Ribosome profiling reveals the rhythmic liver translatome and circadian clock regulation by upstream open reading frames

1The mammalian circadian system consists of a master pacemaker in the brain's suprachiasmatic nuclei (SCN) that synchronizes subsidiary oscillators present in most cell types. In the liver and other organs, up to 15% of gene expression shows daily oscillations that are driven directly by local clocks, or by systemic signals such as feeding and body-temperature rhythms. Of the molecular mechanisms potentially accounting for rhythmic gene expression, transcription has been extensively studied, notably within the core clock circuitry consisting of transcriptional activators (mainly CLOCK; ARNTL/BMAL1; RORA, RORB, RORC) and repressors (mainly PER1, 2; CRY1, 2; NR1D1/REV-ERB alpha; NR1D2/REV-ERB beta). Their interactions in negative feedback loops generate transcriptional oscillations not only of clock genes, but genome-wide, which has led to the view that transcription represents the dominant driver of gene expression rhythms. However, post-transcriptional mechanisms likely contribute as well. In extension to earlier work showing poor overlap between liver proteome and transcriptome rhythms, two recent studies have indicated that 20% to 50% of cyclically accumulating proteins are expressed from nonoscillating mRNAs. Conceivably, these protein rhythms are generated at the level of translation and/or protein stability. In other fields, translational regulation is emerging as key to understanding the overall moderate correlations between mRNA and protein abundances; a role in rhythmic gene expression is thus conceivable as well.

2Time of day–dependent translation is not unprecedented in mammals. Transcripts encoding ribosomal proteins (RPs) associate with polysomes preferentially at the beginning of the night, coincident with feeding time. These mRNAs contain 5′-terminal oligopyrimidine (5′-TOP) motifs that are regulated by the nutrient-sensitive mammalian target of rapamycin complex 1 (TORC1) pathway. Another documented mechanism for rhythmic translation involves daily dynamics in poly(A) tail length and the rhythmic activity of cytoplasmic polyadenylation element-binding proteins (CPEBs). Despite such individual examples, a comprehensive and quantitative analysis of rhythmic translation from a mammalian organ is still lacking.

3We have used ribosome profiling (RPF-seq), a method based on the massively parallel sequencing of ribosome-protected mRNA footprints, to determine the positions of translating ribosomes transcriptome-wide and to establish a quantitative, high-resolution map of the mouse liver translatome around the clock. From RPF-seq and matching whole-transcriptome sequencing (RNA-seq) data, we determined the relationship between translation and mRNA abundance rhythms, uncovered the set of mRNAs for which these rhythms are uncoupled, and calculated translation efficiencies transcriptome-wide. Moreover, we inferred the relative levels of clock protein biosynthesis and identified upstream open reading frame (uORF) translation as a novel regulatory mechanism within the clock circuitry. Altogether, our study reveals key features of rhythmic protein biosynthesis and the impact of translational control on gene expression in a solid, highly differentiated mammalian organ with well-studied functions.

4We collected time-resolved ribosome profiling data from 48 male mice entrained to light-dark cycles and euthanized at 2-h intervals around the clock. We assembled liver extracts into two independent replicate time series (12 timepoints, Zeitgeber time ZT0 to 22) (Fig. 1A), prepared ribosome footprints and matching total RNA, converted them into sequenceable libraries (Fig. 1B), and sequenced them with high coverage (Supplemental Table S1). Our protocol yielded high-quality footprints that mainly mapped to protein coding sequences (CDS) and were depleted from untranslated regions (UTRs) of mRNAs (Fig. 1C,D; Supplemental Table S1). Moreover, the predominant footprint length of 29–30 nt (Fig. 1E) allowed the precise identification of translated codons. The alignment of CDS-mapping reads relative to the position of the ribosome's aminoacyl tRNA-site (A-site; inferable from footprint length and sequence) thus revealed excellent reading frame preference (Fig. 1F) and captured the CDS triplet codon composition transcriptome-wide (Fig. 1G). These characteristics were absent in the RNA-seq data, as expected (Fig. 1D–G; Supplemental Fig. S1A). Moreover, the quantification of CDS-mapping reads showed high reproducibility across biological replicates (Supplemental Fig. S1B,C). Finally, principal component analysis (PCA) on the ensemble of data sets (Fig. 1H) separated RPF-seq and RNA-seq data on PC1 and recapitulated its cyclic nature with near-perfect temporal resolution (PC2 and PC3); i.e., the covariates in the experimental design (RNA/RPF and factor time) were retrieved by an unsupervised method for sample clustering. We concluded that the data sets were of high technical quality and would be suitable for comprehensive analyses of rhythmic and constitutive translation in the liver.

5Ribosome profiling from mammalian tissues is still relatively uncommon, and we therefore started with a characterization of general properties of the translatome data, independently of its time resolution. From the ratio of CDS-mapping RPF-seq to RNA-seq reads, we first computed relative ribosome occupancies, which can be interpreted as relative translational efficiencies (TEs) because each footprint reflects the synthesis of an individual protein molecule and, importantly, integrating read numbers across the entire CDS corrects for local variation in footprint density. Briefly, while the local speed of translation elongation may vary (e.g., ribosome pausing due to RNA structure or codon usage) and represents a source of inhomogeneous footprint distribution on a given CDS, average translation speeds across genes appear to be rather constant. It should be noted, however, that a possible influence of local variation on overall translation speed of an mRNA has been suggested and is a current topic of debate.

6In mouse embryonic stem cells (mESCs), TEs cover an approximately 10-fold range and have an asymmetric distribution indicative of an intrinsic upper limit that is transcript-specifically decreased by inhibitory mechanisms. TEs in liver showed a broader dynamic range and analogous asymmetry (Fig. 2A). While multiple mechanisms are likely involved in establishing transcript-specific ribosome occupancies, several simple transcript features have previously been observed to correlate with TEs. In yeast, ORF length and translation rate correlate inversely, presumably due to a selection for faster translation initiation on loci encoding short proteins. Also in the liver data set, CDS lengths explained a significant proportion of variance in ribosome occupancies (R2= 0.16; P = 1.26 × 10−160) (Fig. 2B). 5′ UTR (R2= 0.047; P = 2.33 × 10−46) and 3′ UTR lengths (R2= 0.015; P = 4.29 × 10−16) correlated with TEs as well (Fig. 2B), and the predictive power of the 5′ UTR length remained significant even after correction for interdependence of UTR and CDS lengths (Supplemental Fig. S2A–C). These results are consistent with a prominent role for 5′ UTRs in translational control and with the idea that translation regulatory elements—of which longer 5′ UTRs potentially contain more—are predominantly inhibitory.

7Within 5′ UTRs, uORFs are emerging as important cis-regulatory elements that control CDS translation, usually in an inhibitory fashion. Six percent of RPF-seq reads fell into annotated 5′ UTRs (Fig. 1C), albeit with pronounced transcript-specific variability (Supplemental Fig. S3A), suggestive of abundant uORF usage in the liver. uORFs are short and often poorly conserved and frequently initiate at near-cognate (non-AUG) start codons, complicating prediction just from sequence. To explore uORF usage in the liver, we therefore compiled a uORF-enriched transcript set based on whether the 5′ UTRs harbored sequence stretches (1) embraced by AUG initiation and stop codons and (2) covered by footprints with distinct reading frame preference. Despite these simple criteria that miss, for example, non-AUG-initiated uORFs, the detected transcripts showed significantly lower main ORF TEs (difference in location of red vs. gray densities on the ordinate of −0.525 corresponds to >30% TE reduction; P = 2.2 × 10−16; Wilcoxon rank-sum test) (Fig. 2C), whereas the TEs of transcripts lacking translated uORFs were slightly increased (Fig. 2C, blue). Importantly, the correlation of low TEs with translated uORFs was independent of 5′ UTR (or CDS/3′ UTR) lengths (Supplemental Fig. S2D–F). Instead, linear regression analysis uncovered that the underlying cause for the correlation of TE with 5′ UTR length (see above) (Fig. 2B) was uORF presence rather than 5′ UTR length per se (Supplemental Fig. S2, cf. G–K and A–C). Finally, uORF-containing mRNAs were globally less abundant (Fig. 2C, red density on abscissa), possibly reflecting the activity of the nonsense-mediated mRNA decay (NMD) pathway that selectively degrades mRNAs with premature termination codons and that is known to act on uORF-containing transcripts. We concluded that uORF usage is frequent in the liver and likely a major determinant of transcript-specific TEs, correlating with reduced protein biosynthesis from the CDS. Of note, we also analyzed another feature that may have been expected to correlate with TE, i.e., the presence of pause sites. By using an analogous approach to to identify local variation in footprint density that exceeded the CDS median (more precisely, we used the similar, but for low translated transcripts, less stochastic “trimean”) by a certain threshold, we found that the presence of such sites had no predictive power for translation rates (Supplemental Fig. S4A,B).

8We used the clock genes for first temporal analyses of the data sets. As illustrated by the anti-phasic expression of Arntl and Nr1d1, core clock transcripts were detected with high coverage and oscillated in both the RNA-seq and RPF-seq data sets (Fig. 3A). Read count integration over the CDS indicated that for all core clock components, footprint profiles closely matched mRNA abundance rhythms (Fig. 3B). We concluded that the rhythmic biosynthesis of core clock proteins was determined by mRNA availability with no further regulation by time of day–dependent translation (Supplemental Fig. S5A). We next conducted transcriptome-wide rhythmicity analyses. Applying a more than 1.5-fold peak-to-trough amplitude cut-off, we identified oscillations in the RNA-seq and RPF-seq data sets that affected in both cases ∼17% of the protein-coding transcriptome (almost 1900 mRNAs) (Fig. 4A; Supplemental Table S2). However, mRNA abundance and ribosome occupancy rhythms showed different peak phase distributions (Fig. 4B,C). In good agreement with previous reports, a majority of mRNAs thus showed maximal abundance during the night, with an enrichment around ZT15–ZT19 (Fig. 4B). In contrast, maximal translation was prevalent at the beginning of the dark phase, with a dominant peak around ZT15–ZT16 (Fig. 4C). These different distributions resulted from transcripts that were unique to either data set rather than from phase delays occurring between mRNA accumulation and translation, because the intersecting set of 1192 “mRNA and footprints rhythmic” transcripts (Fig. 4A) showed near-identical RNA-seq and RPF-seq oscillations (Fig. 4D–G; Supplemental Fig. S6A). We concluded that whenever both mRNA abundance and ribosome occupancy cycled, they globally did so in sync. Similar to the core clock components (Fig. 3), most rhythmic mRNAs were thus translated concomitant with their cellular accumulation and had constant TEs. Distinct out-of-phase translation was indeed confined to rather few exceptional cases (Supplemental Fig. S6B). Finally, it is noteworthy that the TEs of “mRNA and footprints rhythmic” transcripts were slightly increased compared with the global population of expressed transcripts (location shift of dark blue vs. gray densities of 0.106 corresponds to ∼8% higher TEs; P = 2.2 × 10−05; Wilcoxon rank-sum test) (Fig. 4H). No enrichment or depletion for rhythmic genes was seen with regard to AUG-initiated uORFs or pause sites (P = 0.533 and P = 0.315, respectively; Fisher's exact test).

9More than one-third (682/1874) of genes that cycled at the footprint level did not have a rhythmic mRNA (Fig. 4A); globally, these transcripts showed decreased TEs (location shift of −0.105 corresponds to a 7% TE reduction; P = 0.002) (Fig. 4H, light blue). However, closer inspection of the underlying RPF-seq and RNA-seq profiles indicated that in many cases, nonrhythmicity assignments at the mRNA level had resulted from noise or low amplitudes (close to the imposed 1.5-fold cut-off) in expression profiles that otherwise still appeared to be rhythmic (false-negatives caused by “cliff effects”) (Supplemental Fig. S6C). To refine the “mRNA flat–footprints rhythmic” assignments, we used the analytical framework Babel to first identify all transcripts that had significant TE differences over timepoints (and/or whose TEs significantly deviated from the global transcript population), and subsequently performed the rhythmicity analyses on these. This strategy resulted in a high-confidence set of 147 rhythmically translated, but otherwise nonoscillating, mRNAs (Fig. 5A; Supplemental Table S3). Their RPF-seq profiles showed a striking phase distribution with a dominant peak around the day-to-night transition (ZT10–ZT16) (Fig. 5B). Gene ontology (GO) analyses revealed enrichment for mRNAs encoding components of the protein biosynthesis machinery, including RPs, elongation factors, and poly(A) binding proteins, whose translation underwent a characteristic upsurge starting from ZT10 (Fig. 5C,D). Of note, increased polysome association at the beginning of the night has previously been described for this class of transcripts, which all contain 5′-TOP motifs that are regulated by TORC1. Of 79 RPs, 35 were contained in the high-confidence list, and—with the exception of eight proteins whose mRNAs were undetectable or translationally invariable—visual inspection confirmed that most other RPs shared a similar RPF-seq profile as well (Supplemental Fig. S7A; Supplemental Dataset 1). In summary, our data extend previous findings and precisely quantify the coordination of protein biosynthesis within the translational apparatus in mouse liver. Of note, our study uncovers a peculiarity of RP gene expression, i.e., particularly high mRNA abundances paired with low TEs, which undergo coordinated upsurge/translational de-repression prior to the day-to-night transition (Supplemental Fig. S7B; Supplemental Movie M1).

10Other GO terms and individual “mRNA flat–footprints rhythmic” transcripts caught our attention as well. The electron transport chain components that the GO analysis identified (Fig. 5C) corresponded throughout to mitochondrially encoded transcripts characterized by a translational spike at ZT12 (Supplemental Fig. S8A; Supplemental Dataset 1). As Western blot analysis did not reveal any oscillations at the protein level (Supplemental Fig. S8B), the significance of these translational rhythms remains to be uncovered. We next verified for other examples of rhythmic translation whether protein abundances oscillated. This was indeed the case for geranylgeranyl diphosphate (GGPP) synthase 1 (Ggps1), encoding a key branchpoint enzyme in the mevalonate pathway (Supplemental Fig. S8C,D). GGPP is important for the C20-prenylation of proteins and for the regulation of the nuclear receptor NR1H3/LXR alpha. Moreover, two transcription factors, Deformed epidermal autoregulatory factor 1 (Deaf1) and Max interactor 1 (Mxi1), showed robust greater than twofold rhythms in translation (Fig. 5E; Supplemental Fig. S8E). For DEAF1, steady-state protein levels oscillated as well (Fig. 5E), raising the interesting possibility that translational rhythmicity is propagated to transcriptional target genes and thus contributes to shaping the rhythmic transcriptome. In accordance, previously reported DEAF1 targets were significantly enriched for genes that are rhythmically transcribed (enrichment 1.6-fold; P = 0.009) (Fig. 5F; identified by ). Notably, the majority of DEAF1 target gene pre-mRNAs peaked between ZT6 and ZT12, coinciding with maximal DEAF1 protein abundance (Fig. 5E,F).

11Finally, our data revealed that the well-known case of translational control through iron-responsive elements (IREs) undergoes high-amplitude oscillations. IREs are stem–loops found in mRNAs involved in iron, oxygen, and energy metabolism (see and references therein). Depending on the cytosolic iron concentration and other cues (see Discussion), IREs located in 5′ or 3′ UTRs regulate mRNA translation and degradation, respectively. Ferritin heavy and light chain 1 (Fth1, Ftl1), involved in iron storage, as well as aminolevulinic acid synthase 2 (Alas2), a rate-limiting enzyme of heme synthesis, showed high-amplitude oscillations in translation that led to protein rhythmicity (Fig. 5G). All three transcripts contain IREs in their 5′ UTRs, whereas transferrin receptor (Tfrc) is a case where a 3′ UTR–borne IRE controls mRNA turnover. Tfrc mRNA (and footprint) profiles were rhythmic in our data set (Supplemental Dataset 1); moreover, previous RNA-seq data quantifying pre-mRNA and mRNA levels around the clock revealed that the abundance of Tfrc mRNA, but not of its pre-mRNA (and hence, its transcription), oscillated (Supplemental Fig. S8F). These analyses suggest that regulation by IREs is overall under time of day–dependent control. Of note, besides the temporal regulation of IRE-containing transcripts, we observed high-amplitude oscillations in gene expression (RNA-seq and RPF-seq) throughout key steps in iron metabolism (Supplemental Fig. S8G). These findings indicated widespread rhythmic regulation of iron homeostasis that has been largely overlooked so far.

12Although core clock transcript TEs were temporally invariable (see above) (Fig 3B; Supplemental Fig. S5A), further analyses of the data revealed features of regulation that were potentially of functional importance. First, we noticed that the intrinsic TEs of clock mRNAs spanned a greater than sixfold range from the poorest (Clock, Per3) to the best (Nr1d1) translators (Supplemental Fig. S5A,B, upper panel). TEs and mRNA abundances together define the amounts of protein that are produced, and RPF-seq RPKMs are hence a direct readout of relative protein biosynthesis levels. We therefore used our footprint counts to precisely quantify the stoichiometry at which the clock proteins are produced. Importantly, these estimates explained known features of the clock better than did RNA expression data alone. Notably, Clock is in excess of its heterodimerization partner Arntl at the transcript level (integrated over the day, about 1.6-fold more Clock than Arntl mRNA), but due to TE differences, about 1.5-fold more ARNTL than CLOCK protein is produced (Supplemental Fig. S5B; for similar results obtained when peak levels rather than daily amounts were considered, see Supplemental Fig. S5C), which is consistent with the conjecture that ARNTL is in excess over CLOCK. For the main positive and negative limb components, our data indicated daily biosynthesis at a ratio of CLOCK(1.0):ARNTL(1.5):PER1(0.6):PER2(1.1):CRY1(2.3):CRY2(2.3), i.e., overall similar levels of produced proteins (Supplemental Fig. S5B). Finally, it is established that in the interconnecting limb NR1D1/REV-ERB alpha represents the dominant Rev-erb paralog in liver. While Nr1d2/Rev-erb beta is nevertheless significantly more abundant at the mRNA level, the amount of biosynthesized NR1D1 protein exceeds the NR1D2 paralog by greater than twofold due to greater than fivefold differences in TEs (Supplemental Fig. S5B, lower panel). In summary, these analyses suggest that TE is an important factor in establishing the appropriate clock protein output. Conceivably, it may represent an additional layer at which the core clock can undergo regulation.

13The striking correlation of uORF usage with TEs (Fig. 2C) prompted us to explore whether translated uORFs were present in core clock transcripts. Intriguingly, Arntl, Clock, Cry1, Nr1d1, and Nr1d2, all showed considerable ribosome occupancy in their 5′ UTRs and contained one or more AUG-initiated uORFs (Fig. 6A) with footprint coverage that showed clear frame preference, indicative of their active translation (Fig. 6B). To investigate how uORFs regulated the rhythmic production of a clock protein, we chose Nr1d1, for which RPF-seq reads on uORFs 1 and 2 showed particularly high coverage (Fig. 6A), frame bias (Fig. 6B), and rhythmicity in sync with the main ORF (Supplemental Fig. S9A). Moreover, uORF1 was remarkably long (192 nt) and conserved in mammals, potentially coding for a 63-amino-acid polypeptide (Supplemental Fig. S9B). We constructed a lentiviral reporter gene from an Nr1d1 genomic fragment that contained promoter sequences conferring rhythmic transcription, exon 1 (5′ UTR and codons 1–10), intron 1, and a modified exon 2, in which firefly luciferase (FL) was fused to NR1D1 after amino acid 15 (Fig. 6C). Moreover, we designed reporter variants in which the predicted uORF initiation codons were mutated or the uORFs were deleted altogether (Supplemental Fig. S9C). When expressed in a circadian model cell line, NIH3T3 fibroblasts, all constructs showed comparable high-amplitude bioluminescence rhythms in the detrended data (Fig. 6C), indicating that uORFs were dispensable for rhythmic protein expression per se. Importantly, the differences in absolute luciferase signals that we observed between reporters (Supplemental Fig. S9D,E) could have resulted from altered TEs or simply reflect unequal lentiviral titers, transduction efficiencies, cell numbers, or similar. We therefore measured the effect of the uORFs on main ORF translation in an independent assay in which the 5′ UTRs were cloned upstream of FL CDS in a lentiviral vector that also expressed an internal control gene, Renilla luciferase (RL), driven from the same bidirectional promoter (Fig. 6D, top; ). These analyses revealed that uORF deletions, or the subtle initiation codon mutations, led to increased levels of FL reporter activity (Fig. 6D, bottom). Moreover, uORF1 and uORF2 had an additive effect, as judged by mutants in which uORF1 and uORF2 AUGs were mutated to alanine codons either singly (mutants M1A-uORF1 and M1A-uORF2) or in combination (M1A-uORF1+2). We next measured RNA expression levels of both luciferases, which allowed us to estimate the relative contributions that altered RNA stability (lighter shading in Fig. 6D, bottom) and translation regulation (darker shading in Fig. 6D, bottom) made to the observed increases in reporter protein output. These analyses suggested dual contribution by both mechanisms, in line with the initial observation (Fig. 2C) of decreased TE and mRNA abundance of uORF-containing transcripts (the latter possibly involving regulation through the NMD pathway). Extrapolated to the regulation of the endogenous Nr1d1 transcript in vivo, the prediction from these results would be that uORF1+2 could regulate the magnitude of NR1D1 oscillations.

14There was evidence for translated uORFs in several core clock transcripts (Fig. 6A,B), but the net regulatory effect of uORF translation on clock function would likely be difficult to predict from studying each case individually. In order to estimate the overall impact of uORF translation on the clock, we took advantage of the recent discovery that Density Regulated Protein (DENR) is implicated in ribosome recycling after translation termination and acts as a selective regulator of reinitiation at the main CDS after uORF usage. In the absence of DENR, proteins whose expression is regulated by uORFs are thus produced at lower levels. We down-regulated endogenous Denr in NIH3T3 cells carrying a circadian Dbp-Luciferase reporter gene using three different shRNAs (Fig. 6E). Denr-deficient cells showed robust period shortening of free-running circadian oscillations by up to 1.5 h (Fig. 6F); moreover, a short period phenotype was also observed using a second reporter, Arntl-Luciferase (Supplemental Fig. S9F). We concluded that DENR has important functions in the regulation of the mammalian circadian clock.

15How translation efficiency contributes to temporal gene expression is a largely unexplored facet of chronobiology. Translation is one step closer than the mRNA to the relevant output of most gene expression, the protein. Our ribosome profiling data should therefore be of wide interest to the research community and complement the many transcriptome data sets that are already available. By providing a resource consisting of transcriptome-wide RPF-seq/RNA-seq and TE plots (Supplemental Dataset 1), associated rhythmicity parameters (Supplemental Table S2), and high-confidence transcript lists from Babel analysis (Supplemental Table S3), we wish to facilitate the widespread use of our data. This resource provides a number of straightforward opportunities for exciting future endeavors, one example being the interesting cases of genes whose mRNAs, but not the footprint profiles, oscillate (Fig. 4A; Supplemental Fig. S6C), which we were unable to further investigate within the scope of this study.

16Our study provides part of the explanation for the longstanding enigma that the mRNAs of many oscillating proteins show constant abundance over the day. Two recent reports have estimated that 20% to 50% of protein rhythms are engendered by translation, protein degradation, or secretion. Our high-confidence set of approximately 150 nonoscillating mRNAs that undergo robust daily TE rhythms corresponds to ∼8% of all detected rhythmically biosynthesized proteins. Considering the conservative selection criteria that were applied, the true extent of translationally driven rhythmicity may even be higher.

17How does the translatome data correlate with the rhythmic proteome? The answer to this question is less straightforward than expected. A first complication lies in the poor overlap of the proteomics studies; although both report on almost 200 rhythmic proteins, <20% are shared between the studies, and the particularly interesting “mRNA flat–protein rhythmic” class has only three proteins in common (Supplemental Fig. S10A,B). As the overlap in the total detected proteome (about 3000–5000 polypeptides in both studies) is >50%, the differences seen in the rhythmic sets are not just a matter of proteome coverage. Differences in mass-spectrometric and, very likely, rhythmicity detection methodology may have caused the discrepancies; sophisticated meta-analyses on all available raw data using comparable algorithms and statistical parameters would thus be of great value. Another concern when comparing RPF-seq and proteome data sets is that MS data are inevitably biased for abundant (i.e., highly expressed and/or stable) polypeptides, whereas rhythmic TEs predominantly affected mRNAs whose abundance was below average (Fig. 4H). Many translationally regulated transcripts are hence not covered by the proteome data. It is reassuring that, despite such limitations, several translationally rhythmic transcripts are part of the rhythmic proteome (e.g., FTH1, EEF1A1, and EEF2 in study by ).

18Transcripts encoding components of the protein biosynthesis machinery stand out among the rhythmically translated mRNAs. Their preferential association with polysomes at the light-to-dark transition has been reported before; it likely gates the energy-consuming ribosome biogenesis to the appropriate time when nutrients are plentiful, and involves regulatory cues from both feeding (via TORC1-regulated 5′-TOP motifs) and from the clock. As we observed increased TEs on these mRNAs already at ZT10 (Fig. 5D), i.e., ∼2 h before the main surge in food intake in ad libitum fed animals, we consider it likely that the mechanism entails more than a simple, immediate reaction to nutrients. Moreover, the relatively variable up-regulation seen across biological replicates (Fig. 5D, RPF-seq error bars) is remarkable for genetically identical animals and could point to a behavioral component contributing to the regulatory mechanism. Interestingly, and reminiscent of the timing in the liver, increased ribosome association of mRNAs in Drosophila occurs at phases of relative behavioral quiescence, just prior to locomotor activity bouts. It remains to be explored whether this similarity is indicative of mechanistic parallels. Another exciting open question concerns the possibility that the rhythmic biosynthesis of components of the translational apparatus contributes to daily changes in overall translation rate that have been reported and that may involve mTOR signaling and a noncanonical cytoplasmic role for ARNTL.

19Among the other cases of TE rhythmicity, only a few were directly suggestive of an underlying mechanism, as was the case for mRNAs encoding iron metabolic proteins that all contain IREs. IREs are bound by iron regulatory proteins (IRPs) 1 and 2 (encoded by the genes Aco1 and Ireb2, respectively), which sense intracellular iron levels by distinct mechanisms and respond to other metabolic signals as well (for review, see ). IRP1 assembles a 4Fe-4S cluster in response to increased iron availability, which precludes IRE binding and permits translation. Other signals, such as NO, H2O2, and O2, also influence the Fe-S cluster and IRP1 activity. IRP2 is regulated by protein degradation via FBXL5, an E3 ubiquitin ligase stabilized by iron and oxygen. We did not observe high-amplitude rhythms in the expression of IRPs or their known regulators (Supplemental Fig. S11; please note low amplitude rhythms for Ireb2), and it is conceivable that rhythmicity occurs at the level of available, bioactive iron in the hepatocyte (day/night changes in hepatic total iron have been reported), of O2 pressure/consumption, or of reactive oxygen species. Together with the oscillations in mRNA abundance seen for multiple other iron metabolic genes, the rhythmic regulation of IRE-containing transcripts uncovers a previously unappreciated extent of temporal control in this physiologically important pathway.

20It is noteworthy that clock genes showed constant TEs, indicating exclusion from time of day–dependent translational control. The considerable delays between mRNA and protein accumulation that have been reported for several core clock components (e.g., ) must therefore have other, post-translational origins. Nevertheless, our study has unveiled important insights into how translation contributes to core clock regulation. First, the CDS-mapping RPF-seq reads allow estimating relative biosynthesis rates of core clock proteins, which will likely add to a better quantitative understanding of the clock mechanism. Moreover, the footprint profiles from several clock mRNAs showed hallmarks of regulation that, however, may be operative not in a temporal fashion but under other (e.g., environmental, metabolic, cell-type–specific) conditions yet to be defined. In this context, the high number of translated uORFs within the core clock transcripts is particularly striking. uORF translation is generally viewed as inhibitory for protein production from the main ORF and represents an attractive mechanism for how clock protein levels (and consequently clock parameters) could be adjusted post-transcriptionally. It is tempting to speculate that one or several of the identified core clock uORFs are implicated in the short period phenotype observed in Denr-depleted cells. Of note, there is growing evidence for cell-type–specific uORF usage (e.g., ), and it is also largely unexplained how certain core clock parameters can be strikingly tissue specific (e.g., >2 h longer free-running period in kidney vs. lung). It is conceivable that cell-type–specific differences in clock protein concentration and/or stoichiometry are involved and that tissue-specific uORF usage and translation rates contribute. Altogether, our results suggest that the circadian system represents a particularly suitable paradigm for future studies of uORF biology.

21For time series experiments, 12-wk-old male mice (C57BL/6J; Janvier Labs) were entrained for 2 wk to LD 12:12 with free access to food and water and were euthanized at indicated Zeitgeber times (ZT0 corresponding to “lights on”) by decapitation after anesthesia (isoflurane). Livers were removed and processed either directly or flash-frozen in liquid N2. All experimental procedures were approved by the Veterinary Office of the Canton Vaud (authorization VD2376).

22RPF-seq and RNA-seq libraries were generated using Ribo-Zero and ARTseq ribosome profiling kits (Epicentre) and sequenced on an Illumina HiSeq 2500. Detailed protocols, including for lysate preparation, are described in the Supplemental Material.

23Adapter-trimmed, size-filtered sequencing reads (lengths 26–35 nt and 21–60 nt for RPF-seq and RNA-seq, respectively) were mapped sequentially to mouse rRNA, human rRNA, mt-tRNA, mouse tRNA, mouse cDNA (Ensembl release 75), and, finally, mouse genomic sequences (GRCm38.p2). cDNA-mapping reads were counted toward 5′ UTR, CDS, and 3′ UTR per gene basis. CDS counts were normalized by the upper quantile method and transformed into modified RPKM values. TEs were calculated as the ratio of RPF-RPKM to mRNA-RPKM. For detailed information on bioinformatic analysis, see the Supplemental Material.

24Total, nuclear, and mitochondrial protein extracts were prepared from two to three individual mice per timepoint and analyzed by SDS-PAGE and Western blotting according to standard protocols. Figures show one representative time series. Detailed experimental protocols and antibodies are described in the Supplemental Material.

25The generation of lentiviral luciferase reporter plasmids containing wild-type/mutant fragments of the Nr1d1 genomic region is described in the Supplemental Material. For the generation of lentiviral shRNA expression vectors targeting Denr, sequences from the TRC shRNA Library at the Broad Institute were cloned into pLKO.1puro backbone vector (Addgene no. 10878); sequences/clones are listed in Supplemental Material. Scramble shRNA (Addgene no. 1864) served as control.

26Cell culture, lentiviral production/transduction, the recording of circadian bioluminescence rhythms, and the dual luciferase assays followed standard methods. Detailed experimental protocols and additional references can be found in the Supplemental Material.

27RNA was extracted with TriFast peqGOLD (PEQLAB), reverse-transcribed with SuperScript II (Invitrogen), and amplified with SYBR green rox master mix (Roche) and gene-specific primers (Supplemental Material) on a Stratagene Mx3000P apparatus (Agilent). Relative expression levels were determined using the ΔΔCt method.

28GO analysis was carried out on the footprints rhythmic set after Babel analysis using the DAVID bioinformatics resource.

29The sequencing data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE67305.