Translational Profiling of Clock Cells Reveals Circadianly
Synchronized Protein Synthesis
Yanmei Huang*, Joshua A. Ainsley, Leon G. Reijmers, F. Rob Jackson*
Department of Neuroscience, Sackler School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, United States of America
Genome-wide studies of circadian transcription or mRNA translation have been hindered by the presence of heterogeneous
cell populations in complex tissues such as the nervous system. We describe here the use of a Drosophila cell-specific
translational profiling approach to document the rhythmic ‘‘translatome’’ of neural clock cells for the first time in any
organism. Unexpectedly, translation of most clock-regulated transcripts—as assayed by mRNA ribosome association—
occurs at one of two predominant circadian phases, midday or mid-night, times of behavioral quiescence; mRNAs encoding
similar cellular functions are translated at the same time of day. Our analysis also indicates that fundamental cellular
processes—metabolism, energy production, redox state (e.g., the thioredoxin system), cell growth, signaling and others—
are rhythmically modulated within clock cells via synchronized protein synthesis. Our approach is validated by the
identification of mRNAs known to exhibit circadian changes in abundance and the discovery of hundreds of novel mRNAs
that show translational rhythms. This includes Tdc2, encoding a neurotransmitter synthetic enzyme, which we demonstrate
is required within clock neurons for normal circadian locomotor activity.
Citation: Huang Y, Ainsley JA, Reijmers LG, Jackson FR (2013) Translational Profiling of Clock Cells Reveals Circadianly Synchronized Protein Synthesis. PLoS
Biol 11(11): e1001703. doi:10.1371/journal.pbio.1001703
Academic Editor: Ueli Schibler, University of Geneva, Switzerland
Received May 7, 2013; Accepted September 24, 2013; Published November 5, 2013
Copyright: ? 2013 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by NIH R01 HL59873 (FRJ), NIH R01 NS065900 (FRJ), a center grant to Tufts School of Medicine (P30 NS047243; PI, FRJ), a
Pilot Award from the CNR (FRJ), a Young Investigator Award (NARSAD 17339) from the Brain and Behavior Research Foundation (YH), and a NIH Director’s New
Innovator Award (DP2 OD006446) to LGR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: EGFP, enhanced green fluorescent protein; eIF4E, eukaryotic translation initiation factor 4E; GPCR, G protein-coupled receptor; L10a, large
ribosomal protein 10a; L11, large ribosomal protein 11; LNd, dorsal lateral clock neurons; LNv, ventral lateral clock neurons; NTO, nontranscriptional oscillator; PDF,
pigment dispersing factor; Per, Period; Tdc2, tyrosine decarboxylase 2; Tim, Timeless; TTFL, transcriptional/translational feedback loop; TRAP, Translating
Ribosome Affinity Purification; TRX, thioredoxin.
* E-mail: firstname.lastname@example.org (Y.H.); email@example.com (F.R.J.)
Genetic studies carried out in several model systems have
provided seminal knowledge about the biochemistry of the
circadian molecular oscillator and the neural circuitry regulat-
ing circadian behavior. The best characterized circadian
oscillators consist of transcriptional/translational feedback loops
(TTFLs) , although nontranscriptional oscillators (NTOs)
exist in organisms ranging from unicellulars to Drosophila and
humans [2–4]. In Drosophila and mammals, a well-characterized
TTFL oscillator consisting of several canonical clock genes
regulates circadian behavioral rhythms (reviewed in ).
Similarly, transcription of many (perhaps most) genes is
orchestrated by the circadian clock, based on gene profiling
studies carried out in Drosophila, mammals and plants. Only a
few studies, however, have documented cell-type–specific
transcriptional rhythms [5–7], due to methodological limita-
tions. Most of those studies utilized Fluorescence-Activated Cell
Sorting (FACS), the manual isolation of identified cells, or cell-
specific transcriptional profiling techniques, but such methods
are either not applicable to all cell populations or lack the
sensitivity to detect the entire transcriptome; nor do they
distinguish between ribosome-bound (i.e., translating) and
soluble mRNA without the use of polyribosome isolation.
Drosophila is an excellent model for cell-type–specific profiling
of clock cells because of its outstanding genetics and well-
characterized circadian system. Studies have described the fly
circadian molecular oscillator  and the circadian neuronal
circuitry , revealing molecular and functional differences
among groups of pacemaker neurons that mediate morning and
evening bouts of activity or responses of the clock to environmental
cues [5,10–18]. To date, no study has documented genome-wide
expression profiles for all clock cells of the fly head or the complete
translatome of such cells. In this study, we describe use of the
Translating Ribosome Affinity Purification (TRAP) method 
to define the rhythmic translatome of circadian clock cells. Our
results reveal a daily synchronization of protein synthesis and
identify novel cycling mRNAs within clock cells that are required
for diverse physiological processes.
Implementation of TRAP for Studies of Circadian Biology
Previous studies have shown that TRAP reflects the transla-
tional status of mRNAs in a manner similar to that of conventional
polyribosomal analysis . In addition, a recent study in
Drosophila indicates that an EGFP-L10a fusion incorporates into
polysomes and can be employed for cell-specific translational
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profiling . To employ TRAP in our studies, we generated
Drosophila strains carrying a UAS-EGFP-L10a transgene insertion
(see Materials and Methods). Using a pan-neuronal driver (elav-
Gal4), we found that the EGFP-L10a fusion has a cytoplasmic/
nucleolar pattern of localization in neurons (Figure 1A–C),
consistent with incorporation into ribosomes. Indeed, the ring
shape pattern in nucleoli (seen in the nucleus of Figure 1A) likely
results from expression in the Granular Component (GC,
Figure 1D), a structure within which ribosomal proteins assemble
into functional ribosomes. As expected, EGFP-L10a was localized
in all neurons of the adult nervous system (Figure 1E). In contrast,
the tim-uas-Gal4 driver results in expression within the cytoplasm of
clock neurons and glia of the nervous system (Figure 1F) or only
clock neurons when combined with repo-Gal80 (Figure 1G), which
inhibits expression in all glial cells. A different GFP–Drosophila
ribosomal protein fusion (L11) has an identical intracellular
localization pattern . In addition, it has recently been shown
that our EGFP-L10a fusion localizes to branch points of neuronal
dendrites, consistent with incorporation into ribosomes that
mediate local protein synthesis . Collectively, these pieces of
evidence indicate that EGFP-L10a incorporates into functional
We examined circadian locomotor activity of flies overexpress-
ing the UAS-EGFP-L10a transgene in clock cells (Pigment
Dispersing Factor, PDF, or Timeless, TIM) to determine whether
there were adverse effects on behavior. As shown in Figure 1H,
these files have normal behavioral rhythmicity, indicating that
EGFP-L10a does not act in a dominant negative manner even at
high levels [Average periods (P) and Rhythmicity Indices (RI) were
23.760.08/0.5760.02, 24.060.03/0.5560.01, and 24.360.14/
0.5060.03 for control, pdf-Gal4.UAS-EGFP-L10a, and tim-uas-
Gal4.UAS-EGFP-L10a flies; n=17–30]. Thus, the presence of
GFP-tagged ribosomes in clock cells does not affect their function.
TRAP Can Detect Changes in Translational Status
We optimized TRAP methods for use with Drosophila and
demonstrated that significant amounts of RNA could be immu-
noprecipitated from head tissues of flies expressing UAS-EGFP-
L10a under control of the pan-neural elav-Gal4 or clock cell tim-uas-
Gal4 driver (see Materials and Methods). Prior to pursuing
genome-wide studies, we wished to determine if our Drosophila
TRAP methods could detect bona fide changes in translational
status. To ask this question, we employed overexpression of Iron
Regulatory Protein (IRP), which is known to repress translation of
an unspliced form of ferritin (fer) mRNA by inhibiting binding of
the small ribosomal subunit to the message. We generated act5C-
Gal4/tub-Gal80ts, UAS-EGFP-L10a/UAS-IRP flies in order to be
able to activate expression of the TRAP and IRP transgenes
conditionally during larval development (by raising temperature to
inactivate Gal80ts, an inhibitor of Gal4). Larvae of this genotype
and controls (act5C-Gal4/tub-Gal80ts; UAS-EGFP-L10a) were ex-
posed to 30uC to activate expression of UAS-EGFP-L10a in both
genotypes and additionally UAS-IRP in the experimental class.
Early pupae were collected for both genotypes and subjected to
TRAP coupled with Q-RT-PCR to quantify ribosome-associated
fer mRNA (relative to control Rp49 mRNA). Similar to previous
studies in Drosophila that employed polysome gradient analysis
, we observed IRP-induced translational repression of an
unspliced but not a spliced form of fer (Figure 1I). Indeed,
translation of spliced fer was enhanced slightly by IRP overex-
pression, similar to that observed from the analysis of a high
molecular weight polysome fraction in the previous study .
This result shows feasibility for the use of TRAP in Drosophila to
detect changes in translational status.
To determine if our methods were able to detect rhythmic
changes in the ribosomal association of cycling transcripts, we
examined the period (per) and timeless (tim) clock mRNAs. TRAP
methods were employed to immunopurify RNA from head tissues
of elav-Gal4/UAS-EGFP-L10a flies two times of day (ZT11 and
ZT23, the times of high and low per/tim RNA abundance,
respectively). Extracted RNA was then subjected to Q-RT-PCR,
using gene-specific primers, to detect the clock mRNAs. Figure 1J
shows that the abundances of ribosome-bound tim and per clock
mRNAs are significantly higher at ZT11 than at ZT23. This result
is consistent with the known rhythmic profile of tim and per RNA
abundances at the two time points (higher at ZT11) and the
expected translational status of the mRNA at the two times of day.
We emphasize that Figure 1J shows differences in ribosome
association of the clock RNAs, not simply the previously
documented RNA abundance for per and tim. In addition, we
note that the temporal resolution of our measurements does not
exclude translational regulation of per mRNA, which has been
suggested in certain studies [24–26]. Nonetheless, these results
demonstrate that TRAP methods are capable of detecting diurnal
changes in the translational status of specific mRNAs.
Clock Cell-Specific Expression Profiling Efficiently Detects
Circadianly Translated RNAs
Using the newly developed methods, we performed TRAP on
head tissue lysates of tim-uas-Gal4; UAS-EGFP-L10a flies that were
collected at 4-h intervals during the first two days of constant
darkness (DD) following entrainment to LD 12:12. Such flies
express the EGFP-L10a fusion in all clock cells of the head,
including the ,150 pacemaker neurons, photoreceptors, and glia.
RNA was extracted from affinity-purified samples and used to
generate libraries representing all ribosome-associated transcripts
(see Materials and Methods). TRAP libraries corresponding to six
different times of the circadian cycle (CT0, 4, 8, 12, 16, and 20)
were independently constructed for DD1 and DD2 (see details in
Materials and Methods). Libraries were sequenced, using a
multiplexing strategy, to produce single end, 100 base sequencing
reads; these were mapped to the Drosophila reference genome and
analyzed as described in Materials and Methods.
The circadian clock controls daily rhythms in physiology
and behavior via mechanisms that regulate gene expres-
sion. While numerous studies have examined the clock
regulation of gene transcription and documented rhythms
in mRNA abundance, less is known about how circadian
changes in protein synthesis contribute to the orchestra-
tion of physiological and behavioral programs. Here we
have monitored mRNA ribosomal association (as a proxy
for translation) to globally examine the circadian timing of
protein synthesis specifically within clock cells of Drosoph-
ila. The results reveal, for the first time in any organism, the
complete circadian program of protein synthesis (the
‘‘circadian translatome’’) within these cells. A novel finding
is that most mRNAs within clock cells are translated at one
of two predominant circadian phases—midday or mid-
night—times of low energy expenditure. Our work also
finds that many clock cell processes, including metabolism,
redox state, signaling, neurotransmission, and even pro-
tein synthesis itself, are coordinately regulated such that
mRNAs required for similar cellular functions are translated
in synchrony at the same time of day.
Circadian Regulation of Protein Translation
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We employed two recently developed programs, JTK_CYCLE
and ARSER [27,28], to compare their usefulness for detecting
circadian rhythms in the ribosome association of mRNAs. Using
criteria and statistical cutoffs described in the Materials and
Methods section, 1,195 and 263 translationally cycling mRNAs
were detected by the ARSER and JTK_CYCLE programs,
respectively. Interestingly, the majority of the cycling mRNAs (203
out of 263) detected by JTK_CYCLE were also detected by the
ARSER program (Figure 2A), indicating consistency of the two
analyses. Figure S1 shows robust cycling for eight mRNAs out of
the 60 identified by JTK_CYCLE but not ARSER. Thus,
JTK_CYCLE may identify cycling mRNAs not detected by
ARSER. Table S4 lists the 1,255 mRNAs that were identified as
exhibiting significant translational cycling by either program
(mRNAs identified by both programs are indicated in bold). The
False Discovery Rate (FDR) calculated by the ARSER program at
the relevant p value was 0.148, indicating that approximately 186
mRNAs are false positives. This FDR is quite low relative to other
recent genome-wide studies of cycling mRNAs [29–31]. We did
not compute an FDR for the JTK_CYCLE program, because
203/263 mRNAs identified by JTK_CYCLE are included in the
ARSER dataset, and therefore the latter dataset represents a good
approximation of FDR for our analyses. Based on the ARSER
analysis, we estimate that approximately 1,069 of these mRNAs
show circadian changes in translation in clock cells of the adult
head, representing about 10% of all analyzed genes in the genome.
This large number of cycling mRNAs is consistent with recent
studies utilizing manual dissection approaches to perform cell-
specific transcriptional profiling of the Drosophila PDF clock
neurons [5,10]. Cell-specific profiling methods may identify a
Figure 1. Expression of EGFP-L10a and assays of function in clock cells. (A–C) Expression of EGFP-L10a in a large neurosecretory cell. Nu,
nucleolus; N, Nucleus; C, Cytoplasm. Staining for a nuclear protein called LARK (red signal) is used to identify the nucleus. (D) Schematic
representation of the structure of the nucleolus. FC, Fibrillar Center; DFC, Dense Fibrillar Components; GC, Granular Components. GC is the location of
ribosome assembly. (E) Expression pattern of EGFP-L10a in the brain and ventral ganglion using the elav-Gal4 pan-neuronal driver. (F) Expression of
EGFP-L10a in all clock cells driven by tim-Gal4. (G) Restricted expression of EGFP-L10a to clock neuron but not glia using a combination of tim-Gal4
and repo-Gal80. (H) Expression of EGFP-L10a in clock cells does not disrupt normal circadian behavior. Left panels shows representative free-running
actograms of control flies and flies expressing EGFP-L10a in either PDF neurons (using pdf-Gal4) or all clock cells (using tim-Gal4). Right panels show
the corresponding correlograms. (I) TRAP is capable of detecting changes in mRNA translation, as assayed by changes in the translational status of
Ferritin 1 Heavy Chain Homolog (Fer1HCH) mRNA in response to overexpression of the Iron Regulatory Protein (IRP). Control, w1118; act5C-Gal4/tub-
Gal80ts; UAS-EGFP-L10a/+. IRP overexpression, w1118; act5C-Gal4/tub-Gal80ts; UAS-EGFP-L10a/UAS-IRP. (J) Circadian changes in the translation of period
(per) and timeless (tim) mRNAs. Genotype of the flies assayed, elav-Gal4; UAS-EGFP-L10a/+. Error bar represents standard error of the mean (SEM).
*p,0.01; **p,0.001 (Student’s t test).
Circadian Regulation of Protein Translation
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larger number of cycling Drosophila mRNAs, relative to previous
studies, due to a more homogeneous starting cell population (i.e.,
Known Cycling mRNAs Exhibit Translational Rhythms
We examined a number of mRNAs in our datasets that had
previously been shown to exhibit abundance rhythms to assess the
quality of our datasets. These include both clock and clock-
regulated mRNAs (per, tim, vri, clk, to, fer2, slob, ugt35b, 5-HT1A, bw,
Ir, and WupA). All showed translational rhythmicity (Figure 2B)
with an expected phase, although the tim rhythm damped on DD2.
Figure 2C, for example, shows robust rhythmicity in the sequence
reads for per and lack of rhythmicity for a nearby gene. Our
analysis also revealed translational cycling for many other genes
that express rhythmic mRNAs. For example, our list of mRNAs
includes 49 of 420 mRNAs showing circadian abundance rhythms
identified in five previous microarray-based studies (see Introduc-
tion). This comparison does not include a recent study that
identified 2,751 cycling mRNAs in hand-dissected PDF neurons
; our results include 172 of those mRNAs (see Table S4).
Interestingly, Ugt35b mRNA, one of several encoding fly
glucuronosyltransferase activity, was previously shown to exhibit
transcriptional cycling in head tissues but not in PDF neurons
. Given that we employed a clock cell tim-Gal4 driver in our
TRAP studies, we suggest that Ugt35b cycles in other clock cells of
We conducted TRAP combined with quantitative PCR for
Ugt35b, tim, and 18 novel cycling mRNAs (not previously found to
show abundance rhythms in head tissues) to verify results obtained
by RNA-seq. As expected, Ugt35b and tim exhibited rhythmicity,
presumably a consequence of their mRNA abundance rhythms.
Of the novel mRNAs, 15/18 showed rhythmic changes in
translation, with a profile very similar to that observed with
RNA-seq analysis (Figure S2). We further analyzed cycling of a
number of these mRNAs in the per0mutant, which lacks a
functional clock, during the first day of constant darkness (DD1).
Figure 2. Identification of mRNAs displaying a circadian translational rhythm in clock cells. (A) Number of rhythmically translated genes
identified by two different programs: JTK_CYCLE and ARSER. (B) Translational profile of known cycling genes. The y-axis represents normalized read
counts. (C) Quantification of sequence reads aligned to the period (per) gene and a nearby nonrhythmic gene (CG2658) across the time-series.
Circadian Regulation of Protein Translation
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We found that rhythmic expression of these mRNAs was abolished
in the per0mutant, confirming their circadian clock regulation
Translational Profiling Reveals Circadianly Synchronized
Previous genome-wide studies showed that peaks of mRNA
abundance occur at many different circadian phases (see Figure
S4). In contrast, our profiling of the clock cell translatome revealed
a striking feature of circadianly regulated protein synthesis. We
found that peak translation for most of the 1,255 mRNAs
identified in our study occurs predominantly during two circadian
phases: midday or mid-night (Figure 3A–C; Figure S4). These are
times of relative behavioral quiescence and just prior to initiation
of locomotor activity bouts (Figure 3A, lower panel). Thus, protein
synthesis may be confined to times of day that require reduced
metabolic expenditure and/or are just prior to initiation of
behavioral activities. A further analysis revealed surprisingly
synchronized translation of mRNAs required for the same cellular
process: translation is predominantly unimodal (with a peak during
the day or night) or biomodal, depending on the process
(Figure 3C). This bias in the timing of translation was true of
many other cellular processes (Figure 3D). For example, of the 10
enzymes involved in glucose metabolism in our list of cycling
RNAs, nine are translated during the day. In contrast, all 10
GPCRs in our list are translated during the night (Figure 3E).
Of note, mRNAs encoding a number of translational initiation
factors (eIF4E isoforms) exhibit cycling with a phase that
corresponds to the daytime peak of circadian translation (Figure
S5). Thus, circadian translation of these eIFs may contribute to a
broad clock regulation of protein synthesis. In contrast, the major
initiation factor, eIF4E-1, does not exhibit translational cycling,
suggesting that it does not participate in circadian regulation
(Figure S5). Consistent with previous results indicating that
ribosome biogenesis is regulated in a circadian manner , 20
mRNAs encoding ribosomal proteins, translation initiation factors,
or other translational regulatory components show translational
rhythmicity (Table S4).
Translational Regulation Contributes to Circadian Gene
The synchronized rhythmic expression profiles identified by our
cell-specific profiling approach may result from a clock regulation
of translation or mRNA abundance. To ask whether changes in
Figure 3. TRAP identifies two major phases of rhythmic translation. (A, Upper) A heat map showing the relative level of translation during
DD days 1–2 for each of the 1,255 genes. Genes are arranged vertically according to their phases. (A, Lower) Population plot of free-running activity
(DD days 1–2) for the fly strain used to generate the translational profiles (vertical axis, activity level; horizontal axis, time of day). n=17, error bars are
SEM. (B) Phase distributions of ribosome association for all cycling RNAs. (C) Phase distributions of cycling RNAs relevant for several different cellular
processes. Horizontal axes show phase; vertical axes indicate the number of RNAs. (D) Day or night distribution for major biological processes. (E)
Translational profiles of mRNAs representing two functional groups: G protein–coupled receptors (upper panel) and glucose metabolic enzymes
Circadian Regulation of Protein Translation
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translational status contribute to the synchronization of gene
expression in clock cells, we carried out additional studies, using
We reasoned that total RNA isolated from whole heads
contains mRNAs from both clock and nonclock cells. Thus, if a
gene is robustly expressed in nonclock cells, the abundance
profile obtained from whole head total RNA will not represent
its expression profile in clock cells. However, for mRNAs
predominantly expressed in clock cells (such as per, tim, and
others), assays of total head RNA will reflect clock cell
expression. Such an mRNA ought to show enrichment in a
TRAP sample from tim-uas-gal4.EGFP-L10a heads relative to
total RNA from the starting lysate, and the circadian expression
profile, when assayed from total RNA, should approximate the
profile in clock cells. Thus, if such an mRNA shows a rhythm by
TRAP but not in total RNA, then it is likely to be regulated at
the translational level.
To identify mRNAs enriched in clock cells, we created new
genome-wide libraries for TRAP and total RNA samples from
head tissues of tim-uas-gal4.EGFP-L10a–expressing flies. These
were sequenced to identify mRNAs that are substantially
enriched by TRAP relative to total RNA—that is, enriched in
clock cells. We identified many that show an enrichment within
clock cells similar to or greater than that observed for tim
mRNA. Forty-nine of them are present in our previous list of
cycling mRNAs. We chose 12 cycling mRNAs from the
enriched list and examined their expression profiles in total
RNA versus TRAP RNA samples using Q-PCR methods. Of
the 12 mRNAs tested, three did not show cycling similar to that
detected by RNA-seq analysis (25%, and the same as we
reported for another set of RNAs; Figure S2); thus, these three
were not examined further. Of the remaining nine mRNAs,
which showed cycling by Q-PCR similar to that detected by
RNA-seq, three of them exhibited constant abundance in total
RNA but showed circadian cycling in ribosome association,
indicating that they are likely regulated at the translational level.
Figure 4 shows cycling profiles for these three mRNAs and a
fourth mRNA showing both abundance and ribosome-associa-
tion rhythms (Figure 4D). Thus, for certain mRNAs, there is
good evidence for a clock regulation of translation.
Broad Circadian Regulation of Clock Cell Physiology
We manually annotated the proteins encoded by the 1,255
cycling RNAs using information obtained from Flybase and
classified them by biological process (Figure 5A). Of the
annotated genes, the most represented functional class is
metabolism/energy production, including NAD-dependent pro-
cesses and oxidation-reduction reactions. This class includes 85
genes involved in intermediary metabolism, 14 genes with
mitochondrial functions, and 46 genes that regulate oxidation-
reduction processes. These results are consistent with Drosophila
and mouse circadian transcriptional profiling studies that
identified a large subset of metabolic genes [33,34]. Another
overrepresented group is signaling (including both intracellular
pathways and intercellular signaling mechanisms). Interestingly,
44 members of the signaling class belong to the G Protein
signaling family, represented by many G Protein Coupled
Receptors (GPCRs) and GTPases.
Rhythmic Translational Regulation of the NADP+/NADPH
Ratio and Cellular Redox State
Several particularly interesting cycling mRNAs encode proteins
that potentially modulate the NADP+/NADPH ratio or are
known components of the cellular redox (thioredoxin) system.
Examples include the CG3483 and CG7755 genes, both predicted
to encode isocitrate dehydrogenase-like proteins. While at least
one isocitrate dehydrogenase (IDH) is a component of the
mitochondrial citric acid cycle, others have a cytoplasmic
localization, producing aketoglutarate with a conversion of
NADP+ to NADPH . We also found that the mRNA
encoding Glutathione Transferase E10 (GstE10), which utilizes
the redox regulator glutathione in detoxification reactions, exhibits
a translational rhythm (Table S4). Interestingly, it was recently
shown that glutathione and a different Gst mRNA (GstD1) show
circadian changes in abundance in Drosophila head tissues ,
suggesting a complex regulation of redox state.
Components of the thioredoxin (TRX) system, a general
regulator of cellular redox state, are also under circadian
control. Thioredoxin T (TrxT) and Thioredoxin reductase (Trxr-2)
mRNAs show robust circadian changes in translation, with
peaks in the late subjective day (Figure 5B). This circadian
translation may reflect an underlying transcriptional control as
both TrxT and Trxr-2 show mRNA abundance rhythms in
Drosophila head tissues (Figure S6). Of interest, it was previously
suggested that TrxT showed an mRNA abundance rhythm
within the Drosophila PDF clock neurons, but this was based only
on examination of two circadian times in a screen for cycling
mRNAs . Thioredoxin reductases are known to catalyze
reduction of thioredoxin, in the process converting NADPH to
NADP+ , an important regulator of cellular redox. In
addition to these TRX system genes, Grx-1, a glutaredoxin also
involved with cell redox state homeostasis, shows circadian
translational cycling (Table S4). Rhythmicity in cellular redox
state is significant as it regulates many biochemical processes
including circadian transcription factors (see Discussion).
Circadian Translational Regulation of Nervous System
Previous studies have indicated that synaptic vesicle cycling
mechanisms are important within clock neurons  and glial cells
 for circadian oscillator or output functions. Similarly, there
are reciprocal interactions between the oscillator and neuronal
membrane events, including ion channel activity, that are critical
for timekeeping in Drosophila and mammals [6,40,41]. It is of
interest that we identified mRNAs encoding at least 20 ion
channels or channel regulatory proteins that exhibit rhythms in
ribosome association. These include cac (Ca2+channel), Ir (K+
channel), SK (K+ channel), Slob (K+ channel regulator), and inaF-B
(Trp channel regulator), although Ir showed significant rhythmic-
ity only during day 1 of DD. Interestingly, however, Ir was
identified in a previous study as a rhythmic mRNA within PDF
neurons that is important for oscillator function . Likewise, a
number of mRNAs encoding vesicle trafficking or release proteins,
including exo70, syn, and unc-104, exhibited rhythmicity in our
We note that at least two potential brain glial mRNAs were
revealed in our study: CG9977 and CG6218. CG9977 encodes
adenosylhomocysteinase activity, whereas CG6218 encodes an
ATPase. Both were identified in a previous microarray-based
screen for Drosophila mRNAs enriched in glial cells , and are
known to be expressed in the adult brain according to FlyAtlas
. The CG9977 enzymatic activity converts S-adenosyl-L-
homocysteine to L-homocysteine and adenosine, the latter a
known mammalian gliotransmitter . As the tim-uas-gal4 driver
is expressed in neurons and glia (including astrocytes) with PER-
based oscillators, CG9977 and CG6218 may be expressed in the
latter cell type.
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Tdc2, a Rhythmically Translated mRNA, Is Expressed in
PDF Neurons and Required for Circadian Locomotor
Tdc2 encodes the neurally expressed isoform of tyrosine
decarboxylase, which converts tyrosine to tyramine, the latter
compound acting as a substrate for octopamine synthesis. In
Drosophila, both tyramine and octopamine serve as neurotrans-
mitters, regulating diverse functions including adult locomotion,
male aggression, male courtship, drug sensitivity, ovulation,
circadian activity rhythms, and appetitive memory formation
[45–49]. Therefore, it is of interest that Tdc2 mRNA exhibits
circadian translational rhythms in clock cells (Figure 6A; Table
S4). We verified the circadian translation of Tdc2 using the
TRAP technique coupled with Q-RT-PCR (Figure 6B), and
showed that this rhythm is abolished in per0flies (Figure S3).
Using an anti-TDC2 antibody, we further verified that
expression of the TDC2 protein exhibits the predicted circadian
changes in two major groups of clock neurons, the l-LNvs and
the LNds (Figure 6C and D).
We used two different strategies to characterize the expression
pattern of Tdc2 in the adult brain, in particular in various
groups of clock cells. First, we characterized the expression
pattern of a Tdc2-Gal4 transgene  and its co-localization
with PERIOD protein. We found that a UAS-GFP reporter,
driven by Tdc2-Gal4, was expressed in multiple regions of the fly
brain, as expected. However, the only clock cells showing GFP
fluorescence (identifiable by PER expression) were the ventral
lateral (LNv) PDF neurons of the brain (Figure S7A), which are
critical for circadian behavior . Next, using anti-TDC2
antibody, we localized the TDC2 protein in flies expressing a
membrane-bound GFP (mCD8-GFP) in all clock cells (driven by
tim-uas-gal4). As expected, we found that there is a strong
immunoreactive signal for TDC2 in many nonclock neurons.
Within the clock neuronal population, we detected TDC2
immunoreactivity in all l-LNvs (Figure S7B, a–d), s-LNvs (a–d),
and LNds (i–k), as well as a few cells in the DN1 region (l–n).
Finally, a comparison of TDC2 immunoreactivity and Tdc2-gal4
driven mCD8-GFP expression found that Tdc2-gal4 does not
express in all TDC2 immunoreactive cells (unpublished data),
indicating that the Tdc2-gal4 transgene does not reflect the
complete expression pattern of the Tdc2 gene. These results
suggest that rhythmic production of TDC2 in various clock
neurons, and a consequent rhythm in release of tyramine and/
or octopamine from these cells, may be required for normal
To assess the role of Tdc2 in circadian behavior, we analyzed
locomotor activity of the Tdc2RO54mutant, which carries a point
mutation that abolishes the enzymatic activity of TDC2 .
Consistent with previous reports , we found that Tdc2RO54
mutants displayed decreased activity (Figure 7A). In addition,
however, the mutant population exhibited decreased rhythmicity.
The average Rhythmicity Index (RI) for Tdc2RO54was 0.1860.02
compared to 0.5660.02 for control flies, and indeed only 2063%
of the mutant population displayed significant free running
rhythms, whereas the control population was 100% rhythmic
(Figure 7A). We note that decreased activity does not result in
arrhythmicity, as there was no correlation between activity level
and rhythmic locomotor activity (Figure 7B). These observations
indicate that octopamine and/or tyramine are required for normal
circadian behavior (see Discussion).
Figure 4. Comparison of abundance and ribosome-association profiles for several mRNAs. (A–C) Examples of mRNAs that show constant
abundance but rhythms in ribosome association. (D) An example of an mRNA showing both abundance and ribosome association rhythms. RNA
abundances were normalized to that of Rp49 for each time point. Abundance is expressed relative to that of the first time point (CT0), which was
designated a value of 1. Negative and positive error bars show the range of possible relative values calculated based on the SEM of the Ct values
obtained in the Q-PCR experiments. Each data point represents a sample size of 6 (3 biological replicates, each with 2 technical replicates).
Circadian Regulation of Protein Translation
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To ask whether the observed arrythmicity of the Tdc2 null
mutant results from loss of Tdc2 function in clock cells, we examine
circadian behavior in flies with a Tdc2 knockdown specifically in
clock cells. We found that populations of flies expressing Tdc2
RNAi, driven by either pdf-gal4 or tim-uas-gal4, were 75%
(Figure 8A,B,F), whereas control flies were normally rhythmic
(Figure 8C–F) Thus, tdc2 is required within clock neurons for
normal locomotor activity rhythms.
Synchronized Translational Rhythmicity
This study is the first to profile the circadian translatome of a
defined cell population in a complex tissue. In contrast to previous
studies showing that mRNA abundance rhythms peak at multiple
circadian phases (Figure S4), our results indicate that translation of
most rhythmic transcripts within clock cells is restricted to two
major phases—midday and mid-night. Furthermore, we provide
evidence that circadian regulation of either mRNA abundance or
protein synthesis (depending on the mRNA) contributes to this
synchronization. We speculate that protein synthesis may occur
predominantly at circadian phases that are associated with
reduced metabolic expenditure. In Drosophila, such times coincide
with behavioral quiescence, just prior to initiation of locomotor
activity bouts (Figure 3). The synchronized translation of
functionally related mRNAs (Figure 3B–D) suggests a clock-
orchestrated sequential activation of biological processes; these
results reinforce the concept that fundamental cellular processes
are under circadian control within clock cells.
Two significant technical improvements enabled the discovery
of synchronized translation. First, our analysis was restricted to
circadian clock cells, circumventing the problem of profiling a
mixed population, in which some cells express a rhythmic mRNA,
whereas others express the same mRNA constitutively (thus
masking rhythmicity). In addition, different cell types may express
out-of-phase rhythmic mRNAs, also masking a rhythm in a mixed
cell population. Second, our technique analyzes ribosome
association rather than steady-state mRNA abundance, represent-
ing a more direct assessment of protein expression. Although it is
not currently technically feasible to directly compare transcrip-
tional and translational rhythms in the same cell types, our results
Figure 5. Biological processes represented by the rhythmically translated mRNAs. (A) Pie chart showing different represented processes.
The number of mRNAs belonging to each category is shown next to each slice of the pie. (B) Translational profile of thioredoxin system mRNAs.
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indicate that translational regulatory mechanisms contribute to
synchronized protein synthesis. Consistent with this idea, we have
shown that mRNAs encoding relevant translational regulatory
factors are rhythmically expressed. These include translation
initiation factors, ribosomal proteins, and enzymes involved in
rRNA and tRNA synthesis (Table S4). In mammals, ribosome
biogenesis is known to be regulated by the circadian clock .
Thus, it is possible that the circadian clock regulates translation of
many mRNAs, including those relevant for clock function [25,26]
by controlling availability of the translational apparatus.
Rhythms in Cellular Redox State
We document rhythmic translation of mRNAs within clock cells
that is relevant for diverse biochemical and behavioral functions. A
particularly interesting class includes factors important for cell
redox homeostasis (CG3483, CG7755, TrxT, and Trxr-2), as it has
been demonstrated that a clock control of redox state drives
rhythms in the excitability of suprachiasmatic nuclei (SCN)
neurons . Furthermore, there is redox control of many cellular
factors, including enzymes, receptors, cytokines, growth factors,
and transcription factors. Thioredoxin, for example, regulates
NFkB activity , which is known to be under circadian control
. NADP(H) and NAD(H), the reduced forms of these
cofactors, stimulate DNA binding of the CLOCK/BMAL1 and
NPAS2/BMAL1 transcriptional heterodimers, which are critical
components of the mammalian circadian clock . Circadian
translational regulation of cellular redox may be important for
rhythmicity of clock components and clock outputs as well as
metabolic feedback to the clock [33,55].
The rhythm in TrxT translation may also function in another
important circadian output. It has recently been demonstrated that
there is circadian control of peroxiredoxin (PRX) protein
oxidation state in organisms ranging from unicellulars to humans,
and that this rhythm is regulated by an uncharacterized NTO
(reviewed in ). Significantly, oxidized PRX multimers serve as
cellular chaperones and cell cycle modulators. Thioredoxin (TRX)
mediates reduction of oxidized PRX molecules to complete the
PRX catalytic cycle , and thus rhythmic TrxT may contribute to
circadian changes in PRX oxidation state. Of relevance, mRNAs
encoding other chaperones are also rhythmically translated (Table
Evidence for a Novel Neurotransmitter in PDF Neurons
Rhythmic factors important for neurotransmission were also
identified by our analysis. Among them, Tdc2—encoding the
synthetic enzyme for tyramine and octopamine—is rhythmically
expressed in clock neurons and localized to the PDF cell
population. Rhythmic release of these transmitters from PDF or
other clock neurons may contribute to the temporal coordination
of the clock cell circuitry, similar to the role of PDF .
Figure 6. TDC2 protein shows circadian changes in the PDF-positive large ventral lateral neurons (l-LNvs) and dorsal lateral
neurons (LNds). (A–B) Translational profile of Tdc2 revealed by RNA sequencing (A) and Q-PCR (B). In the Q-PCR graph, the level of mRNA
expression for the first time point (CT0) serves as a reference, and is thus designated a value of 1. RNA expression levels at other time points are
plotted relative to the value at CT0. Negative and positive error bars show the range of possible relative values calculated based on the SEM of the Ct
values obtained in the Q-PCR experiments. n$4 for all time points. (C) Abundance of TDC2 protein in the l-LNvs and LNds at two different times of
the circadian cycle, using immunohistochemical methods. (D) Sample images showing differential expression of TDC2 in l-LNvs (red channel) at ZT1
and ZT9. Quantification of average pixel intensities is described in the Materials and Methods section. For LNvs, 10 pairs of brain hemispheres were
compared between ZT1 and ZT9. For LNds, nine pairs of brain hemispheres were compared between ZT1 and ZT9. *p,0.01; **p,1.5E-05 based on
paired Student’s t test.
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Alternatively, rhythmic release of octopamine and/or tyramine
may regulate downstream neurons that drive locomotor activity
Of note, previous studies have suggested a clock control of
tyramine synthesis, showing that there was decreased tyrosine
decarboxylase activity in the brains of per clock mutants . In
addition, mutants of several clock genes, including per, clock, cycle,
and doubletime, were found to be required for normal cocaine
sensitization, a process depending on induction of tyrosine
decarboxylase activity and production of tyramine . Expres-
sion of Tdc2 in clock neurons is consistent with a role for tyramine,
and perhaps octopamine, in this process.
Tdc2 was not described as a cycling mRNA in several previous
genome-wide circadian expression studies [59–63] that utilized
whole fly heads as a starting material. Based on the expression
pattern of Tdc2—that is, broad and strong expression in a large
number of neurons including only certain clock neurons —it seems
likely that the cycling of Tdc2 eluded detection in previous studies
Figure 7. Mutation of the Tdc2 gene results in decreased activity and circadian arrhythmicity for adult locomotor activity. (A)
Quantification of average activity level, average rhythmicity index (RI), and percent of rhythmic flies in wild-type and Tdc2RO54populations. n=25 for
control; n=29 for Tdc2RO54. Error bars represent SEM. *p,0.0001. (B) Representative actograms, mean activity, and correlograms for control flies and
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because of the presence of other TDC2-containing neurons in
which the transcript does not exhibit rhythms in abundance.
Indeed, Tdc2 was included in a long list of mRNAs (2,751) showing
enrichment in the large LNv clock neurons at one time of day
(ZT12) in a recent study that utilized manual dissection procedures
to profile PDF neurons . Our detection of rhythmic Tdc2
translation and confirmation of its role in maintaining circadian
locomotor activity rhythms clearly demonstrates the advantage of
a cell-type–specific approach in genome-wide studies of gene
Materials and Methods
Drosophila Strains, Rearing Conditions, and Genetic
For translational profiling of flies with a normal circadian
clock, males of a homozygous w1118; tim-uas-gal4 stock were
crossed to virgin females of a homozygous w1118; UAS-EGFP-
mL10a stock. F1 progeny carrying both the UAS and Gal4
transgenes (and expressing EGFP-tagged ribosomes in all clock
cells) were collected and used for TRAP experiments. To profile
a clock mutant, females from a homozygous per0w1118; UAS-
EGFP-L10a stock were crossed to a Gal4 strain and only male
flies from the F1 progeny were used in the TRAP experiments.
All flies were reared in a lighting schedule consisting of 12 h of
light and 12 h of dark (LD 12:12) at 25uC and 60% humidity.
The tdc2RO54strain and its isogenic parental strain were gifts
from Dr. Jay Hirsh of University of Virginia. UAS-Tdc2 RNAi
flies were obtained from the VDRC stock center (stock numbers
10687R-1 and 10687R-3).
Construction of a UAS-EGFP-L10a Transgene and
Production of Transgenic Drosophila
The UAS-EGFP-L10a transgene was generated by cloning the
coding sequence of the EGFP-L10a fusion protein (provided by
Nat Heintz) into the pUAST vector. We chose to use the mouse
L10a ribosomal protein (mL10a) because it is virtually the same as
fly L10a (identical in size and ,90% similar)—not surprising for a
ribosomal subunit—and it has been shown to work well for the
TRAP method. The cloning service was provided by Entelechon
(Regensburg, Germany), and the resulting UAS-EGFP-L10a
plasmid was verified by sequencing. The UAS-EGFP-L10a plasmid
was purified using a Qiagen Maxi-prep kit and then used to
generate transgenic flies (Genetic Services, Cambridge, MA).
Genomic insertions were mapped to chromosomes using standard
segregation analysis procedures.
Affinity Purification of Ribosomes and Isolation of
Adult flies were collected in 50 ml conical tubes at desired time
points and flash frozen in liquid nitrogen. Fly heads were collected
by vigorously shaking frozen flies and passing them through
geological sieves according to standard procedures. Approximately
200 heads were employed for each affinity purification experi-
ment. Frozen heads were homogenized in a buffer containing
20 mM HEPES-KOH (pH 7.4), 150 mM KCl, 5 mM MgCl2,
0.5 mM DTT, 100 mg/ml Cycloheximide, and 2 U/ml SUPER-
ase (Life Technologies) and centrifuged at 20,0006g for 15 min to
obtain cleared lysate. After adding DHPC and Igepal CA-630 to a
final concentration of 30 mM and 1%, respectively, the lysates
were incubated on ice for 5 min and centrifuged at 20,0006 g
Figure 8. Knockdown of Tdc2 in clock neurons results in circadian behavioral arrhythmicity. (A–E) Representative actograms showing
free-running locomotor activity of flies with a Tdc2 knockdown in PDF neurons (A) or all clock neurons (B), as well as relevant control files (C–E). (F)
Quantification of the average rhythmicity index (RI) for various genotypes. Number of flies tested is indicated on the histograms. *p,1.4E-30 for
comparison with the control groups based on Student’s t test.
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again for 15 min. After centrifugation, the supernatant was
applied to magnetic beads coated with a purified high-affinity
anti-EGFP antibody (prepared using the Dyabeads Antibody
Couple Kit from Invitrogen) and incubated at 4uC with end-to-
end rotation for 1 h to allow binding of EGFP-tagged ribosome to
the antibodies. Following incubation, samples were washed with a
buffer containing 20 mM HEPES-KOH (pH 7.4), 150 mM KCl,
5 mM MgCl2, 0.5 mM DTT, 100 mg/ml Cycloheximide, and 1%
Igepal CA-630 for five times at room temperature. RNA was
extracted from the beads using the TRIzol reagent (Life
Technologies). Quality and quantity of the isolated RNAs were
analyzed using a Bioanalyzer (Agilent).
Using these methods, we affinity purified RNA-containing
ribosomes from head tissues of adult flies expressing UAS-EGFP-
L10a in all neurons or clock cells. Similar to published studies
, we optimized homogenization procedures for Drosophila
head tissues, included magnesium and cycloheximide in the lysis
buffer to preserve polysomes, inhibited RNAase activity, and
employed a purified, high-affinity anti-GFP antibody for
ribosome precipitation. In those experiments, the UAS-EGFP-
L10a transgene was expressed in all neurons or all clock cells
using, respectively, the elav-Gal4 or tim-uas-Gal4 drivers. In three
pilot experiments—two using elav-Gal4 and one using tim-uas-
Gal4—we obtained a total of 305–544 ng RNA from head
tissues of 200 UAS-EGFP-L10a–expressing flies, whereas there
were negligible amounts (50–100-fold less) of precipitated RNA
in control samples (elav-Gal4 or tim-uas-Gal4 alone) (Figure S8).
Nearly as much RNA was precipitated using the tim-uas-Gal4
driver as with elav-Gal, and we attribute this result to the
strength of the tim-uas-Gal4 driver and the observation that it is
expressed in all clock neurons including photoreceptors and
thousands of glial cells. With expression of UAS-EGFP-L10a in
only the clock neuron population (,150 neurons, some of which
can be seen in Figure 1G), we were able to immunopurify 44 ng
of RNA from 200 fly heads—10-fold more than control
precipitations—indicating good sensitivity for our methods.
Expression of a different ribosomal protein fusion, GFP-
Drosophila L11 , can also be employed for TRAP analysis;
we immunopurified 118 ng of ribosome-bound RNA from elav-
Gal4/UAS-GFP-L11 head tissues starting with 150 flies (unpub-
RNA-Seq Library Construction and Sequencing
We employed standard Illumina protocols and reagents (the
TruSeq RNA sample preparation kit) for RNA-seq library
construction. RNAs extracted from the immunoprecipitation
contain a mixture of mRNAs, ribosomal RNAs, and other small
RNAs that are involved in translation, such as tRNAs. Using the
TruSeq RNA kit, mRNAs were isolated using poly-dT coupled
magnetic beads and fragmented by addition of divalent cations at
94uC. Cleaved mRNAs were then reverse transcribed into cDNA
using random primers, and cDNA was subjected to second strand
synthesis using DNA polymerase I and RNaseH. DNAs were end
repaired, ‘‘A’’ tailed, and then ligated to Illumina sequencing
adaptors prior to enrichment by PCR to create a library.
Sequencing of libraries was accomplished using an Illumina
HiSeq 2000 in the Tufts Medical School Molecular Core Facility.
Sequence reads were obtained and their quality analyzed using the
quality control metrics provided by the FastQC pipeline (http://
www.bioinformatics.babraham.ac.uk/projects/fastqc/). We ob-
tained, on average, 21 million high-quality 100-b reads for each
of the 24 samples (after removing low-quality reads), and an
average of 82% of the high-quality reads could be mapped to the
Drosophila 5.22 reference genome (Table S1) using Tophat (v 2.0.0)
and Bowtie2 (v 126.96.36.199) [64,65]. The reads represent approxi-
mately ,12,000 genes that are expressed in clock cells of the
Analysis of RNA-Seq Data
After mapping with Tophat and Bowtie, we counted the
number of reads aligning to individual annotated genes in the
Drosophila genome using HTseq-count (EMBL). Using these
methods, there was good agreement between the two biological
replicates for each time point in the DD1 and DD2 datasets.
The correlation coefficient (r) of the two replicates was greater
than 0.9 for all time points (Table S2, representative scatter
plots of two replicates are shown in Figure S9). Next, we
conducted a preliminary assessment of each of the four
individual datasets (DD1, replicate 1 and 2 and DD2, replicate
1 and 2) by calculating the ‘‘best cosine correlation’’ for all genes
including 10 genes in our datasets that are known to show
transcriptional cycling from previous studies (Table S3). The
‘‘best cosine correlation’’ is obtained by calculating the
correlation coefficient (r) between read counts of the six time
points and corresponding six values on one of 48 cosine curves
each with 0.5 h difference in phase, and selecting the highest r
value from the 48 comparisons. We found that 10 ‘‘control’’
RNAs had high r values in the two DD1 datasets and DD2
dataset 1. However, poor correlation coefficients were observed
for DD2 dataset 2, and thus this dataset was not used in our
subsequent analyses. Given the good correlation between DD1
datasets 1 and 2, we pooled reads from these two replicates to
generate one set of combined expression values. For DD2, we
employed only dataset 1 in the analysis. As a consequence of
improved sequencing technology, samples of the DD2 dataset 2
contained roughly the same number of reads as the combined
DD1 datasets 1 and 2. Thus, the total number of reads analyzed
for each sample was similar across all time points of DD1 and
DD2—on average ,32 million reads per sample. The resulting
datasets (six time points for both DD1 and DD2) were quantile
normalized to control for variation among experiments.
Identification of Genes and Transcripts Showing a
Circadian Expression Pattern
Relative sequence read coverage at different circadian time
points, quantified using HTseq-count and quantile normalized,
were used to construct a time-lapse expression series and analyzed
using two different programs, ARSER and JTK_CYCLE [27,28],
to identify the presence of circadian periodicity. ARSER was
developed by Yang and Su , and it analyzes circadian
expression data by harmonic regression based on autoregressive
spectral estimation; JTK_CYCLE was developed by Hughes et al.
, and uses a nonparametric algorithm to detect rhythmic
components in genome scale datasets. Results obtained from the
two different analyses were filtered in several ways to obtain the
final set of cycling genes: (1) we required the average raw read
counts across the 12 time points to be at least 20; (2) we required a
‘‘cycling amplitude,’’ defined as K (maximum expression value –
minimum expression value)/median expression value, of at least
0.5; and (3) for results with the ARSER program, p,0.021 was
considered statistically significant, whereas for the JTK_CYCLE
program, p,0.015 was used as a cutoff. As the two programs
appear to have different sensitivities in detecting circadian genes,
different cutoff p values were chosen for them in order to include
the majority of known clock genes. We think the use of this
biological criterion to determine a statistical cutoff is reasonable for
this type of analysis.
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Identification of Genes Predominantly Expressed in Clock
One-week-old adult flies expressing EGFP-mL10a in all clock
cells—that is, carrying one copy each of tim-uas-gal4 and UAS-
EGFP-L10a—were entrained to a LD 12:12 cycle for 3 d at 25uC
and flash frozen in liquid nitrogen at ZT8 on the 4thday. Three
sets of samples, each containing about 200 flies, were collected.
Head collection, homogenization, TRAP, and RNA isolation were
carried out as described above in ‘‘Affinity Purification of
Ribosomes and Isolation of Ribosome-Bound mRNAs.’’ Before
the immunoprecipitation step, 1/10 of the tissue lysate was set
aside for extraction of total RNA. RNAs were isolated from the
TRAP immunoprecipitates (referred to as ‘‘TRAP RNA’’) as well
as from the input whole head lysates (referred to as ‘‘total RNA’’).
Equal amounts (300 ng) of TRAP RNA and total RNA were used
to construct RNA-seq libraries. For each of the three sets of fly
heads, one TRAP RNA library and one total RNA library were
constructed. RNA-seq library construction, sequencing, and
mapping were conducted as described above. Sequence read
counts were obtained using HTSeq (EMBL) with BDGP5,
Ensembl release 68 for gene coordinates. Normalized sequence
read counts were used to test for differential expression between
the TRAP RNA samples and whole head total RNA samples.
Differential expression was determined using the DESeq package
for R . Genes that showed significantly increased abundance
in the TRAP RNA samples were considered to be enriched in
Antibodies and Immunohistochemistry
Adult or larval brain and ventral ganglion were dissected in PBS
(137 mM NaCl, 2.7 mM KCl, 8 mM Na2HPO4N 2 H2O, 2 mM
KH2PO4, pH 7.4) under a dissecting microscope and fixed in 4%
paraformaldehyde. After fixation, tissues were washed three times
with PBST (PBS with 0.1% Triton-X-100), blocked with 5%
Normal Goat Serum (NGS) in PBST for 3 h, and incubated with
primary antibody solution in PBST with 2% NGS at 4uC
overnight. Anti-PER, anti-LARK, anti-TDC2, and anti-PDF
primary antibodies were diluted 1:15,000, 1:2,000, 1:300, and
1:20, respectively. The primary antibody solution was removed the
next day and tissues were washed five times in PBST and
incubated with fluorescence-conjugated secondary antibody for
3 h (Cy3 conjugated goat anti-rabbit secondary antibody from
Jackson ImmunoResearch for PER, LARK, and TDC2; Alexa
Fluor 488 or Alexa Fluor 647 conjugated goat anti-mouse
secondary antibody from Invitrogen for PDF). Following incuba-
tion with secondary antibody, tissues was washed five times in
PBST and mounted on slides in VECTASHIELD mounting
media (Vector Lab).
Confocal Image Analysis and Quantification
Florescence microscopy of brains was conducted using either a
Leica SP2 confocal microscope at the Tufts Center for Neurosci-
ence Research (CNR) Imaging Core or a Leica SP8 confocal
microscope at the Enhanced Neuroimaging Core of the Harvard
NeuroDiscovery Center. GFP, Cy3, and Alexa Fluor 647 were
excited using laser light of 488 nm, 561 nm, and 647 nm,
respectively. Fluorescence excitation and image acquisition in
the three different channels were performed in a sequential
manner to avoid signal bleed-through between channels. One-
micron optical sections were acquired in the vicinity of the LNv
and LNd neurons using a 636 oil objective. Brain specimens
collected from ZT1 and ZT9 were imaged in an alternating order
so that every ZT1 image was paired with a ZT9 image and paired
t tests were used in the final statistical analyses of image
quantification. Such analyses minimize random variation due to
fluctuation in laser power. To quantify TDC2 immunoreactivity in
l-LNv and LNd, Regions of Interest (ROIs) were manually selected
to include all l-LNv cells or all LNd cells based on PDF
immunoreactivity (for l-LNv) or expression of tim-uas-gal4–driven
mCD8-GFP in the appropriate region (for LNd). A custom Image
J program was used to calculate the average pixel intensity across
the entire stack within the ROI for all pixels that had an intensity
value greater than that of a manually selected background region.
Primers and Q-RT-PCR
Quantitative real-time PCR was conducted on a Stratagene
Mx3000P or Mx4000 QPCR system using SYBR Green Real-
time PCR Master Mix (Applied Biosystems). Primer sequences are
listed in Table S5. Primers were tested to be sure a single product
was amplified with the expected melting temperature. A primer
pair for an abundant noncycling gene, Rp49, was used in all
samples to serve as an internal control for the amount of starting
material. The relative abundance of a gene of interest was
calculated based on the difference between the Ct value of the
specific primer pair and that of the Rp49 primer pair.
identified by the JTK_CYCLE but not the ARSER
Translational profiles of a number of genes
result for 20 candidate cycling mRNAs. Two panels are
shown side-by-side for each mRNA containing the sequencing
(left) and Q-PCR (right) results. In the Q-PCR graphs, mRNA
abundance at the first time point (CT0) serves as a reference, and
is thus designated a value of 1. Abundances at other time points
are plotted relative to the value at CT0. Negative and positive
error bars show the range of possible relative values calculated
based on the SEM of the Ct values obtained in the Q-PCR
experiments. n$4 for all time points.
Comparison of RNA Sequencing and Q-PCRs
wild-type and the per0mutant during the first day of DD.
Relative abundances were calculated based on comparison to that
of a noncycling gene, Rp49. Negative and positive error bars show
the range of possible relative values calculated based on the SEM
of the Ct values obtained in the actual Q-PCR experiments. n$4
for all mRNAs analyzed.
Q-PCR analyses for six rhythmic mRNAs in
and mRNA abundance rhythms documented in several
previous studies. Cycling mRNAs are arranged along the x-
axis according to their phases, shown on the y-axis.
Phase comparisons of translation (this study)
For each mRNA, translational level at each time point is
normalized to the average translation across the time series.
Rhythmic translation of the eIF-4E mRNAs.
Trxr-2 and TrxT in total RNA samples collected at two
different time points: CT8 and CT20. n=4 for all time
points. Error bars represent SEM. *p,0.001 (Student’s t test).
Q-PCR analyses of transcript abundance for
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several groups of clock neurons including the PDF positive
large and small ventral lateral neurons (LNvs), the dorsal
lateral neurons (LNds), and dorsal neurons (DNs). (A)
Expression of Tdc2-gal4 in the PDF neurons. Green, expression of
mCD8-GFP driven by Tdc2-gal4; blue, PDF neuropeptide detected
by anti-PDF antibody; red, PER protein detected by anti-PER
antibody; arrow head, large LNvs; arrow, small LNvs; asterisk, PDF-
negative small LNv. (B) Expression of TDC2 protein in LNvs (e–h),
LNds (i–k), and DNs (l–m). Green, expression of mCD8-GFP driven
by tim-uas-gal4 (for marking all clock cells); blue, PDF neuropeptide
detected by anti-PDF antibody; red, TDC2 protein detected by anti-
TDC2 antibody; arrowhead, large LNvs; arrow, small LNvs.
Tdc2 mRNA and protein are expressed in
isolated from flies expressing EGFP-L10a but not from
control flies without the transgene. Note the difference in
the scale of the y-axis.
Using the TRAP technique, RNAs can be
two independent biological samples (sample 1, vertical
axis; sample 2, horizontal axis) for all time points
Scatter plots of read counts for all genes in
RNA-seq statistics for all samples.
samples for each circadian time point.
Correlation coefficients for two independent
pression profiles of known clock mRNAss and those
predicted by a standard cosine function.
Correlation coefficient of the circadian ex-
All identified cycling mRNAs.
Primers used in the Q-RT-PCR experiments.
We thank members of the Jackson lab for help and support with these
experiments. We thank Lax Iyer for implementation of the ARSER and
JTK_CYCLE programs and personnel of the Tufts University Molecular
Core (TUCF) and Center for Neuroscience Research (CNR) Genomics
and Imaging Cores for access to facilities. We thank Lai Ding for access to
the Harvard NeuroDiscovery Center Enhanced Neuroimaging Core, the
development of custom Image J programs for quantification of image
stacks, cosine correlation analysis of the sequencing data, as well as other
computational assistance. We thank John Hogenesch for JTK_CYCLE
and Rendong Yang for ARSER. We greatly appreciate the genomics
support and fly strains provided by FlyBase (flybase.org) and the
Bloomington Drosophila Stock Center, respectively.
The author(s) have made the following declarations about their
contributions: Conceived and designed the experiments: FRJ YH.
Performed the experiments: YH JAA. Analyzed the data: YH JAA.
Contributed reagents/materials/analysis tools: YH JAA LGR FRJ. Wrote
the paper: YH FRJ.
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