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J. Biol. Chem., Vol. 277, Issue 16, 14048-14052, April 19, 2002
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From the
Received for publication, December 27, 2001, and in revised form, February 15, 2002
Circadian rhythms govern the behavior,
physiology, and metabolism of living organisms. Recent studies have
revealed the role of several genes in the clock mechanism both in
Drosophila and in mammals. To study how gene expression is
globally regulated by the clock mechanism, we used a high density
oligonucleotide probe array (GeneChip) to profile gene expression
patterns in Drosophila under light-dark and constant dark
conditions. We found 712 genes showing a daily fluctuation in mRNA
levels under light-dark conditions, and among these the expression of
115 genes was still cycling in constant darkness, i.e.
under free-running conditions. Unexpectedly the expression of a large
number of genes cycled exclusively under constant darkness. We found
that cycling in most of these genes was lost in the arrhythmic
Clock (Clk) mutant under light-dark conditions.
Expression of periodically regulated genes is coordinated locally on
chromosomes where small clusters of genes are regulated jointly. Our
findings reveal that many genes involved in diverse functions are under
circadian control and reveal the complexity of circadian gene
expression in Drosophila.
The use of Drosophila has been at the forefront
of studies of the molecular and genetic basis of circadian rhythms (1). A number of clock genes have been identified in Drosophila,
and interlocked per-tim and Clk feedback loops
are now thought to underlie the central molecular machinery of
circadian rhythms (2, 3). However, we still do not know how expression
of the whole genome is orchestrated by the circadian mechanism nor have
we identified all the genes involved. One comprehensive way to find out
all the rhythmically expressed genes is to utilize microarray. A number
of genes regulated in a circadian manner have been identified in
Arabidopsis and mammalian cultured cells (4, 5). Since
information about all the possible transcription units is available in
Drosophila (6, 7), we can extensively analyze the
data for all the genes relating to their function. Functions of
identified genes can be analyzed using various genetic tool and
databases (9-11) available in Drosophila.
Strain and Sample Preparations--
white1118
was used as a wild-type strain, and ClkJrk
was also used (11). Flies were reared in a regime of 12 h of light
followed by 12 h of darkness
(LD),1 and collected every
4 h over 2 days. Total RNA was prepared from 100 heads of
1-week-old adult males and females using the Fast RNA kit (BIO 101, Inc.) followed by DNase treatment. Double-stranded cDNA was
synthesized from 10 µg of total RNA using Superscript II reverse
transcriptase (Invitrogen) and was used as a template to synthesize
biotin-labeled cRNA by in vitro transcription using an ENZO
BioArray High Yield RNA transcript labeling kit. Amplified cRNA was
fragmented and hybridized to GeneChip Drosophila arrays (Affymetrix, Santa Clara, CA) for 16 h at 45 °C. Hybridized
arrays were washed, stained, and scanned using a Hewlett-Packard
GeneArray Scanner. Affymetrix GeneChip software was used to determine
the average difference between perfectly matched oligonucleotide probes and single base pair mismatches for each probe set. Data were then
scaled globally such that the total intensity of each microarray is
equal. The resulting hybridization intensity values reflect the
abundance of a given mRNA relative to the total RNA population and
were used in all subsequent analyses. Quantitative PCR was performed
using the ABI Prism 7700 and SYBR Green reagents (Applied Biosystems).
Analysis of Cycling Genes--
We examined gene expression
profiles under LD using two successive filters: a periodic filter to
extract genes with periodic expression patterns and a deviation filter
to identify genes where the changes were above background level.
First, to extract genes with periodic expression pattern, we
empirically tested for statistically significant cross-correlation between the temporal expression profiles of each probe set and cosine
waves of defined period and phases. We prepared cosine waves of nine
test periodicities (
Next, to further extract genes whose variation was above background, we
determined the noise level associated with a series of experimental
procedures for each probe set. Two replicate samples (i.e.
two sets of 100 fly heads collected independently at the same time of
the day) were hybridized to two GeneChips. The standard deviation of
the two signal intensities for each probe set was calculated and used
as noise deviation (
To estimate the false positive rate, we generated ~14,000 (the same
number of probe sets) random expression profiles that were normally
distributed using the noise deviations as determined above. Then we
filtered these random expression profiles using two successive filters.
Random profiles produced 27 genes classified as "periodically
changing." We assume that this estimates the false positive rate
(i.e. 3.8% of all genes identified would be false positives).
To analyze periodicity of gene expression profiles under constant dark
(DD), we used damping cosine curves as test waves. We prepared damping
cosine waves of four decay rates (k) from 0.0/h (no damping)
to 0.03/h (half-life is 23.0 h) in increments of 0.01/h. Each was
considered at nine test periodicities and over 60 phases as described
above. We used the same cut-off cross-correlation values and the same
deviation filters as in LD analysis.
Phase Analysis--
To determine the phase of cycling genes, we
tested for correlation between the temporal expression profiles of each
gene and 24-h period cosine waves at 60 different phases. We estimated the phase of each cycling gene from the phase of the cosine wave with
which it was correlated most closely.
Determination of Statistical Significance for Rhythmic Biological
Processes and Periodically Regulated Molecules--
We classified the
cycling genes by biological process category in the Gene Ontology
database (9). For each category, we calculated the probability
of finding at least r periodically regulated genes from the
category size (n) using the cumulative hypergeometric
probability distribution. Probability is given by:
We also performed similar analyses using the LIGAND metabolic database
(9). We mapped cycling genes into the metabolic network in
Drosophila and calculated the probability for observing at
least r cycling genes within n enzymes
metabolizing the same molecule using the cumulative hypergeometric
probability distribution as above.
Analysis of ClkJrk--
For each gene, expression
levels in ClkJrk mutants were averaged with
those in wild-type flies. Both were kept in LD conditions, and
equivalent points in the light-dark cycle were compared. Genes were
classified as "up-regulated" if expression was at least 2 times
higher in the mutant and "down-regulated" if expression was least 2 times lower in the mutant. Otherwise genes were classified as
"unchanged."
Mapping of Periodically Regulated Genes and Calculation of
Chromosome Correlation Maps--
Among 14,010 probes on the GeneChip,
44 probes are for control, and 299 probes map to multiple genes. The
other 13,667 probes map to single genes. Among these, there are several
redundant probe sets that map to the same gene, leaving 13,282 nonredundant probes. 12,795 of these match in FlyBase ID (10) to
identified genes from Release 2 Drosophila genomic sequences
(6, 7). We identified the chromosomal positions of all 12,795 genes
using the BLASTN algorithm. Using the chromosomal positions obtained above, we mapped the genes belonging to each Class I, II, and III on
chromosomes. To detect co-regulated regions, we calculated the
correlation between expression patterns under LD conditions of genes on
the same chromosome as described elsewhere (12). To identify
significantly co-regulated regions, we calculated the average
correlation of six adjacent genes and compared it with the average
correlation of six nonadjacent genes as background. There were 140 chromosomal regions where the average correlation of the six adjacent
genes was more than 3.5 standard deviations from background,
i.e. the mean average correlation of six nonadjacent genes.
This analysis showed that a substantial number of adjacent sets have
correlated expression patterns in comparison with 25 expected
co-regulated regions derived from a control set of nonadjacent genes.
Similar results were obtained from analysis of 2-10 adjacent genes.
Among 140 chromosomal regions, 38 clusters of genes included periodically regulated genes. We also analyzed co-expressed region under the DD condition and obtained similar results to the LD condition.
We have examined temporal patterns of gene expression under LD and
under DD using a GeneChip (Affymetrix) representing the entire genome
(more than 13,500 genes) of Drosophila melanogaster. Flies
were collected every 4 h over 2 days both in LD and DD, and
biotin-labeled probes made from cDNA from 100 heads were used for
hybridization. We estimate that the expression of 6,061 genes (43.4%
of all genes) was detected on GeneChip. The number of genes detected
here is thought to be delimited by the detection method using GeneChip,
and there should be additional cycling genes expressed at a lower level
or in a small number of cells. Data were analyzed through two
sequential statistical filters, and 712 genes (5.3% of the whole
genome) were classified as periodically regulated genes in LD (Fig.
1a). This is likely to be a
minimum number for the genes that are periodically regulated; the
number may increase if a different filtration analysis was applied. Our
analyses might not detect genes that cycle in some cells but not in
others, and moreover, it is technically difficult to monitor genes with
very low levels of transcription. The number of periodically regulated genes in Drosophila is similar to that reported from
Arabidopsis under constant light (4), in which 6% of genes
investigated are rhythmic, but is in contrast to cyanobacteria, in
which nearly all genes are expressed periodically (13). We found that
genes implicated in circadian rhythms, including period
(per) (14), timeless (tim) (15),
Clock (Clk) (11), vrille
(vri) (16), cryptochrome (cry)
(17), and takeout (to) (18), cycled with high amplitude and in similar phase, as previously reported, validating our experimental and statistic procedures (Fig. 1e). We
analyzed the phase of periodically regulated genes at a resolution of
0.4 h and found two peaks around ZT10 and ZT20 (Fig.
1c). The peaks may reflect the after-effect of the change
from dark to light and light to dark since significant peaks were
absent under the constant dark condition (Fig. 1d). The peak
phases of the clock genes, Clk, cry,
per, vri, tim, and to, were not at
these times. We then analyzed the gene expression under DD (Fig.
1b) and found that 115 genes of 712 were still periodically
regulated in the free-running condition (Class I, periodically
regulated both in LD and DD). The remaining 597 genes were judged to be
periodically regulated only in LD (Class II). Surprisingly 341 genes
that were not judged as periodically regulated under LD were, however,
periodically regulated under DD (Class III). Their cyclings might have
been suppressed or masked under LD as suggested from behavioral
experiments (19). In our classification of genes we should note that
because we used a strict filter to identify periodically regulated
genes, genes judged to be not cycling might in fact cycle with low
amplitude. Lists of all genes in each class may be found in
Supplemental Tables I-III. After completion of our work two similar
works using GeneChip were published (20, 21). Their findings are
similar to ours, but there are several differences, specifically the
Class II and III genes were not mentioned in other studies. We think that the major differences are the numbers of sampling and statistical analyses. We analyzed data for 2 days both under LD and DD, while the
previous studies analyzed data only for 1 day in each light condition.
Several interesting periodically regulated genes were identified from
the three classes, I, II, and III (Fig.
2). The reliability of GeneChip data was
assessed by quantitative RT-PCR analyses, which confirmed that both
methods yielded similar data. A novel candidate of clock genes,
Pdp1, showing a robust cycling (Fig. 2a), encodes
a transcription factor with homology to vri (16). Most of
the genes we found to cycle could be classified according to the
category of their "biological process" as defined in the Gene
Ontology database (8) (Supplemental Tables IV and V). Phototransduction
is one such category, which includes a significant number of
periodically regulated genes. Two of these were Class I genes.
Photoreceptor dehydrogenase, Pdh, also showed a clear cyclical expression (Fig. 2). One retinoid-binding protein (CG5958) was
periodically expressed with peak at dusk, while the other retinoid-binding protein (CG10657) belonging to Class II was cycling with almost opposite phases. A number of genes belonged to Class II.
Three opsins, Rh3, Rh4, and Rh6, which
express in the central rhabdomeres of the compound eye's ommatidia,
showed rhythmic expression. Another opsin, Rh5, belonged to
Class III. The ninaA gene encoding cyclophilin, which
transports opsins from endoplasmic reticulum to microvilli membrane,
also showed rhythmic expression. Molecules associating with
Ca2+ signal transduction in the photoreceptors,
inaC, inaD, and trpl, also cycled
(Fig. 2b). The expression of most genes listed above peaked
in the morning, whereas Rh6 and trpl peaked in
the evening. Visual sensitivity is controlled by a circadian rhythm in
insects (22), and it would therefore be interesting to know how
cyclical changes in these genes influence photoreceptor structure and
function.
We also used the LIGAND metabolic database (9) to examine the
functional significance of periodically regulated genes (Supplemental Tables VI and VII). Enzymes and transporters involved in metabolism or
function of glutamate and GABA were periodically regulated. Eaat1, CG5618, and CG7470 are Class I genes, and
Gdh, black, CG4233, and CG7433 are Class II
genes. Gs1 belongs to Class III. All genes except
CG4233 showed robust rhythmicity with peaks in the dark phase. In
mammals, glutamate (23) and GABA (24) are neurotransmitters associated
with clock function. These molecules mimic the dark pulse to a
circadian rhythm in the optic lobe of Musca (25). In
Drosophila, one type of glutamate receptor is highly
enriched in pacemaker neurons (26), and our data suggest glutamate and GABA might have a role in the circadian mechanism. In the light of a
recent finding that the redox state of NAD cofactors is involved in
circadian rhythms (27), it is interesting that many enzymes related to
NAD+, NADH, NADP+, and NADPH metabolism were
periodically regulated. There are 16 periodically regulated genes
directly associated to the synthesis of these nicotinamide nucleotides.
Three are in Class I, and the remaining 13 are in Class II. Their peaks
expression occurred in three phases under LD: noon, dusk, and night.
We next examined the cycling of gene expression in the arrhythmic
mutant of the Clock (Clk) gene (11) under LD. Our
study showed that many genes are cycling only in LD, and we then asked whether the cyclings of the Class II genes are merely a reflection of
light responses. Homozygous ClkJrk mutants show
completely arrhythmic locomotor behavior under DD (11). CLK is a
transcription factor and activates clock-regulated genes (1, 2, 11). In
the Clk mutant, periodically expression of all the clock
genes disappeared (Fig. 1e), and only a few genes were
judged to cycle under LD (7, 16, and 23 genes in Class I, II, and III,
respectively; Supplemental Table VIII). It is possible that the
cycling genes in the ClkJrk mutant may
represent genes controlled by a possible CLK-independent mechanism. We
did not analyze the cycling pattern with two peaks in a day. If cycling
is simply controlled by light-on and -off, it shows a pattern with dual
peaks in a day. We did not investigate this possibility, but genes
belonging to the Class III may have such a property. Further studies
are necessary to investigate this possibility.
Our results suggest that the cycling of the Class II genes is not
merely a result of light exposure during LD but is under circadian
control. Under LD, the expression levels of per,
tim, vri, and to were decreased
relative to Clk (Fig. 1e), whereas Clk
and cry continue to express at a high level as previously shown (11, 17). Fig. 2 shows the expression patterns of seven genes in
ClkJrk. If the transcription level of a gene is
lowered in ClkJrk, the gene might be
up-regulated by Clk, whereas if the level is not affected,
the gene might be regulated by a CLK-independent mechanism. The
expression of about 6% of genes was decreased, while in about 3% of
genes it was increased. In the latter case, their transcription might
normally be down-regulated by genes controlled by CLK. These results
indicate that CLK regulates transcription in many genes, but there are
other genes in which transcription is not directly controlled by CLK.
In addition some genes might be negatively regulated by CLK. We do not
think that these changes are caused by the genetic background
differences as we dealt with genes whose expression level changed over
2-fold or one-half. Our study thus shows that a single mutation in such
a central gene regulator results in global but differential changes of
gene expression.
We mapped the chromosomal locations of genes belonging to each class
(I, II, and III) and found that they were not randomly distributed but
clustered on chromosomes. There were 15 clusters where periodically
regulated genes occupied a highly condensed chromosome interval. This
suggests that temporal gene expression might be locally regulated on
chromosomes. To confirm this possibility, we calculated the correlation
of temporal expression pattern along the neighboring genes on all
chromosomes and found 140 chromosomal regions where neighboring genes
are expressed with a similar pattern to each other. Among them, 38 regions contained at least one periodically regulated gene. There were
six genes in Class I, 24 in Class II, and eight in Class III. For
example, the expression patterns of six neighboring genes belonging to
the cytochrome P450 family, located on the right arm of the second
chromosome, are similar (Fig. 3,
a and b). There is a region where genes are
co-regulated, but their functions are unknown (Fig. 3, c and
d). Moreover, we found that functionally unrelated genes are
co-regulated (Fig. 3, e and f). These results
suggest that the temporal expression of neighboring genes is influenced
by a periodically regulated gene. Searches of each class of
periodically regulated genes so far failed to reveal any common motifs
in the nucleic acid sequence along the putative regulatory region of
each class of cycling genes. We found that the co-regulation of
temporal expression occurs even more globally. The co-regulated regions
were observed along the fourth chromosome at intervals of about 5-10
genes (data not shown). The regular spacing suggests control at the
level of higher order chromatin structure as previously reported in suprachiasmatic nuclei neurons (28). These results suggest that gene expression on chromosomes is globally regulated by circadian mechanisms, although we still do not know their molecular bases. Coordinated gene regulation at the chromatin level might be an economical way in remodeling chromosome structures.
Genome-wide Transcriptional Orchestration of Circadian Rhythms in
Drosophila*,
§¶
,,
, and Department of Pharmacology, Graduate School
of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, § Molecular Medicine Research Laboratories, Institute for
Drug Discovery Research, Yamanouchi Pharmaceutical Co., Ltd., 21 Miyukigaoka, Tsukuba, Ibaraki 305-8585, and the ** Department
of Biology, Faculty of Science, Kyushu University, Ropponmatsu, Fukuoka
810-8560, Japan
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
REFERENCES
INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
REFERENCES
EXPERIMENTAL PROCEDURES
TOP
ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
REFERENCES
) from 20 to 28 h in increments of 1 h. Cosine waves of each test period were considered over 60 phases
(i.e. peaks at 60 equally spaced times in the defined period), yielding a total of 540 test cosine waves. Statistical significance was assessed by an empirical procedure. We generated ~14,000 (the same number of probe sets) normally distributed random expression profiles. Then we calculated correlation between the random
profiles and each of the 540 test cosine waves. Standard deviations and
averages of cross-correlation were virtually equal for 540 cosine
filters despite their different periods and phases. Thus, we searched
for the common cut-off correlation of these cosine filters so that 95%
of random expression profiles were filtered out after passing 540 parallel cosine filters. We determined this value as 99.8% probable
correlation. This analysis is independent of signal strength and
imposes no minimal change in amplitude.
) in subsequent analysis. Expression profiles
which, over the 12 time points, show a standard deviation
(s) greater than noise deviation () with 95%
significance are classified as "changing." 95% probability cut-off
values are determined from 
2 (chi-square) distribution
with 11 degrees of freedom ((12 
1) s2/2 > 19.6751).
where N is the total number of genes within the
genome, and R is the total number of periodically regulated
genes. p values (
log10 (probability)) where
the sum of probabilities is below 0.05 were considered significant.
RESULTS AND DISCUSSION
TOP
ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
REFERENCES
View larger version (79K):
[in a new window]
Fig. 1.
Cycling of gene expression in wild-type
flies kept under LD and DD and in ClkJrk
mutant flies under LD. a, a cluster image of 712 cycling genes under LD. Data were normalized so that the average and
the standard deviation of signal intensities of 12 time points are 0.0 and 1.0, respectively. For each gene, the 12 horizontal bars along the time axis represent a
48-h series of data. The genes were ordered by their peak time to help
to visualize the extensive pattern of cycling. Bars are
colored red for positive values and green for
negative values as shown in the upper color code.
b, a cluster image of 456 genes whose expression is
free-running under DD. The details of representation are similar to
a. c and d, phase distributions of the
peak expression times of periodically regulated genes under LD
(c) and DD (d) derived from data on 712 and 456 periodically regulated genes under LD and DD, respectively. Two major
populations have peaks at around ZT10 and ZT20 under LD. These peaks
are not found in DD. e, periodic expression of
per, tim, vri, Clk,
cry, and to under LD and DD in wild-type and in
ClkJrk mutant background flies under LD. Data of
LD and DD were normalized so that the average signal intensity of 12 time points was 1.0. For data in ClkJrk
background, signal intensities of these genes were divided by the
average signal intensities under LD conditions. WT, wild
type.
View larger version (41K):
[in a new window]
Fig. 2.
Three classes of periodically regulated genes
and validation of GeneChip data by quantitative RT-PCR using wild-type
flies (WT) under LD and DD and
ClkJrk (Clk) mutant flies
under LD. a, Class I genes cycling both in LD and DD;
b, Class II genes cycling only in LD; c, genes
cycling in DD but not in LD. In each class, data are shown for two to
three genes, the rhythmic expression of which was found in this study.
Upper curves in a, b, and c
are based on the GeneChip analyses; lower curves are based
on the quantitative PCR analyses. Pdp1, PAR domain protein
encoding a transcription factor; Pdh, photoreceptor
dehydrogenase; trpl, transient receptor potential-like
encoding a Ca2+ channel; inaC, inactivation no
after-potential C encoding a protein kinase C; inaD,
inactivation no after-potential D encoding a structural protein
containing a PDZ domain; Cyp4e2, cytochrome P450-4e2.
View larger version (56K):
[in a new window]
Fig. 3.
Global chromosomal profiles of periodic gene
expression. a, correlation map for gene clusters that
included the Class I genes on the right arm of the second chromosome.
The green block at the center indicates the group
of six adjacent co-expressing genes, including cytochrome P450,
Cyp6a17 (Class I), Cyp6a23, Cyp6a19,
Cyp6a9, Cyp6a20, and Cyp6a21. The
numbers along the matrix represent the gene number along the
chromosome. Green squares indicates a positive correlation;
red squares indicate a negative correlation. b,
the six cytochrome P450 genes that are periodically co-regulated under
LD and DD. Cyp6a17 is represented by the green
line with the highest peak at a time point of 25 h.
c, correlation map for gene clusters that included
the Class I genes but that have no functional relatedness to each
other. d, three genes on the third chromosome (CG11891,
which belongs to Class I, CG11889, and CG10513) showed similar rhythmic
expressions in LD, and periodic expression of these genes also persists
under DD. Data for CG11889 under DD is not shown here as they included
a few negative values. e, correlation map for gene clusters
that included the three Class II genes on the left arm of the third
chromosome. f, CG7646, CG7654, and CG7433 are Class II
genes, and their neighboring gene, neurocalcin, showed a similar
expression pattern in LD. WT, wild type.
Our study thus reveals the complex transcriptional orchestration of
genes under LD and DD conditions in Drosophila. Although cycling gene expression is not always essential for circadian function,
in clock genes such as cyc (29) and double-time
(30) our study has established the candidacy of many candidate genes that might be implicated in circadian mechanisms. Further work using
genetic tools available in Drosophila should help to explore the function of these genes with the prospect of leading to a greater
understanding of the molecular basis of circadian rhythms in all organisms.
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ACKNOWLEDGEMENTS |
|---|
We are grateful to Michael Rosbash and Jeffrey C. Hall for the ClkJrk strain. We thank Shigeru Iwase for computational analysis; Satoko Hayashi, Tomoko Kojima, Toshie Katakura, Yuko Mitsuiki, and Makiko Haruta for technical assistance; and Masami Horikoshi and Takashi Umehara for hospitality in the use of their laboratory. We also thank Hideo Iwasaki and Kenji Tomioka for discussion and Jennifer Hallinan and Ian Meinerzhagen for comments on the manuscript.
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FOOTNOTES |
|---|
* This work was performed as a part of a research and development project of the Industrial Science and Technology Program supported by NEDO (New Energy and Industrial Technology Development Organization) and was also supported by grants from the Ministry of Education, Science, Sports and Culture of Japan (to T. T. and A. M.).The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The on-line version of this article (available at
http://www.jbc.org) contains Tables I-VIII.
¶ Both authors contributed equally to this work.
To whom correspondence may be addressed. Tel.: 81-298-54-1524;
Fax: 81-298-54-1669; E-mail: hiro@m.u- tokyo.ac.jp.
To whom correspondence may be addressed. Tel.: 81-92-726-4759;
Fax: 81-92-726-4625; E-mail: tanimura@rc.kyushu-u.ac.jp.
Published, JBC Papers in Press, February 19, 2002, DOI 10.1074/jbc.C100765200
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ABBREVIATIONS |
|---|
The abbreviations used are:
LD, 12 h of
light followed by 12 h of darkness;
DD, constant dark;
RT, reverse
transcription;
GABA, 
-aminobutyric acid.
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