RESEARCH ARTICLE| NEUROSCIENCE

OPEN ACCESS

Circadian ribosome profiling reveals a role for the Period2
upstream open reading frame in sleep
Arthur Milliusa,b,c , Rikuhiro G. Yamadaa, Hiroshi Fujishimaa, Kazuhiko Maedaband Hiroki R. Uedaa,g,1, Daron M. Standleyc, Kenta Sumiyamad, Dimitri Perrine,f,Downloaded from https://www.pnas.org by RIKEN, RIKAGAKU KENKYUSHO LIBRARY on November 10, 2023 from IP address 134.160.145.100.

Edited by Joseph Takahashi, The University of Texas Southwestern Medical Center, Dallas, TX; received August 30, 2022; accepted August 29, 2023

Many mammalian proteins have circadian cycles of production and degradation, andmany of these rhythms are altered posttranscriptionally. We used ribosome profiling to
examine posttranscriptional control of circadian rhythms by quantifying RNA translation in the liver over a 24-­h period from circadian-­entrained mice transferred to constantdarkness conditions and by comparing ribosome binding levels to protein levels for 16
circadian proteins. We observed large differences in ribosome binding levels compared
to protein levels, and we observed delays between peak ribosome binding and peakprotein abundance. We found extensive binding of ribosomes to upstream open reading
frames (uORFs) in circadian mRNAs, including the core clock gene Period2 (Per2). An
increase in the number of uORFs in the 5′UTR was associated with a decrease in ribosome binding in the main coding sequence and a reduction in expression of synthetic
reporter constructs. Mutation of the Per2 uORF increased luciferase and fluorescencereporter expression in 3T3 cells and increased luciferase expression in PER2:LUC MEF
cells. Mutation of the Per2 uORF in mice increased Per2 mRNA expression, enhanced
ribosome binding on Per2, and reduced total sleep time compared to that in wild-­type
mice. These results suggest that uORFs affect mRNA posttranscriptionally, which can
impact physiological rhythms and sleep.

Significance
Period (Per) is an iconic gene in
the field of circadian rhythms
since its discovery in 1971 by
Seymour Benzer and Ronald
Konopka in fruit flies. The
inhibitory feedback loop of PER
protein drives circadian rhythms.
We show that Per2 is regulatedby an upstream open reading
frame (uORF) in the 5′
untranslated region of the Per2
mRNA. Mutation of the Per2
uORF altered the amplitude of
luciferase reporter expression inwell-­characterized cell culture
models. Per2 uORF mutant mice
had significantly elevated Per2mRNA levels and exhibited sleep
loss, particularly during light-­to-­dark and dark-­to-­light transitions,which suggests a role for uORFs
in modulating molecular andphysiological circadian rhythms.

circadian rhythms | ribosome profiling | uORF | mass spectrometry | RNA

Life is remarkably adapted to the 24-­hour rotational movement of the earth. In mammals,
the molecular time-­keeping mechanism for circadian rhythms relies primarily on a hierarchical network of transcription activators and repressors in cells and tissues (1). In thepast, circadian clocks have been measured using systems approaches to quantify
genome-­wide changes in RNA levels (2), which has resulted in an understanding of thetranscriptional regulatory network; however, less is known about how translation andposttranscriptional regulation influence biological rhythms.
About 10% of genes in the liver have circadian oscillations in the steady-­state levels ofmRNA (3), but rhythmic transcription accounts for only some of these rhythms (4). For
example, the timing of when a circadian mRNA is expressed does not necessarily correspond to that of mRNA translation or peak protein abundance (5). Some proteins have24-­h rhythms in abundance in the absence of rhythmic RNA expression (6, 7), which
may suggest a role for rhythmic translation in regulating the clock (8).
In mouse liver, detection of low-­abundant components of the core circadian circuit
using systems proteomics is difficult (8, 9), unless special care is taken to examine a particular protein on a case-­by-­case basis (10) or by using advanced mass spectrometry
techniques (11, 12). Researchers have used next-­generation sequencing of ribosome-­boundmRNA protected from RNAse degradation to understand how translation regulation
affects protein output (13, 14). Previous studies using ribosome profiling to measure daily
rhythms focused on a cell culture model (15) or mouse tissues in light–dark conditions(16–18) to examine rhythms in diurnal gene expression, which may be influenced by
noncircadian time-­keeping systems. These studies also examined the timing between RNA
abundance and ribosome binding, but it remains unclear how circadian translation relates
to peak protein abundance in terms of protein turnover and timing. For example, thepeak abundance of PER and CRY proteins is delayed relative to the expression of theirmRNA in the liver (19) and suprachiasmatic nuclei (SCN) (20). This difference in timing
may result from a delay in RNA processing before translation, a delay during translation,a delay in protein transport from one cellular location to another, or a delay in protein
turnover (21, 22).
Previously, we developed a mass spectrometry method called MS-­based Quantification
By isotope-­labeled Cell-­free products (MS-­QBiC) to determine the absolute protein levelsof 16 selected circadian proteins in mice liver over a 24-­h period (10). This method takes
advantage of the PURE system (23) for reconstituting cell-­free protein expression of
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Author contributions: A.M., R.G.Y., K.S., D.P., and H.R.U.
designed research; A.M., R.G.Y., H.F., and K.S. performed
research; K.M. and D.M.S. contributed new reagents/
analytic tools; A.M. and D.P. analyzed data; and A.M.
wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
Copyright © 2023 the Author(s). Published by PNAS.
This open access article is distributed under Creative
Commons Attribution License 4.0 (CC BY).
1

To whom correspondence may be addressed. Email:uedah-­tky@umin.ac.jp.
This article contains supporting information online athttps://www.pnas.org/lookup/suppl/doi:10.1073/pnas.​2214636120/-­/DCSupplemental.
Published September 28, 2023.

https://doi.org/10.1073/pnas.2214636120

1 of 9optimal peptide standards for detection and quantification by
selected reaction monitoring-­based targeted proteomics analysis.
We found delays between the peak levels of RNA expression, as
measured by qPCR, and the abundance of the corresponding
protein, suggesting either a delay in posttranscriptional RNA processing or in protein turnover. Here, we investigated the same liver
samples by ribosome profiling in order to understand the timingof ribosome binding compared to peak protein and RNA levels.
We found that upstream open reading frames (uORFs) modulatedtranslation globally, repressed reporter expression in a combinatorial manner, and suppressed expression of Per2. Mutation of thePer2 uORF in mice reduced total sleep duration, particularly
during the early morning and early evening, without disrupting
the circadian period, which suggests that uORF-­mediated repression may impact physiological behaviors.

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Results
Ribosome Profiling of the Liver from Mice in Constant Darkness
Conditions. An experimental workflow was designed to analyze

ribosome-­protected mRNA fragments from liver samples
previously examined by MS-­QBiC (10) (SI Appendix, Fig. S1).
Briefly, mice were entrained to a 12-­h light/12-­h dark (LD) cyclefor 14 d, transferred to constant darkness (DD) for 24 h, and killed
at circadian times (CT0, CT4, CT8, CT12, CT16, CT20, andCT24). Liver samples from two mice were collected and analyzed
following established ribosome profiling protocols (24). We
prepared ribosome profiling libraries and sequenced ~70 million
reads per sample; this resulted in 25 to 45 million reads that could
be mapped to mRNA (25) (SI Appendix, Table S1). Ribosome-­
protected fragments primarily aligned to the coding regions and5′ untranslated regions (5′UTRs) of the mRNA, with few reads
mapping to the 3′ untranslated region (3′UTR) (SI Appendix,
Fig. S2A). Alignment of coding sequence (CDS)-­mapped reads
based on the footprint length revealed reading frame periodicity(SI Appendix, Fig. S2B). Reads were of the expected size, mappedwith a high percentage to mRNA, and were correlated between
samples (SI Appendix, Fig. S2 C–E).
From ~14,000 well-­translated transcripts (defined by a medianof at least two reads per five-­codon mRNA window), we identified
rhythms in ribosome-­protected read fragments using the JTK_CYCLE algorithm (26), yielding 2,952 rhythmic transcripts withan adjusted P < 0.05, including well-­known circadian transcripts
such as Bmal1, Per1, Per2, Clock, and Cry1 (Dataset S1 and
SI Appendix, Table S2 and Fig. S6A).
Relationship between Ribosome Profiling Reads and Protein
Levels. We compared the timing of protein production, as

measured by ribosome profiling reads, to the absolute numberof protein molecules per cell for 16 previously reported corecircadian proteins (10). There was broad agreement in the timingof ribosome binding compared to that of protein abundance(Fig. 1A). However, for several circadian proteins, such as BMAL1
and CLOCK, there was a delay of approximately 6 h between peakribosome binding and peak protein abundance, clearly outside the4-­h range of our sampling intervals (Fig. 1B), which suggested
that there is a posttranslational delay in protein turnover for thesemolecules rather than a posttranscriptional delay in ribosome
binding. We compared the average number of ribosome profilingreads to the number of protein molecules over a 24-­h period but
found that the two values were poorly correlated (Fig. 1C). We
measured this protein production efficiency (as defined by theratio of our mass-­spectrometry protein levels to ribosome profilingreads) at each time point. For some proteins, such as BHLHE40, a
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large amount of ribosome binding resulted in a moderate amountof protein (Fig. 1D), whereas for other proteins, such as PER2, amuch smaller amount of ribosome binding resulted in the sameamount of protein as BHLHE40 (Fig. 1E), which suggests that
BHLHE40 protein is more unstable than PER2 or that someother factor is limiting the amount of BHLHE40 produced. Thus,
ribosome profiling reads can provide an approximate estimate forwhen a protein is produced, but protein abundance reflects bothprotein production and posttranslational mechanisms to control
overall protein levels (27).
When we compared mRNA and protein abundance phases inour dataset in DD to the reported phases in previously publishedstudies of mouse liver in LD (12, 15, 16, 18), we observed a correlation with a 1 to 2 h delay in DD (SI Appendix, Fig. S10 A and
C). The transcripts with the largest differences in delay in themRNA-­to-­peak-­protein abundance between LD and DD conditions included Nfil3, Per2, Per3, and Bmal1 (SI Appendix,
Fig. S10B). The differences resulted from a slightly earlier peak in
RNA abundance and ribosome binding in LD rather than a delayin the protein abundance peak; there was little difference between
LD and DD in the delay between RNA abundance and ribosome
binding (SI Appendix, Fig. S10D). Although these small differences
may reflect a true biological effect from light-­dependent changes
in transcription, they could also simply result from differences in
the algorithms used for phase determination, differences in experimental conditions, or both. Besides these 16 core circadian transcripts, most other transcripts with rhythmic ribosome binding inour dataset had a phase of around CT0 (SI Appendix, Fig. S6).
uORFs Suppress Translation. We also noticed a relationshipbetween ribosome occupancy and the presence of uORFs,
similar to previous reports (15, 18). Roughly half of all mousetranscripts contained at least one uORF (SI Appendix, Fig. S4B),
and we found rhythmic ribosome binding in 602 uORFs with an
adjusted P < 0.05 using JTK_cycle (Dataset S2). In particular, forcircadian transcripts, such as Cry1 and Bmal1, there appeared to
be increased ribosome binding in uORF regions, as measured byincreased ribosome profiling reads (Fig. 2A), although ribosome
binding in the 5′UTR was not always associated with uORFs
in circadian transcripts (SI Appendix, Fig. S4A). mRNAs withincreased numbers of uORFs had lower levels of ribosome
occupancy in the downstream CDS (Fig. 2B), whereas the lengthof the uORF and the distance of the uORF to the start codon did
not have a significant impact on ribosome binding in the CDS(SI Appendix, Fig. S4 C and D). To investigate whether uORFs
were sufficient to suppress translation in a combinatorial manner,
we created a luciferase reporter vector with multiple synthetic
uORFs. Predictably, increasing the number of uORFs reduced
luminescence from the reporter (Fig. 2C). Using this synthetic
reporter, we varied the position of a single uORF relative to the startcodon or the uORF length and found a higher degree of uORF-­
mediated repression the closer the uORF was to the start codon;
however, uORF length had no effect on repression (SI Appendix,
Fig. S4 E and F). Both the position of the uORF and the numberof uORFs altered the amplitude and mesor (mean luminescence)
without altering the period using two different promoters in cell-­based circadian luminescence assays (SI Appendix, Fig. S15). Other
factors, such as the Kozak consensus of the uORF, overlap with the
CDS, or uORF conservation may also impact repression strength(28, 29).
The Per2 uORF Suppresses Reporter Expression without Altering
Period. To further explore the impact of uORFs on circadianrhythms, we focused on the uORF in the circadian transcript

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Protein molecules per cell (thousands)

A

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Ribosome Profiling (RPKM)

Fig. 1. Measuring translation and protein from the same liver samples for 16 circadian mRNAs. (A) Time course of the quantified proteins and the correspondingtranslation level. Protein concentration (gray) is expressed as copies per cell assuming that the total amount of protein per cell is 0.5 ng for the average forall quantified peptides (10). Ribosome binding (red) is expressed as reads per kilobase of transcript per million mapped reads (RPKM). Data are mean ± SEM.
(B) Phase delay between ribosome binding and protein abundance. Copies per cell were averaged from all peptides for each protein. JTK analysis was used toestimate the phases for these averaged protein levels and for ribosome binding RPKM levels. (C) Linear regression analysis comparing ribosome binding toprotein amount averaged over 24 h showed that there was no correlation between ribosome binding and protein abundance (D and E) Linear regression analysis
comparing ribosome binding to protein amount at each sampled time point showed that some proteins like BHLHE40 (D) had high protein turnover or reduced
protein biosynthesis, whereas other proteins like PER2 (E) had low protein turnover or increased protein biosynthesis.

Per2. This uORF is evolutionarily conserved and consists of only
a start and stop codon (Fig. 3A and SI Appendix, Fig. S13), which
eliminates any potential effect of a translated uORF peptide on theregulation of Per2. Ribosomes bound the Per2 uORF rhythmicallyand slightly before peak ribosome binding on the Per2 transcript(Fig. 3B). Mutation of the uORF in Per2 increased the amplitude
of expression without affecting the phase or period in 3T3 cells
transfected with a luminescence reporter (Fig. 3 C and D). This
increase in amplitude was not affected by the amount of transfected
plasmid, inclusion of the full-­length Per2 5′UTR, or addition ofPER2 protein (SI Appendix, Fig. S5). CRISPR/Cas9-­mediatedmutation of the Per2 uORF in PER::LUC MEFs increased overall
luciferase expression levels compared to that of wild-­type MEFs;
however, we were unable to generate circadian rhythms in thesecells to determine the impact of the Per2 uORF on other circadianparameters such as period and phase (SI Appendix, Fig. S12).
To understand uORF-­mediated repression in individual cells,
we created a GFP fluorescent reporter plasmid driven by the Per2
promoter with or without a mutation in the Per2 uORF. We
transfected 3T3 cells with these plasmids and an mCherry normalization plasmid and analyzed the cells by FACS (SI Appendix,
Fig. S11). Mutation of the uORF increased GFP brightness without affecting mCherry brightness in individual cells (Fig. 3E), andincreased the GFP-­to-­mCherry ratio of the population (Fig. 3F)
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regardless of whether the cells were first gated on mCherry expression (Fig. 3 E and F) or GFP expression (SI Appendix, Fig. S11 C
and D). Mutation of the uORF also increased the total numberof GFP-­expressing cells without affecting the total number ofmCherry-­expressing cells (Fig. 3G). Thus, the Per2 uORF canrepress reporter expression within individual cells to control circadian amplitude.
Mice with a Mutation in the Per2 uORF have Reduced Sleep.

We used CRISPR/Cas9 to generate a knock-­in mouse harboringa mutation in the uORF of Per2, which removed both the startand stop codon of the uORF without disrupting a nearbyE’-­box (SI Appendix, Fig. S7A). Wild-­type and mutant mice were
phenotyped over 13 d in LD and then for 12 d in DD using the
Snappy Sleep Stager (30, 31), which is a respiration-­based method
to characterize sleep/wake parameters and circadian rhythms.
Both male and female Per2 uORF mutant mice had significantly
reduced sleep per day (mean ± SEM: 717 ± 19 min and 642 ±7 min, respectively) compared to their wild-­type littermates (774
± 11 min and 678 ± 4 min, respectively, P < 0.001 by two-­way
ANOVA) and a corresponding increase in wake duration per day(Fig. 4 A and B and SI Appendix, Fig. S8A) in LD conditions. There
was a decrease in sleep episode duration and a significant increasein the transition probability from sleep to awake termed Psw in Per2
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gacaaaTTGTAAgacaaaTTGTAAgacaaaTTGTAAgacaaaTTGTAA
gacaaaATGTAAgacaaaTTGTAAgacaaaTTGTAAgacaaaTTGTAA
gacaaaATGTAAgacaaaATGTAAgacaaaTTGTAAgacaaaTTGTAA
gacaaaATGTAAgacaaaATGTAAgacaaaATGTAAgacaaaTTGTAA
gacaaaATGTAAgacaaaATGTAAgacaaaATGTAAgacaaaATGTAAPer2 short promoter

0-4 uORFs linker

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Fig. 2. uORFs suppress protein expression. (A) The 5′UTRs of Cry1ClockPer2, and Bmal1 transcripts contain at least one uORF (shaded regions below arrows).
The distribution of raw counts of ribosome profiling reads summed across all time points (black bars) for the 5′UTR of each transcript is shown. (B) Global
distribution of uORFs compared to ribosome profiling translation level (RPKM) shows that mRNAs with more uORFs are translated less compared to mRNAs withfewer uORFs. (C) Introduction of a variable number of synthetic uORFs represses relative luminescence from a Per2 short promoter in a dose-­dependent manner.

uORF mutant mice (SI Appendix, Fig. S8B); however, there were
no differences in other sleep parameters such as amplitude, Pws (thetransition probability from awake to sleep), or wake episode duration(SI Appendix, Fig. S8C). Per2 uORF mutant mice exhibited reduced
sleep duration particularly during the light-­to-­dark and dark-­to-­light transitions in the early morning and early evening (Fig. 4 C
and D). There were also differences in sleep duration later in themorning (SI Appendix, Fig. S8D) but not at other times during theday (SI Appendix, Fig. S8E). Next, we observed mice in constantdarkness over 12 d and found no differences in activity rhythms
or period (Fig. 4E); furthermore, there were less pronounced sleep
differences in DD compared to LD (SI Appendix, Fig. S9). Cosinor
analysis of the activity rhythms in LD and DD conditions revealed
no differences in period and amplitude between wild-­type and Per2
uORF mutant mice (SI Appendix, Fig. S9).
To understand the molecular mechanisms behind these phenotypic differences, we performed ribosome profiling and total
RNA sequencing of the liver from wild-­type and Per2 uORF
mutant male mice killed at ZT02-­04 (32) (SI Appendix, Figs. S18
and S19 and Datasets S4 and S5). Because Per2 levels are near
their lowest at ZT02-­04, we hypothesized that this time point
would show the largest difference in derepression of Per2 by mutation of the Per2 uORF. There was an increase in ribosome binding
in Per2 uORF mutant mice (SI Appendix, Fig. S19C), particularlyin the 5′UTR and around the start codon (SI Appendix, Fig. S19
D and E). Per2 RNA levels were also higher in the mutant miceby total RNA sequencing (SI Appendix, Fig. S19 C and H and
Dataset S5) and confirmed by qPCR analysis (SI Appendix,
Fig. S19B), although no differences were observed in Per2 translation efficiency (TE), which is defined as the ratio of ribosome
profiling reads to bulk RNA-­seq reads (SI Appendix, Fig. S19G
and Dataset S4). Protein levels were similar in Per2 uORF mutant
mice compared to that in wild-­type mice (SI Appendix, Fig. S19
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A and I). Taken together, these results suggest that Per2 mRNA
expression and ribosome binding is suppressed by the Per2 uORF,
which has a moderate impact on sleep duration.
Discussion
Global proteomics studies of mice livers revealed large numbers ofdiurnally rhythmic proteins. It has been estimated that 20 to 50%of these proteins are dependent on posttranscriptional and posttranslational mechanisms (8, 9, 11, 12, 33). Previously, we used
targeted proteomics to detect the rhythmicity and concentration of
a select number of low-­abundance core circadian proteins in constant darkness conditions (10). Here, we used ribosome profilingand quantitative mass spectrometry to understand the relationshipbetween those circadian protein rhythms and mRNA translation.
We found a correlation between the timing of protein production, as measured by ribosome profiling, and peak protein abundance for most transcripts (Fig. 1). However, BMAL1 and CLOCK
proteins had a delay between peak ribosome binding and protein
levels (Fig. 1) caused by delayed protein turnover via posttranslational modification pathways (34), rather than a delay in ribosome
binding (SI Appendix, Fig. S3). The peak protein abundance of PER
and CRY is also slightly delayed relative to their peak mRNA abundance (10, 19, 20), and we found that this was also due to protein
turnover rather than to a delay in ribosome binding (SI Appendix,
Fig. S3), similar to previous ribosome profiling studies (15, 16, 18).
There was a larger delay between the peak abundance of Cry2 ribosome binding and CRY2 protein than that of Cry1, which may
simply be a result of the lower amplitude of CRY2 rhythms.
We lacked the RNA-­seq data needed to compute TE in our circadian samples, which limited our ability to determine how uORF
features in 5′UTRs, such as number, length, and distance to CDS,
alter translation efficiency. However, using the closest publicly
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Fig. 3. The Per2 uORF is evolutionarily conserved and suppresses PER2 expression. (A) Genomic position of the Per2 short promoter, which contains an E’box
and uORF. Evolutionary conservation scores according to UCSC genome browser among 22 mammalian species in the Per2 5′UTR (black bars) with the uORF
highlighted (red bars). (B) Ribosome binding (RPKM) on the Per2 uORF (red) compared to the Per2 transcript (5′UTR, CDS, and 3′UTR, black). (C) Representative
bioluminescent traces showing that mutation of the Per2 uORF (ATGTAA to TTGTGA) increases the expression level of a pGL3-­P(Per2)-­dLuc luminescent reporter.
Reporter containing uORF (black), reporter with a mutant uORF (red). The shaded region is SD. (D) Cosinor analysis showing the normalized amplitude (Left),
period (Middle), and phase (Right) for cells transfected with the Per2 uORF (black) or a mutant Per2 uORF (red). Data from each group comprise at least 22 tracesfrom eight different experiments (see SI Appendix, Fig. S5, for the plots of individual traces for each experiment). (E) Representative scatterplot of GFP versus
mCherry expression for 3T3 cells transfected with a GFP-­fluorescence reporter with a Per2 uORF (+uORF, black) or a mutant Per2 uORF (−uORF, red) and amCherry-­normalization plasmid. The analyzed cells were gated on mCherry+ expression. (F) Histogram of the GFP/mCherry ratio for cells in E. (G) The percentage
of GFP+ cells (Left) was significantly higher in the −uORF population compared to the +uORF population, whereas the percentage of mCherry+ cells (Right) was
unchanged (n = 3 independent experiments). See SI Appendix, Fig. S11, for FACS gating strategy and analysis of cells from the GFP+ gate.

available RNA-­seq data (4) to our ribosome profiling data as a proxy
for transcript abundance (Dataset S3), we observed a decrease in TE
in transcripts with one or more uORFs (SI Appendix, Fig. S16), but
no relationship between TE and uORF length or uORF distance to
CDS (SI Appendix, Fig. S17).
Most transcripts with rhythmic translation in our dataset had aphase around CT0, corresponding to what would be thedark-­to-­light transition (SI Appendix, Fig. S6). Less than 10% of
the circadian translatome had significant rhythms in mRNA abundance according to JTK cycle analysis (P < 0.05), and these transcripts were enriched for circadian and metabolic processes byPanther gene ontology (GO) analysis (SI Appendix, Fig. S6 B and
C). Transcripts with a nonsignificant mRNA rhythmicity by JTK
cycle analysis (P > 0.05) also were biased toward CT0, but there
was no clear GO biological process underlying these rhythms
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(SI Appendix, Fig. S6 D and E). The set of transcripts with phases
between CT22 and CT02 (SI Appendix, Fig. S6 F and G) included
Npas2, Bmal1, Cry1, Nfil3, and Clock. These transcripts are known
to be regulated by the nuclear receptor NR1D1, also known as
Rev-­erbα, which transcriptionally represses many genes involved inmetabolism in a tissue-­and circadian-­dependent manner (35). In
the liver, NR1D1 predominantly binds genomic locations at
ZT08-­ZT10 with little-­to-­no binding at ZT22 (36–38). Thus,
NR1D1 derepression may be driving some of transcriptional, andsubsequent translational, peak observed in our data between CT22
and CT02. In addition, cyclic changes in translation machineryand polyadenylation (39, 40), translation activity of BMAL1 mediated by the mTORC1/S6K1 (41), or mTORC1 regulation by PER2(42) may also drive rhythmic translation of nonrhythmic mRNAtranscripts.
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B

Mutant (-uORF)

30

Female

20

4

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*

30

10

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800

20

700

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= 23.8 0.8
= 23.7
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= 23.5
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Sleep time ZT0–4 h (min)Sleep time ZT12–16 h (min)Downloaded from https://www.pnas.org by RIKEN, RIKAGAKU KENKYUSHO LIBRARY on November 10, 2023 from IP address 134.160.145.100.

D

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Sleep duration per hour (min)MaleFemaleWild type (+uORF)MaFe l emaleAFig. 4. Disruption of the Per2 uORF reduces sleep in mice. (A) Sleep duration per hour over 13 d in 12-­h light/12-­h dark (LD) conditions for Per2 uORF mutant
and wild-­type male and female mice. Each row indicates data from one mouse. (B) Mean sleep (Top) and wake (Bottom) duration over 24 h, averaged over 13 d.
Red, Per2 uORF mutant mice. Black, wild type. (C) Sleep duration per hour over 24 h in LD, averaged over 13 d for Per2 uORF mutant (red) and wild-­type (black)male (Top) and female (Bottom) mice. Lines indicate mean sleep duration at each time of day for each strain. Shaded area, SD at each time point. (D) Mean sleep
duration per hour averaged over 13 d during the early morning (Top) and early evening (Bottom). Per2 uORF mutant (red) and wild-­type (black) dots are individual
mice; lines indicate mean. (E) Representative double-­plotted actograms of sleep duration per 6 min bins of wild-­type and Per2 uORF mutant mice in constantdarkness (DD) over 12 d (Left) and corresponding periodograms (Right).

We observed widespread binding of ribosomes to uORFs in the
5′UTRs of many core circadian transcripts and found an inverse
relationship between uORF number and luciferase expression intransiently transfected reporter cells and CRISPR/Cas9-­generated
MEF cells (Fig. 2 and SI Appendix, Figs. S4, S12, and S15).
Approximately 50% of mouse and human mRNAs containuORFs, which are associated with widespread translational repression (29). Thus, uORF-­mediated suppression of translation may
constitutively reduce the abundance of circadian proteins or provide a posttranscriptional foothold to adjust protein abundanceby altering the activities of ribosome reinitiation factors (43–45).
Moreover, mutagenesis of uORFs has been shown to alter rhythmicity in other circadian systems (46, 47). Not all ribosome binding in 5′UTRs was associated with uORFs, and we observed
extensive ribosome binding in areas without an apparent uORF
for Cry1, Nr1d1, Clock, Dbp, and Per3 (Fig. 2 and SI Appendix,
Fig. S4). For the Nr1d1 5′UTR, we observed close overlap in the
nucleotides necessary for internal ribosome entry site (IRES)-­
mediated translation (48) and ribosome binding (SI Appendix,
Fig. S14A) and confirmed IRES-­mediated translation from the
Nr1d1 5′UTR but not the Cry1 5′UTR (SI Appendix, Fig. S14B).
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Near-­cognate uORF translation (from a non-­AUG start codon)
may also drive ribosome binding (14). In silico mapping ofnear-­cognate start codons with ribosome binding data revealed
numerous potential near-­cognate uORFs (SI Appendix, Fig. S14C)
but experimental validation is still needed.
The uORF in the Per2 5′UTR is an attractive target to understand the role of uORFs in circadian biology, not only because
cis-­elements within the Per2 promoter are well understood (49–52)
but also because the Per2 uORF is too short to encode a peptide.
PER2 production is posttranscriptionally controlled by miRNAs(53, 54), antisense transcription (55), and hnRNP1-­mediatedmRNA degradation (56), which suggests that Per2 posttranscriptional control is important for producing the optimal amount of
PER2 protein. Mutation of the Per2 uORF increased the amplitudeof a reporter plasmid without affecting the phase or period (Fig. 3),
similar to effects observed by mutating the Per2 antisense transcript(57).
Both the abundance and timing of Per2 expression are criticalfor maintaining circadian rhythmicity in mice because constitutive expression of PER2, unlike CRY1, disrupts behavioral
rhythms (58). Several posttranscriptional mechanisms such aspnas.org

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Per2 antisense transcription (55) and miRNAs (53, 54, 59) in the
Per2 genomic locus alter the timing and abundance of PER2
protein expression. For example, replacing the Per2 3′UTR withan SV40 late poly(A) signal greatly amplifies bioluminescencerhythms in PER2:LUC mice and increases free-­running periods(54). Mutation of the Per2 uORF increased amplitude in reporter
cells (Fig. 3) but did not increase the free-­running period in mice
(Fig. 4). Per2 uORF mutant mice did have reduced total sleep in
LD conditions (Fig. 4), as observed in Per1/Per2 double mutant
mice under similar sleep phenotyping conditions (30), but nosignificant change in sleep in DD conditions (SI Appendix,
Fig. S9). Although our Per2 mutation abolished the canonical
ATGTGA uORF in the Per2 5′UTR, it also introduced anear-­cognate uORF (CTGTAG) two nucleotides upstream of theoriginal uORF. We think that this near-­cognate uORF was nonfunctional because we observed increased Per2 ribosome binding
and mRNA levels in Per2 uORF mutant mice compared to thatin wild-­type mice (SI Appendix, Fig. S19) and increased luciferase
expression in mutant PER2:LUC MEF cells (SI Appendix,
Fig. S12). One possible explanation for the rise in Per2 mRNAlevels is uORF-­triggered nonsense-­mediated decay (NMD) (60).
In this scenario, mutation of the Per2 uORF derepresses NMD
of Per2, which results in an increase in Per2 mRNA. However,
Per2 mRNA expression does not increase upon knockdown of
the NMD component Smg6 (61), which suggests that other proteins in NMD independent of SMG6 may be involved (62).
Another group deleted the start codon of the Per2 uORF but
found no effect on Per2 mRNA levels and did not perform ribosome profiling (63), so it is unclear whether ribosome binding in
Per2 is elevated in their Per2 uORF mutant mice as it is in ourmutant mice. In both studies, PER2 protein levels in the mutant
mice were similar to that in wild-­type mice, indicating that circadian proteostasis pathways compensate for the increase in PER2
protein production. In LD conditions, sleep in our Per2 uORF
mutant mice was particularly reduced during light-­to-­dark anddark-­to-­light transitions (Fig. 4), which may result from a lower
sleep episode duration and an increase transition probability fromsleep to wake (SI Appendix, Fig. S8B). Regulation of PER2 stability affects sleep (64, 65), and sleep deprivation reciprocallyaffects Per2 expression (66–69) providing a rationale for how
increased Per2 expression could disrupt sleep in our mice.
Mutation of the E’box cis-­element, which is only a few base pairs
upstream of the Per2 uORF, abolishes molecular oscillations inmutant cells without disrupting the free-­running period in mice(49). These mice re-­entrain quicker under an artificial jetlag experiment than wild-­type mice, and it will be interesting to observe
how Per2 uORF mutant mice behave under similar conditions.
Further studies, particularly at a neurological level, are also needed
to understand the precise mechanism by which sleep is reducedin these animals.
Materials and Methods
Animals. All animal experiments were approved by the Institutional Animal Careand Use Committee of the RIKEN Kobe branch. Eight-­to-­ten-­week-­old wild-­type
male mice (C57BL/6N, Japan SLC) were entrained under 12-­h light (400 lx) 12-­hdark (LD) for 2 wk. Twenty-­four hours after transferring to constant darkness (DD),wild-­type mice were killed every 4 h over one day (CT0, CT4, CT8, CT12, CT16,
CT20, and CT24) for ribosome profiling analysis as in ref. 10. Livers were excised,snap-­frozen in liquid nitrogen, and stored at −80 °C until use. For details regarding Per2 uORF mutant mouse construction, see SI Appendix.
Plasmids. For details regarding plasmid construction, see SI Appendix.
Ribosome profiling. Ribosome profiling was performed essentially as describedin ref. 24. Frozen liver samples (~50 mg) were pulverized and then homogenizedin 400 µL polysome lysis buffer (150 mM NaCl, 20 mM Tris–HCl pH 7.4, 5 mM
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MgCl2, 5 mM DTT, 100 µg/mL cycloheximide, 1% Triton X-­100, and 25 U/mL Turbo
DNAse I). Lysates were incubated on ice for 5 to 10 min, triturated through a 26-­G
needle 10 times, and clarified by centrifugation (20,000 × g for 10 min at 4 °C).
About 300 µL of the supernatant was transferred to a new tube. Unbound RNA
was digested by the addition of 7.5 µL RNAse I (ThermoFisher) for 45 min andthen stopped by 10 µL SUPERase In RNAse Inhibitor (ThermoFisher). The digestion was transferred to 13 mm × 51 mm polycarbonate ultracentrifuge tubes,
layered on top of 0.9 mL sucrose cushion (150 mM NaCl, 20 mM Tris–HCl pH 7.4,
5 mM MgCl2, 5 mM DTT, 100 µg/mL cycloheximide, 1 M sucrose, and 10 U/mL
SUPERase In), and centrifuged in a TLA100.3 rotor at 70,000 rpm at 4 °C for 4 h.
Ribosome pellets were resuspended in 0.7 mL Qiazol, and mRNA was recovered
using the miRNeasy RNA extraction kit (Qiagen) according to the manufacturer’s
instructions. For library construction and sequencing, see SI Appendix.
Bioinformatic analysis of ribosome profiling. For details regarding sequence
processing and alignment, see SI Appendix. For each transcript, nonoverlapping
five-­codon windows were tiled across the coding region, and the transcript wasconsidered well-­translated if it had a median value of at least two reads per window (excluding the excluding the first fifteen and the last five codons). Using all
well-­translated transcripts, we created file where each transcript is represented
across all samples by its RPKM value. We used this file in JTK_CYCLE (26) to
identify all rhythmic transcripts. We used a threshold on the adjusted P < 0.05
to assess significance.
To detect uORFs, we processed the 5′UTR of all transcripts, extracted theirsequence, and identified all pairs of a start codon (AUG) and a stop codon (UGA,
UAA, or UAG) in phase with each other. Using the same read assignment method
as in SI Appendix, we also allocated reads aligned to the UTR to their corresponding uORF (where appropriate).
Cells. NIH3T3 and MEFs from PER2::LUCIFERASE (PER2::LUC) knock-­in reporter
mice (70) were cultured in DMEM (ThermoFisher), 10% FBS (JRH Biosciences),and 1% penicillin/streptomycin (ThermoFisher) at 37 °C in 5% CO2. For additional
details, see SI Appendix.
Real-­time circadian luciferase assay. Real-­time circadian luciferase assays
were performed as previously described (71). Briefly, the day before transfection,
NIH3T3 cells or PER2:LUC MEFs were plated onto 35-­mm dishes at a density 4 ×
105 per well. The following day, NIH3T3 cells were cotransfected using FuGene6(Roche) with 1 µg of the indicated luciferase reporter plasmid according to the
manufacturer’s instructions (n = 3) and cultured at 37 °C. After 72 h, the media
in the well were replaced with 2 mL of DMEM containing 10% FBS supplemented
with 10 mM HEPES (pH 7.2, ThermoFisher), 0.1 mM luciferin (Promega), antibiotics, and 10 µM forskolin (Fermentek, NIH3T3) or 100 µM dexamethasone(ThermoFisher, PER2::LUC MEFs). Luminescence was measured by a photomultiplier tube (LM2400R, Hamamatsu Photonics) for 1 min at 12 min intervals ina darkroom at 30 °C.
Dual-­luciferase assay. NIH3T3 cells were plated on 6-­well plates at a densityof 2 × 105 per well. The following day cells were cotransfected with 0.95 µg of aFirefly luciferase reporter plasmid and 50 ng phRL-­SV40 plasmid (Renilla luciferase, Promega) as an internal control for transfection efficiency using FuGene6(Roche). Cells were harvested and assayed by the Dual-­Luciferase Reporter
Assay System (Promega) according to the manufacturer’s instructions 48 h after
transfection.
Preparation of mouse nuclear lysate and immunoblot analysis. Mice were
killed by cervical dislocation, and livers were dissected at CT2-­4, snap-­frozen inliquid nitrogen, and stored at −80 °C. Liver extracts were prepared according toref. 72 with minor modifications. See SI Appendix for further details.
FACS. NIH3T3 cells were plated onto plastic 35-­mm dishes or 35-­mm imaging
dishes (Ibidi) at a density 4 × 105 per well. The following day, cells were cotransfected using FuGene6 (Roche) with 0.5 µg of the indicated fluorescence reporter
plasmids (1 µg total) according to the manufacturer’s instructions (n = 3) andcultured at 37 °C. After 72 h, cells were trypsinized, sorted using FACSAria I (BD
Biosciences), and analyzed by FlowJo version 10.8.1.
Sleep phenotyping. Sleep phenotyping was conducted in 12-­week-­old (LD) and14-­week-­old (DD) mice in a Snappy Sleep Stager (SSS) using Per2 uORF mutant
mice and wild-­type littermates as a control (30). SSS is a noninvasive, respiration-­based sleeping staging system in which mice are placed in a chamber connected
to a respiration sensor that detects pressure differences between the outside andinside of the chamber. Detailed methods have been described previously (30).
For details regarding sleep and wake parameters, see SI Appendix.
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Statistical analyses. Statistical analyses were performed in R version 3.4.3,
Prism 7.0, and custom Jupyter notebooks version 6.3. Two-­way ANOVA and aStudent’s t test were used to test differences in sleep parameters between wild-­
type and Per2 uORF mutant mice. For data analyzed by the Student’s t test, thedata were first confirmed to have a Gaussian distribution by the Shapiro–Wilk
normality test, and equal variance within this distribution was confirmed by an F
test. Lomb–Scargle periodograms were implemented using SciPy version 1.7, andcosinor analysis was implemented using CosinorPy (73). Statistical significancewas defined as *P < 0.05, **P < 0.01, ***P < 0.001, and n.s. for not significant.
Data, Materials, and Software Availability. NGS data have been deposited in

GEO (GSE201732 (25), GSE231820 (32)). Previously published data were used
for this work [Data from mass spectrometry (Fig. 1A) and qPCR (SI Appendix,
Fig. S3A) was previously published by our lab in Narumi et al., 2016 in PNAS.].
We thank Ryohei Narumi for the circadian liver
samples and for the use of the mass spectrometry and qPCR data and
Dr. Shigehiro Kuraku and the Genome Information Research Center at Osaka
University for sequencing of the ribosome profiling libraries. We thankDr. Shihoko Kojima and Dr. Seung-­Hee Yoo for PER2::LUC MEFs. We thank all lab
members at the RIKEN Center for Biodynamics Research, in particular, Natsumi
Hori and Yumika Sugihara for the production of mutant mice, and Masako Kunimi
and Ruriko Inoue for breeding and helping with the phenotype measurements ofthe mutant mice. We thank all members of the Systems Immunology Laboratory
and the Laboratory for Host Defense at the Immunology Frontier Research Center,in particular, Kitiya Piboonprai for help with genotyping and Shizuo Akira for hiskind support. This work was also supported by grants from Exploratory Research

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https://doi.org/10.1073/pnas.2214636120

for Advanced Technology grant (JPMJER2001, H.R.U.) from the Japan Scienceand Technology Agency, KAKENHI Grant-­in-­Aid from the Japan Society for the
Promotion of Science (Scientific Research S 18H05270 to H.R.U., Scientific Research
C JP21K06385 to R.G.Y.), Human Frontier Science Program Research Grant Program(HFSP RGP0019/2018, H.R.U.), Ministry of Education, Culture, Sports, Science andTechnology Quantum Leap Flagship Program (JPMXS0120330644), Japan Agency
for Medical Research and Development (JP20am0401011), and the intramural
Grant-­in-­Aid from the RIKEN Center for Biosystems Dynamics Research to H.R.U.
This research was supported by the Research Support Project for Life Science and Drug
Discovery (Basis for Supporting Innovative Drug Discovery and Life Science Research)from the Japan Agency for Medical Research and Development under Grant Number
JP22ama121025. This work was supported by a Human Frontiers Science Program
postdoctoral fellowship to A.M. and a Japan Society for the Promotion of Science
Grant-­in-­Aid for Early Career Scientists (18K14755 to A.M.). Two children were born
to A.M. during the course of this project, and he thanks them and Mari Nishino for
their care, love, and support.

Author affiliations: aLaboratory for Synthetic Biology, RIKEN Quantitative Biology Center,
Suita, Osaka 565-­0871, Japan; bLaboratory for Host Defense, Immunology Frontier
Research Center, Suita, Osaka 565-­0871, Japan; cLaboratory for Systems Immunology,
Immunology Frontier Research Center, Suita, Osaka 565-­0871, Japan; dLaboratory of
Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya
University, Nagoya 464-­8601, Japan; eSchool of Computer Science, Queensland Universityof Technology, Brisbane QLD 4000, Australia; fCentre for Data Science, Queensland
University of Technology, Brisbane QLD 4000, Australia; and gDepartment of Systems
Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-­0033,
Japan

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