10,214,580 77.4% 3.916

Rep1_ZT10 5 ACAGTG 13,479,636 9,916,774 73.6% 4.034

Rep1_ZT14 6 GCCAAT 10,366,702 7,497,386 72.3% 5.335

Rep1_ZT18 7 CAGATC 13,147,649 9,600,125 73.0% 4.167Rep1_ZT22 12 CTTGTA 11,182,756 8,233,815 73.6% 4.858

Rep2_ZT2 13 AGTCAA 14,645,263 9,876,359 67.4% 4.050

Rep2_ZT6 14 AGTTCC 15,836,013 12,270,338 77.5% 3.260

Rep2_ZT10 15 ATGTCA 15,123,726 11,507,856 76.1% 3.476

Rep2_ZT14 16 CCGTCC 12,127,102 8,594,609 70.9% 4.654

Rep2_ZT18 18 GTCCGC 12,903,678 9,512,765 73.7% 4.205

Rep2_ZT22 19 GTGAAA 13,438,873 9,592,404 71.4% 4.170

Strand-speci cNascent-Seq libraries

Norm. 40 m

Rep1_ZT0 2 CGATGT 34,386,622 15,930,801 46.3% 2.511Rep1_ZT4 4 TGACCA 45,356,906 24,224,151 53.4% 1.651

Rep1_ZT8 5 ACAGTG 44,309,216 24,275,357 54.8% 1.648

Rep1_ZT12 6 GCCAAT 49,118,104 22,882,163 46.6% 1.748

Rep1_ZT16 7 CAGATC 49,535,738 21,835,605 44.1% 1.832

Rep1_ZT20 12 CTTGTA 54,905,005 32,586,396 59.4% 1.228

DOI: 10.7554/eLi e.00011.022


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though no rRNA removal has been per ormed. Uniquely mapped sequences rom the tophat out-put les (bam ormat) were then used or urther analysis. Wig les, used or signal visualizationwith the Integrated Genome Browser ( Nicol et al., 2009 ), were created as described in UCSCwebsite ( tp://hgdownload.cse.ucsc.edu/apache/htdocs-rr/goldenPath/help/bedgraph.html ) andnormalized to uniquely mapped reads. The normalization actor used or normalization is indi-cated in the last column o the Table 1 .

Quantifcation o gene signalQuanti cation o nascent RNA and mRNA signal has been calculated rom the Nascent-Seq andRNA-Seq datasets using custom-written perl and mysql scripts (see Source code 1 ). No quanti ca-tion was per ormed using the strand-speci c Nascent-Seq datasets. To allow direct comparisonbetween the Nascent-Seq and RNA-Seq datasets, and because many genes have another genecoding or non-coding RNA expressed within their introns (e.g., Camk2b , Figure 2F ), the gene signalwas calculated by only considering the sequences mapping to exons. Moreover, because onlyapproximately 13% o the Nascent-Seq signal is mapped to exons (most o the signal is intronic), wereached a higher sequencing depth or the Nascent-Seq samples (>15,000,000 uniquely mappedsequences or all samples, representing a minimum o 160,000,000 nucleotides mapping to thetranscriptome).

To allow comparison between the Nascent-Seq and RNA-Seq datasets, individual gene signal wasquantitated as previously described ( Rodriguez et al., 2012 ) by calculating the number o reads map-ping to all exons (regardless o the iso orms) and by normalizing this number to the total exon length

(hence the signal was called Reads per Base Pair, or rpbp). This rpbp number was then normalized tothe sample sequencing depth (uniquely mapped sequences) and used or urther analysis (see below).

Determination o rpbp threshold: the rpbp threshold has been determined so that the di erencein gene signal refects biological variation rather than low sequencing depth. To this end, we tookadvantage o libraries sequenced on two independent fow-cell lanes and with a similar number o sequences in each lane (e.g., Nascent-Seq, replicate 1, ZT8; 19,207,641 and 20,364,883 sequencesrespectively).

We rst assayed the correlation between gene signals o the two duplicates: the correlation is verygood or high values o rpbp, and progressively decreases as the rpbp decreases ( Figure 1G ). The variationo gene signal that results rom low sequencing depth was then determined by calculating the standarddeviation to the mean (z-score) or every gene between the two duplicates ( Figure 1H ). As expected,the z-score is low or high rpbp and increases with lower gene signal, refecting a progressive decreaseo sequencing depth ( Figure 1H ).

Analysis o this variation allowed us to determine the rpbp threshold. Indeed, among genes with arpbp superior to 2, less than 6.7% display a variation o more than 1.3- old between the two duplicates(432/6486) and only 88 genes (1.36%) display a variation o more than 1.5- old (88/6486; see Table 2 )Moreover, the majority o these genes are relatively short (1 ( rom8868 or ZT16_rep2 to 10419 or ZT0_rep2), 5100 genes have a rpbp > 2 ( rom 5121 to 6999) and
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Table 2. Determination o the rpbp threshold or the Nascent-Seq dataset

Folddi erence

Rpbp > 3 Rpbp > 2.6786 Rpbp > 2# Genes % Genes # Genes % Genes # Genes % Genes

>2 0 0.00 0 0.00 2 0.03

>1.5 32 0.77 44 0.93 88 1.36

>1.4 67 1.61 93 1.97 180 2.78

>1.3 169 4.06 224 4.74 432 6.66

>1.2 543 13.06 689 14.57 1159 17.87

>1.1 1698 40.83 2015 42.62 3012 46.44

1.01.1 2461 59.17 2713 57.38 3474 53.56

Total # genes 4159 4728 6486

DOI: 10.7554/eLi e.00011.023

3200 genes have a rpbp > 3 ( rom 3215 to 4795). More than 1000 genes have a rpbp > 10 in everysamples. A le containing the normalized rpbp or all UCSC genes is available in supplementarymaterial ( Figure 2source data 1 ). The normalization to sequencing depth (uniquely mapped

sequences) was per ormed using the normalization actors displayed in the Table 1 .All RNA-Seq samples exhibit a relatively similar pro le o expression, refecting the low vari-ation in sequencing depth between the samples. A minimum o 8800 genes have a rpbp >1 ( rom8868 or ZT16_rep2 to 10419 or ZT0_rep2), 5100 genes have a rpbp > 2 ( rom 5121 to 6999) and3200 genes have a rpbp > 3 ( rom 3215 to 4795). More than 1000 genes have a rpbp > 10 in everysample. A le containing the normalized rpbp or all UCSC genes is available in supplementary mater-ial (Figure 3source data 2 ). The normalization to sequencing depth (uniquely mapped sequences)was per ormed using the normalization actors displayed in the Table 1 .

Analysis o ChIP-Seq datasetsSequences rom the di erent libraries ( astq ormat) were rst mapped to the mouse genome (versionmm9) using bowtie ( Langmead et al., 2009 ) with the command line: bowtie q a -best m 1. Onlythose that mapped uniquely to the mouse genome were used or urther analysis, and their number

has been used or normalization to compare signal di erence between libraries.ChIP-seq libraries were analyzed with the MACS algorithm ( Zhang et al., 2008 ) by comparingthe treatment sample (BMAL1 or CLK ChIP) to the control sample (Input) using the ollowing cri-teria: e ective genome size = 1.89 10 9, tag size = 36, band width = 80, model old = 5, p-valuecuto = 1 10 5 . Signi cant peaks were computationally assigned to a gene. Briefy, a peaklocated between the transcription start site and the transcription start end o a gene was assignedto that gene, regardless o the ChIP-Seq peak position. The other peaks, re erred as intergenic,were assigned to the gene with the closest transcription start site. Con rmation o this computa-tional gene assignment was then con rmed by manual inspection or the 211 CLK:BMAL1 peaks.

Visualization o the ChIP-seq s ignal was per ormed using the wig output le ( rom the MACS ana-lysis) and the IGB browser.

Overlap between CLK and BMAL1 DNA binding peaks has been determined computationally usingall signi cant peaks coordinates. Any overlap between the two peaks (even o one nucleotide) was

considered signi cant. Quanti cation o the signal has been extracted rom the raw data (number o readsper bp) normalized to sequencing depth o each library. For most experiments (e.g., Figure 7A,B )signal was binned using a 25 bp window.

Quanti cation o the number o e-boxes within BMAL1 and CLK DNA binding peaks has beenper ormed computationally using peak asta sequences. Enrichment has been calculated using thenumber o e-boxes ound at a xed position rom the peak center divided by the expected number o e-boxes. The maximal window size (di erence rom the xed position and the peak center) was 500 nt,as the number o expected e-boxes dropped to the background at this window size.

Moti analysis has been per ormed using MEME suite ( http://meme.sdsc.edu/meme/intro.html )using a 100 bp sequence or each peak (peak center 50 bp). Parameters were as ollow: -dna -mod
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anr -nmoti s 20 -minw 6 -maxw 30. The background model contained the same nucleotide distributionas the input le. Signi cant moti s were then analyzed using TOMTOM rom MEME suite.

Gene ontology o genes with high and low Nascent-Seq/RNA-Seqsignal ratioGene signal (reads per base pair) was averaged or the 12 time points, and the ratio NascentRNA/mRNA was calculated. Genes with a ratio over 2 SD rom the average o all ratios were selected

or gene ontology (GO) analysis; 302 genes had a Nascent-Seq/RNA-Seq ratio below 2 SD and

463 genes had a ratio over 2 SD. 13595 genes were considered or this analysis. GO analysis hasbeen per ormed using GOToolBox ( Martin et al., 2004 ) (http://genome.crg.es/GOToolBox/ ), usingan hypergeometric test with Benjamini-Hochberg correction. The background model consisted o the entire list o genes.

Statistical analysis o rhythmic gene expressionOnly genes with more than three reads per base pair or at least one time point o the Nascent-Seqdataset and two read per base pair or the RNA-Seq dataset were urther considered or subsequentanalysis (see above). Rhythmically expressed genes were determined based on three parameters:amplitude, F24 (24-hr spectral power, see below) and p-value o the F24.

The amplitude was calculated by dividing the highest value o the 12 time points by the lowest value.The F24 score was calculated by Fourier trans ormation using a R code originally described byWijnen et al. (2005) . Briefy, normalized reads per base pair rom the two independent six-time pointsrhythms were concatenated and the 24-hr spectral power (F24) was determined or each gene. The F24score (expressed in range 01) indicates the relative strength o the extracted rhythmic component.

The F24 p-value (pF24) represents the probability o observing an F24 score rom randomlypermuted data that is o equal or greater strength than the extracted Fourier component. It wascalculated or each gene a ter per orming 10,000 randomized permutations o the rpbp values.A pF24 was considered signi cant i (pF24 < 0.05) i the experimental pF24 was within the top 5%o the 10,000 pF24 calculated rom randomized permutation.

Transcripts were considered to be rhythmically expressed when meeting the three ollowingcriteria: (1) pF24-values < 0.05 (i.e., experimental pF24-value is within the top 5% o the 10,000pF24-values calculated rom randomized permutation), (2) F24 > 0.45 and (3) amplitude (maximal/minimal experimental values) > 1.5. A more stringent cut-o was also used to identi y strong rhythmicexpression: pF24 < 0.05, F24 > 0.6 and amplitude > 1.75.

The phase in ormation rom the Fourier trans ormation (which indicates the peak o the cosinecurve) was urther used to assess phase di erence between rhythmic nascent RNA and mRNA expression(Figures 3C,4B ).

Analysis o gene expression by real-time PCRTotal RNA rom wild-type and Bmal1/ mice was prepared rom mouse liver using Trizol reagent(Invitrogen) and DNAse-treated using Turbo DNAse (Ambion) according to the manu acturers proto-cols. cDNA derived rom RNA (using Invitrogen Superscript II and random primers) was utilized as atemplate or quantitative real-time PCR per ormed with the Rotor-Gene 3000 real-time cycler (Qiagen).The PCR mixture contained Platinum Taq polymerase (Invitrogen), optimized concentrations o Sybr-green (Invitrogen) and speci c primers or either Rev-Erb , Per1 , Per2 or Cry1 pre-mRNAs.Quantitative PCR using Actg1 -speci c primers was used as a loading control. Cycling parameters were95C or 3 min, ollowed by 40 cycles o 95C or 30 s, 55C or 45 s, and 72C or 45 s. Fluorescence

intensities were plotted vs the number o cycles by using an algorithm provided by the manu acturer.mRNA levels were quanti ed using a calibration curve based upon dilution o concentrated cDNA.

AcknowledgementsWe thank Chris Brad eld (University o Wisconsin-Madison, United States) or providing the Bmal11/mice and the Brandeis University Transgenic Animal Facility or help at various stages o this project.

Yevgenia Khodor helped develop the Nascent-Seq technique, and Cindy He and Andrew Darnellprovided technical help. We also thank Aoi e McMahon and other members o the Rosbash lab aswell as Michael Marr, Nelson Lau, Sebastian Kadener and Christine Merlin or help ul discussions andcomments on the manuscript.
http://dx.doi.org/10.7554/eLife.00011http://genome.crg.es/GOToolBox/http://genome.crg.es/GOToolBox/http://dx.doi.org/10.7554/eLife.00011


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Research article

Additional in ormation

Funding

FunderGrant referencenumber Author

Howard Hughes Medical Institute Michael Rosbash

National Science Foundation:Integrative Graduate Educationand Research Traineeship

DGE 0549390 Joseph Rodriguez

National Institutes o Health:Genetics Training Grant

5T32GM007122 Joseph Rodriguez

The unders had no role in study design, data collection and interpretation,or the decision to submit the work or publication.

Author contributionsJSM, Per ormed the experiments, Conception and design, Acquisition o data, Analysis and interpret-ation o data, Dra ting or revising the article; JR, Conception and design, Analysis and interpretationo data, Dra ting or revising the article; KCA, Conception and design, Dra ting or revising the article;MR, Conception and design, Analysis and interpretation o data, Dra ting or revising the article

EthicsAnimal experimentation: All experiments were per ormed in accordance with the National Instituteso Health Guide or the Care and Use o Laboratory Animals and approved by the Brandeis InstitutionalAnimal Care and Use Committee (IACUC protocol #0809-03).

Additional flesSource code 1. Source code 1. Perl script used to calculate gene signal as reads per base pairDOI: 10.7554/eLi e.00011.024

Major datasetsThe ollowing datasets were generated:

Author(s) Year Dataset titleDataset IDand/or URL

Database, license, andaccessibility information

Menet JS 2012 Nascent-Seq RevealsNovel Features o MouseCircadian TranscriptionalRegulation

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916

In the public domainat GEO: http://www.ncbi.nlm.nih.gov/geo/

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http://dx.doi.org/10.7554/eLife.00011http://dx.doi.org/10.7554/eLife.00011.024http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://www.ncbi.nlm.nih.gov/geo/http://www.ncbi.nlm.nih.gov/geo/http://www.ncbi.nlm.nih.gov/geo/http://www.ncbi.nlm.nih.gov/geo/http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36916http://dx.doi.org/10.7554/eLife.00011.024http://dx.doi.org/10.7554/eLife.00011


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Nascent-Seq reveals novel features of mouse circadian transcriptional regulation

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