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The Journal of Neuroscience, November 1, 2002, 22(21):9305-9319
Genome-Wide Expression Analysis in Drosophila
Reveals Genes Controlling Circadian Behavior
M. Fernanda
Ceriani1,
John B.
Hogenesch2,
Marcelo
Yanovsky1,
Satchidananda
Panda1, 2,
Martin
Straume3, and
Steve A.
Kay1, 2
1 Institute of Childhood and Neglected Diseases,
Department of Cell Biology-ICND216, The Scripps Research Institute,
La Jolla, California 92037, 2 Genomics Institute of the
Novartis Research Foundation, La Jolla, California 92121, and
3 Center for Biomathematical Technology. University of
Virginia, Charlottesville, Virginia 22904
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ABSTRACT |
In Drosophila, a number of key processes such as
emergence from the pupal case, locomotor activity, feeding, olfaction,
and aspects of mating behavior are under circadian regulation. Although we have a basic understanding of how the molecular oscillations take
place, a clear link between gene regulation and downstream biological processes is still missing. To identify clock-controlled output genes, we have used an oligonucleotide-based high-density array
that interrogates gene expression changes on a whole genome level. We
found genes regulating various physiological processes to be under
circadian transcriptional regulation, ranging from protein stability
and degradation, signal transduction, heme metabolism, detoxification,
and immunity. By comparing rhythmically expressed genes in the fly head
and body, we found that the clock has adapted its output functions to
the needs of each particular tissue, implying that tissue-specific
regulation is superimposed on clock control of gene expression.
Finally, taking full advantage of the fly as a model system, we have
identified and characterized a cycling potassium channel protein as a
key step in linking the transcriptional feedback loop to rhythmic
locomotor behavior.
Key words:
oligonucleotide arrays; Drosophila; circadian
outputs; slowpoke; locomotor behavior; gating
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INTRODUCTION |
Circadian behaviors take place at
regular intervals because of the action of a cell autonomous clock,
which marks time even in the absence of environmental information. This
molecular clock relies on negative feedback loops, in which
transactivators drive the transcription of repressor proteins, which in
turn block transcription at their own promoters (Young and Kay, 2001 ).
It is important to note that this oscillation at the mRNA level is only
the first step toward sustainable molecular rhythms, which are
accomplished by introducing a number of "delays" affecting message
stability (Suri et al., 1998) and protein stability (Price et al.,
1998) or controlling subcellular localization (Saez and Young,
1996).
Although we have a relatively good understanding of how these molecular
oscillations are generated in Drosophila, a clear link
between the oscillator and the downstream biological processes under
clock control is still missing. Previous attempts to describe the
extent of circadian transcriptional regulation have identified a
handful of clock-controlled "output" genes. A cDNA library screen identified ~20 oscillating mRNAs, most of them with unknown functions (Van Gelder et al., 1995). Alternative approaches included the screening of subtractive cDNA libraries, which retrieved
circadian regulated gene-1 (Rouyer et al., 1997) and
takeout (Sarov-Blat et al., 2000) differential display,
which identified vrille (vri) (Blau
and Young, 1999), and luminescence-based enhancer trap screens that
uncovered additional clock-controlled genes (ccgs), among those
numb and a putative nicotinamide adenine dinucleotide
kinase (Stempfl et al., 2002) and regular (Scully et al.,
2002).
To carry out a more global examination of clock-controlled
transcription, we used high-density oligonucleotide-based arrays. This
approach has been used successfully in Arabidopsis and
mammals, uncovering an astonishing range of physiological processes
under circadian control (Harmer et al., 2000; Panda et al., 2002;
Storch et al., 2002). Recently three groups used a similar strategy to identify the extent of clock-controlled transcription in flies. McDonald and Rosbash (2001) described 134 genes that oscillate under
free-running [constant dark (DD)] conditions and display altered
levels in the Clkjrk mutant
background. Claridge-Chang et al. (2001), on the other hand, analyzed
cycling profiles under driven [light/dark (LD) cycles] and
free-running (DD) conditions in different genetic backgrounds. They
identified 158 cycling genes, some of which are involved in learning
and memory, vision, olfaction, locomotion, detoxification, and
metabolism. Ueda et al. (2002) reported a very striking observation. Of
712 genes cycling in the fly head in LD, only 115 were also cycling in
DD, and conversely, they identified 341 genes that were considered
cycling only in DD.
To gain deeper insight into clock-controlled processes in the whole
fly, we probed high-density arrays with RNA isolated from both head and
body fractions. We found that although the number of cycling genes is
similar between the two samples, only a small proportion of genes
cycles in both structures, indicating that the clock controls different
aspects of physiology throughout the organism. As is the case in
mammals, we identified a large group of genes that although expressed
to mid-high levels in both fractions are cycling in only one of them.
Finally, taking advantage of the fly as a model genomics organism, we
have characterized a rhythmic potassium channel as a key step in
linking the transcriptional feedback loop to rhythmic locomotor behavior.
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MATERIALS AND METHODS |
Fly stocks. Time courses for the DNA chipping
experiments were performed using y w as wild-type control
and y w;;Clkjrk
(Allada et al., 1998) as the mutant with impaired clock function. The
slo 4 (Atkinson et al., 1991) stock was kindly provided by Dr. N. Atkinson (University of Texas, Austin, TX). The stI
slo I stock (Elkins et al., 1986) was a gift from the Bloomington Stock Center. slo I was generated by ethyl methane
sulfonate mutagenesis and slo 4 was generated by
gamma irradiation; the latter is a result of a chromosome inversion
with one breakpoint within this gene (Atkinson et al., 2000).
slo 4 mutants have been shown to carry a null allele by a
number of approaches, and it fails to complement the slo I
mutation in electrophysiological studies in the dorsal longitudinal
flight muscle (Atkinson et al., 1991). No molecular characterization of
the slo I mutant is available.
RNA and protein time courses. Newly eclosed wild-type
(y w) or mutant (y
w;;Clkjrk) flies were
entrained for 5 d to a 12 hr LD regime under constant temperature
(25°C). Time courses were performed under either LD or DD conditions.
Samples were collected every 4 hr for 2 consecutive days and
immediately frozen in dry ice. Frozen flies were shaken and sieved to
generate two fractions: head (without antennas) and body (decapitated,
and without legs or wings). Heads and bodies were kept apart for
RNA/protein extraction. For the DD time courses, flies were entrained
to LD cycles during 5 d and then released into constant darkness;
samples were taken during the first and second day in DD.
RNA extraction and hybridization. Total RNA was prepared
from fly head or body homogenized in Trizol reagent (Invitrogen). RNA
was purified using RNeasy kit (Qiagen). Total RNA (7.5 µg) was
labeled for hybridization according to manufacturer's instructions (Affymetrix). For Northern analyses, 25 µg of total RNA was loaded into a 1.2% formaldehyde agarose gel and transferred to nylon (Hybond
N, Amersham). expressed sequence tags used as probes were obtained from ResGen (Invitrogen) and correspond to GH27053 (CT38753), LP 11415 (CT8171), AT29985 (CT18196), GH13172 (CT33647), and LD06553 (CT12127).
DNA chip experiments. cDNA synthesis, biotin labeling of
cRNA, and hybridization of the chips were performed as recommended by
the manufacturer (Affymetrix). Cel to text file condensation was
performed using the MAS 4 algorithm in the Microarray Suite v.4
(Affymetrix) and visualized using the GeneSpring (Silicon Genetics)
software package. To identify clock-controlled genes, we used a
statistical program, COSOPT, which we have described in detail
elsewhere (Harmer et al., 2000; Panda et al., 2002). Finally, we have
constructed a publicly accessible database
http://expression.gnf.org/circadian, where users can query for
circadian-regulated genes in the fly head and body.
Locomotor behavior analysis. Newly eclosed flies were
entrained to 12 hr LD cycles for 3 d, and adult males were placed
in glass tubes and monitored for activity with infrared detectors and a
computerized data collection system (Trikinetics) (Hamblen-Coyle et
al., 1992). Activity was monitored in LD conditions for 3-4 d, when
the flies were released into DD at least for 1 week. Data were analyzed
using the Clocklab software package (Actimetrics, Evanston, IL) as in
Yang and Sehgal (2001). Only those flies that were alive 2 d after
analysis were taken into account. Periodogram analysis of flies that
were scored as arrhythmic in Table 4 produced no strong peak that was
statistically significant (p < 0.05). Those
flies that showed recognizable daily onsets and offsets of activity
that was not consolidated, thus resulting in periodograms that
displayed weakly significant periods, were classified as weakly
rhythmic (Yang and Sehgal, 2001) in Table 4 and were not taken into
account for average period calculations. Overall activity was
calculated by averaging equivalent bins during LD or DD cycles for each
fly and then taking an average of all the flies within each genotype.
Western blots. Time courses were performed as described.
Approximately 10 µl of wild-type and mutant fly heads was loaded into
precast 4-15% acrylamide gels (Bio-Rad). Gels were transferred in
15% methanol Tris-glycine buffer onto Nitrobind-supported
nitrocellulose (Osmonics). Primary antibodies used were rabbit IgG
anti-SLO (1:500) and mouse monoclonal IgG anti-hsp70 (1:2000; Sigma).
Washes were performed in TBS (for SLO) or TBS 0.5% Tween (for hsp70).
Appropriate secondary antibodies were detected using ECF (Amersham Biosciences).
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RESULTS |
Clock-controlled transcription throughout the day
We monitored steady-state RNA levels using DNA GeneChips
(Affymetrix) spanning the entire Drosophila genome. Temporal
profiling of 13,500 probe sets was performed with a 4 hr time
resolution during 2 consecutive days under entrained and free-running
conditions. Each sample was hybridized to two DNA GeneChips to
test the reproducibility of the technique (Harmer et al., 2000). The
mean hybridization signal strength and the SEM for each probe set was
calculated from the duplicate hybridizations.
To identify the cyclic mRNAs among the pool of expressed genes, we used
COSOPT, an analytical algorithm that we developed for detection and
statistical characterization of rhythmic gene expression in gene array
experiments. Briefly, data were fit to 100,000 cosine test waves, and
the significance of each fit was then determined empirically by
temporally randomizing the data sets; those genes with traces that fit
a cosine wave with a period between 20 and 28 hr were scored as cycling
with a given probability (Panda et al., 2002). In the LD y w
head time course, COSOPT scored 120 genes as cycling with
p < 0.01 (Table 1). This
may be an underestimation of the total number of cycling genes because
previously characterized clock components such as per (which
cycles with p = 0.015) and Clk
(p = 0.036) fall outside of this category. We
chose a conservative p value for selecting clock-controlled genes for further study. Storch et al. (2002) used a similar criterion to minimize the overall number of genes in the array selected as
cycling while maximizing the number of "guide genes" (known ccgs)
present, which further validates our method. Internal validation of
this approach toward the identification of novel output genes came from
the observation that previously characterized cycling genes (such as
tim, vri, to, and even low amplitude
ones such as cry) displayed a circadian pattern of gene
expression in our array experiment (data not shown). We also detected
dreg-5 (Van Gelder et al., 1995) with some element of
low-amplitude cycling, although it was not identified as such by our
algorithm. Recently, an enhancer trap-based screen retrieved four
additional ccgs (Stempfl et al., 2002). Of those, COSOPT identified
CG2207, CG13432, and CG3779 as cycling in the fly head, although none
of them reached the significance level of p < 0.01. Interestingly, CG6145 appears to cycle robustly in the fly body (see
below and Table 3).
To address type 1 and type 2 errors in our analysis, 1000 Gaussian
random temporal patterns of 13 time points (analogous to 48 hr of 4 hr
sampling) with no rhythmic component were analyzed with COSOPT using
the same criteria applied to our experimental data
(p < 0.01; no multiple-measurement correction).
COSOPT identified two cycling curves when it should have detected none,
indicating that the false positive rate under these conditions is
0.2%. If we applied this percentage to the 14,000 genes assessed in
the experiment, we would have 28 false positives. If it is more
appropriately applied to only the ~5000 genes that are detectable,
the number of false positives becomes 10. Determining the rate of false
negatives is a more complex process because it imposes a
priori assumptions on the quality of the data. We ran these
simulations under two hypothetical conditions: a worse case scenario in
which the signal/noise ratio is 1 (signal and noise are equal in
magnitude), and a more likely scenario in which this ratio equals 2. When we analyzed 1000 cosine waves of amplitude equal to 1 SD above the
noise level, COSOPT detected 445 (implying that the false negative rate
under these conditions is 55%); interestingly, when the amplitude
equals 2 SDs above the noise (still a conservative assumption), the
false negative rate becomes negligible. COSOPT identified 997 of 1000 (that is, 0.3% error rate) of the test cosine waves, providing an
exceptionally high degree of confidence that our approach is valid
(data not shown).
Cycling genes were grouped in clusters according to the phase of peak
expression. The phase distribution is shown in Figure 1A. This approach
allowed the identification of several novel phases of peak expression
[Zeitgeber time 4 (ZT4), ZT8, ZT12] and, more importantly,
made possible a direct comparison between the different genes within
the same experiment. All phases were similarly represented with the
exception of ZT8, and to a lesser extent ZT20, where a larger
proportion of the genes reach maximum levels of expression. This is
similar to what has been reported by Ueda et al. (2002) and contrasts
with the findings of Claridge-Chang et al. (2001), although this could
be attributable partially to differences in the cluster analysis that
was performed.
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Figure 1.
A, Output genes peak throughout the
day in the fly head and body. Genes were grouped according to
the phase calculated by COSOPT; each cluster represents genes peaking
at the specified time ± 2 hr. ZT0 refers to the
time when lights were switched on. Cluster size is represented as
percentage of the total number of cycling genes. B, The
clock controls different subsets of genes in head and body fractions.
Distribution of genes cycling in both tissues, cycling and expressed in
only one, and expressed to mid-high levels in both but cycling in one
is displayed.
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One of the hallmarks of clock-controlled activity is the persistence of
the rhythms in the absence of environmental cues. To confirm the
circadian nature of the newly identified target genes, we performed an
independent experiment under free-running conditions. We tested which
proportion of the novel outputs showed reliable cycling profiles under
constant darkness. One of the limitations of this analysis in DD is the
"dampening" of the amplitude of the oscillation, which could
partially result from the desynchronization of individual cells in the
absence of resetting environmental cues, or it could reflect a direct
effect of light boosting the amplitude of the oscillations (Hardin,
1994). Approximately 50% of the genes identified in the LD experiment
were found to cycle robustly in DD as well (Table
2). Interestingly, the phase predicted by
COSOPT for each individual cycling gene is very similar in LD and
DD.
To examine the cycling profiles of the newly identified output genes in
a behaviorally arrhythmic background, we entrained y
w;;Clkjrk and performed a
time course with a 4 hr time resolution during 2 consecutive days in
LD; this resolution was chosen to properly compare genes peaking at
different phases in the wild-type condition with their counterparts in
the mutant background. We found that 116 of the 120 identified output
genes ceased to cycle in this mutant background, commensurate with the
role that the CLOCK protein plays in sustaining the molecular
oscillations. The remaining four genes were scored by COSOPT as cycling
with p < 0.01. Closer inspection of the individual
traces highlighted the direct effect of light and light/dark
transitions on gene expression: three of the mRNAs appeared to slowly
build up during the day and decrease during the night, whereas others
appeared to be acutely driven by light and decreased thereafter (data
not shown). To determine whether the levels of expression changed in
the mutant background, we calculated the arithmetic average difference
(subtracting the average level of expression in y
w;;Clkjrk from that in wild type) and
corresponding SD. By this criteria we found that 5 genes were
downregulated and 11 were upregulated (p < 0.025) in the y w;;Clkjrk
mutant background (as indicated in the last column of Table 2).
The clock regulates gene expression in a
tissue-specific manner
To provide a more comprehensive view of clock control in a whole
organism, we analyzed cyclic gene expression under entrained conditions
using male bodies as the source of total RNA and compared these
transcripts with those derived from heads. COSOPT identified 177 genes
that cycle within a period between 20 and 28 hr with p < 0.01 (Table 1); the data are summarized in Figure 1, A
and B, and Table 3. As in the
mouse (Panda et al., 2002), only a small proportion of cycling
transcripts was found to overlap between the two tissues (12 genes),
including some previously identified clock components (such as
tim, vri, and cry). The analysis of the remaining genes revealed an even more interesting feature: a large
number of genes that cycle solely in fly heads are still expressed at
medium to high levels in bodies, pointing to differential transcriptional regulation in this subset of clock outputs (Fig. 1B). This finding is also in agreement with what has
been found in the mouse (Panda et al., 2002; Storch et al., 2002).
To independently confirm the cyclic pattern of a subset of output
genes, we performed Northern blot analysis on separate time courses. We
chose genes spanning a range of p values; all were found to
oscillate with a phase concordant to that predicted by the microarray
experiments (Fig. 2).
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Figure 2.
Northern blot analysis of several clock-controlled
genes. Northern blot analysis of independent head and body time courses
confirms both the cyclic nature of the candidates tested and their
respective phases of expression. Gene ID refers to the
cDNA used as probes. The right columns indicate the
expected peak time and the corresponding p value
predicted by the array experiment, respectively. The
asterisk indicates a gene that is outside of the
p < 0.01 cutoff and still appears rhythmic.
According to our experiments, the CT18196 transcript has smaller size
than predicted.
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Circadian transcriptional regulation of physiology
Protein stability
Although cyclic transcription is a signature of clock activity,
important levels of regulation take place downstream. Several genes
involved in various aspects of protein stability were under rhythmic
transcriptional control. We identified three genes cycling in the fly
head with a peak at ZT8 that are part of the 26S proteasome complex
(Fig. 3A). pros26
encodes a multicatalytic endopeptidase (Saville and Belote, 1993).
pros26.4 and rpn9 are part of the 19S regulatory
complex and correspond to subunit 4 of the AAA-ATPase and are required
for assembly and stability of the proteasome. In Drosophila,
different proteasome subunits are expressed throughout development,
possibly to control specific processes such as cell division or
morphogenesis (Haass and Kloetzel, 1989). We also identified a putative
de-ubiquitinating enzyme, the homolog of the mouse Usp8, with peak
expression at ZT12 (Fig. 3A). The cyclic pattern of
CG5798 was confirmed independently by Northern blot analysis (Fig. 2).
De-ubiquitinating enzymes remove the polyubiquitin chain from
conjugated proteins before their degradation by the proteasome. These
enzymes either may regulate degradation by the proteasome or may be
involved in ubiquitination processes regulating subcellular
localization. In summary, these data suggest that temporal regulation
of the proteosome may be important in Drosophila physiology.
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Figure 3.
Cyclic patterns of clock-controlled genes in the
fly head and body under entrained conditions. Complementary RNA samples
were prepared as described and hybridized to duplicate DNA GeneChips.
Data were normalized such that the mean expression level for each
particular gene over the course of all time points equals 1. The
average signal strength at each time point was then expressed as a
ratio over the median signal strength for that particular gene.
Representative traces of cycling transcripts implicated in various
physiologies and metabolic pathways are shown in
A-F. A, Clock control of
protein stability. pros26 (light blue),
pros26.4 (green),
rpn9 (purple), and ubiquitin
thiolesterase (red). B, Heme metabolism.
The gene encoding alas is in red;
heme-oxygenase is in light blue. C, Genes
implicated in detoxification are under circadian regulation. Phase I
cytochrome P450s are colored as follows: cyp4e3
(green), cyp6a2 (light
blue), cyp6a17 (brown),
cyp6a21 (red), cyp6d5
(dark blue), and cyp18
(pink). D, Phase II genes:
ugt35b in red, GST3 in
purple, and two uncharacterized GSTs (CT38753 and
CT38747) in light blue and green,
respectively. (E) Neurotransmission.
ple expression under entrained and free-running
conditions is depicted in red and blue,
respectively. F, Immunity. A number of genes involved in
different aspects of innate immunity are under circadian control and
are indicated as follows: in recognition and phagocytosis: Agr5
(light brown), a peptidoglycan protein (light
green), lectin galC (aqua), Idgf4
(brown), CT29102 (deep blue);
antimicrobial peptides: lysX (red) and CT 30310 (pink); Chitinase-like molecules: Chit
(light blue) and CT5624 (dark green).
Relative intensities ± SEM at each time point are shown.
White and black boxes on the
abscissa represent the duration of the light and dark
periods. Hatched boxes (E)
indicate subjective day.
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Heme metabolism
The observation that the expression of both the rate-limiting
enzyme in heme biosynthesis,  -aminolevulinate synthase,
alas-, and the rate-limiting enzyme of heme degradation,
heme oxygenase, cycle in Drosophila heads suggests that heme
metabolism is tightly regulated by the clock (with a peak at ZT20 and
ZT8, respectively) (Fig. 3B). Heme, or heme-containing
proteins, is involved in respiration, oxygen transport, detoxification,
and signal transduction processes, but as a chelator of iron it may
promote deleterious cellular effects such as oxidative membrane damage.
Thus, maintaining a proper balance between heme biosynthetic and
degradative pathways is crucial for cellular homeostasis (Ryter and
Tyrrell, 2000). Although alas has been found to cycle in the
mouse liver, heme oxygenase has not been observed to cycle (Kornmann et
al., 2001).
Detoxification/olfaction
Heme is also required for P450 function. In insects, P450 enzymes
are thought to be involved in the biosynthetic pathways of ecdysteroids
and juvenile hormones, and as such play a role in insect growth,
development, and reproduction as well as in the metabolism of natural
plant products and insecticides, resulting in bioactivation or
detoxification (Feyereisen, 1999). This biotransformation process has
been described as occurring in two phases. The initial compound can be
transformed into a more reactive species (usually via redox reactions
catalyzed by cytochrome P450s), whereas in the second phase, highly
polar groups such as UDP-glucuronosyl or glutathione are added either
to the products of the first phase or in some cases directly to the
toxic chemicals. The enzymes involved in this second-stage phase are
UDP-glucuronosyl transferases (UGTs) and gluthathione
S-transferases (GSTs). Products of phase II are highly
hydrophilic, can no longer cross membranes, and are eliminated by
secretion (Wang et al., 1999). We found that six different cytochrome
P450 genes, cyp4e3, cyp6a2, cyp6a17, cyp6a21, cyp6d5, and cyp18, cycle with
different phases in heads (ZT8, 4, 0, 20, 20, and 16, respectively)
(Fig. 3C) and cyp6g1, cyp9b2, and
cyp18a1 cycle with different phases in the body
(Table 3). The only functionally characterized enzyme thus far is
cyp6a2, which is involved in the metabolism of
organophosphorus insecticides. Interestingly, we also found that
ugt35b and several GSTs also cycle in fly heads ("phase
II" enzymes) (Fig. 3D), implying that multiple steps in
the biotransformation process appear to be under circadian control.
In Drosophila, ugt35b is preferentially expressed
in the third antennal segment where most of the olfactory sensilla are
found (Wang et al., 1999), although it can be found in the fly head as
well. Given that olfaction is known to be under circadian control (Krishnan et al., 1999) and olfactory UGTs have been found to play a role in odorant signal termination (Lazard et al., 1991), we can
speculate that ugt35b could potentially be a link between them.
Neurotransmission
Another gene that appears to be under circadian control is
ple, implicating neurotransmission as a clock-controlled
process. ple codes for the tyrosine 3-monooxygenase (also
known as tyrosine hydroxylase), the first and rate-limiting enzyme in
dopamine biosynthesis. Dopamine is an intermediate in cuticular
sclerotization and also functions as a neurotransmitter in the fly
nervous system. It has been shown to modulate certain forms of learning
(such as female sexual receptivity and habituation), as well as motor
neuron activity and neuromuscular function in larva (Neckameyer, 1998; Cooper and Neckameyer, 1999) and to exert circadian control over reflexive locomotion in decapitated flies as well (Andretic and Hirsh,
2000). We found that ple cycles with high amplitude, peaking at ZT4 under both entrained and free-running conditions (Fig. 3E). ple expression falls below the level of
detection in y
w;;Clkjrk flies (a
mutation that impairs clock function) (Allada et al., 1998).
ple represents one of the several examples of homologous genes cycling in both flies and mammals (Panda et al., 2002), further
stressing the significance of our observation. The oscillation of
ple expression is unlikely to relate to its role in the head cuticle because the expression is undetectable in the body. Rather, the
dramatic oscillation may contribute to circadian regulation of behavior.
Immunity
Drosophila is able to mount a rapid immune reaction in
response to the infection by a pathogen. In fact, two distinct pathways are triggered depending on the nature of the pathogen involved, namely
Gram-negative bacteria or fungi (Khush et al., 2001). We found a number
of genes that play a role in different aspects of the immune response
that cycle throughout the day in the fly body (Fig. 3F,
Table 3). These genes are involved in microbial recognition and
phagocytosis (such as PGR-SC1b, ldgf4, Tep4, Agr5, and
lectin-GalC), or they encode antimicrobial peptides (such as lysX and
CT30310); others have unknown function but have been found to be
induced specifically by infection (Phas1) (De Gregorio et al.,
2001).
A potassium channel is an output of the clock involved in sustained
rhythmic behavior
The availability of a more complete description of
clock-controlled genes enabled the selection of several candidates for the control of locomotor behavior. One of these candidates was slowpoke
binding protein (slob), which binds to the
Ca2+-dependent voltage-gated potassium
channel slowpoke (slo) (Schopperle et al., 1998).
A mutation in this channel causes behavioral defects (Atkinson et al.,
2000) and an altered mating song, also a hallmark of certain clock
components (Peixoto and Hall, 1998). slowpoke participates
in the repolarization of the action potential in flight muscles and in
motoneurons (Elkins et al., 1986; Gho and Mallart, 1986). SLOB has been
shown to modulate SLO activity per se, and through the formation of a
complex with the  isoform of 14-3-3 protein that acts downstream in
several signaling pathways (Zhou et al., 1999).
We found that slob mRNA cycled robustly in fly heads in LD
and DD (Fig.
4A,B,
Table 2), consistent with recent reports (Claridge-Chang et al., 2001;
McDonald and Rosbash, 2001; Ueda et al., 2002). This pattern was
lost in the y
w;;Clkjrk mutant
background. Although slo was not detected as cycling by COSOPT because of its low level of expression, we noticed that it
appeared to cycle in phase with slob in both LD and DD (Fig. 4A,B). The cycling of
slo was investigated by RT-PCR analysis (data not shown),
and the protein was shown to cycle and peak at ZT20 by Western blot
(Fig. 4C). The slo spatial expression pattern has
been studied extensively (Becker et al., 1995) and has revealed that
the slo mRNA is widely expressed in the adult brain.
Furthermore, SLO protein has been localized both to neuronal cell
bodies as well as to the neuronal projections.
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Figure 4.
slo and slob cycle
in LD and DD in wild-type flies. Shown is circadian pattern of
expression of slob (gray) and
slo (black) under entrained
(A) and free-running (B)
conditions. Relative intensities ± SEM at each time point are
shown. C, Representative example of a Western blot to
detect the SLO protein under LD conditions. Flies were entrained and
collected as described. The first and last
lanes correspond to protein extracts collected at ZT16 from
slo4 and tim0 flies,
respectively. The graph indicates the quantification of
SLO levels throughout the day (normalized to HSP-70).
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Prompted by the speculation that SLO might be involved in circadian
control of activity, we examined the locomotor activity in two
slo mutants, slo I and slo 4. Wild-type flies show increased locomotor activity near dawn and dusk
and remain quiescent the rest of the day (Fig.
5) (Hamblen-Coyle et al., 1992). These
bursts of activity do not merely follow the next temporal transition, but instead anticipate it. Figure 5 shows individual representative actograms of wild-type and mutant flies. Wild-type Canton S (CS) and y w are rhythmic in LD and in DD, where the endogenous
period becomes apparent. pero
and Clkjrk mutants, which have
defects in core clock components, behave differently under entrained
conditions. Although pero flies
still look mostly rhythmic in LD,
Clkjrk is often not (Fig. 5)
(Wheeler et al., 1993; Allada et al., 1998). This apparent rhythmicity
in pero flies is caused by the
so-called "startle effect," an immediate behavioral response to the
light/dark transitions. We found that most of the slo 4 mutant flies display weak rhythms (defined as lacking a consolidated
peak in the periodogram analysis) or no rhythms at all in LD. As
expected, the lack of rhythmicity persisted under free-running
conditions (Fig. 5, Table 4).
Surprisingly, this arrythmicity is comparable to, if not worse than,
the one displayed by Clkjrk.
slo I mutants, on the other hand, displayed a milder
phenotype, with only 40-55% of rhythmic flies in LD and DD,
respectively, which is commensurate with a hypomorphic slo
mutation (as opposed to a true null, as is the case for slo
4). Given the nature of the slo 4 mutation and the
difference in the strength of the phenotype observed between
slo I and slo 4 mutants, we tested slo
4/slo I trans-heterozygotes to rule out the
possibility that other loci (also affected by the chromosomal
inversion) could be contributing to the observed phenotype. We found
that the slo 4/slo I mutants show a somewhat
intermediate phenotype (especially obvious in DD) between that of
slo 4 and slo I (Table 4). We also tested a small
number of slo4 heterozygotes (slo 4/+) and found
that most are either strongly or weakly rhythmic; no arrhythmic flies were found (data not shown). This argues against an effect exerted by
the other putative loci.
View larger version (36K):
[in this window]
[in a new window]
|
Figure 5.
slowpoke is required for sustained
rhythmic behavior. Representative actograms of wild-type CS and
y w flies (left) and the mutants
per0,
Clkjrk, and arrhythmic
slo I and slo 4 flies are displayed.
Flies were entrained for 5 d before the onset of the experiment.
During the experiments, flies were kept in LD for 3-4 d and then
switched to DD and monitored for at least another week. Rhythmicity and
total activity in LD and DD conditions were determined using the
Clocklab software package.
|
|
To determine whether this mutation caused a general decrease in
motility, which by itself could result in arrhythmicity, we quantified
the total locomotor activity displayed by the different genotypes under
LD and DD conditions. Although wild-type flies appear to be slightly
more active under constant darkness, both slo mutants are
impervious to the lighting regimen. More importantly, the overall
levels of activity are not different from those of the wild-type flies
(Table 4). We superimposed the actograms of wild-type, slo
4, and slo I mutant flies because the average activity plots
are known to reveal features not apparent when individual flies are
inspected. This analysis revealed that the most striking difference is
the impaired anticipation of the transitions in the
slo 4 (null) mutant flies, indicating that the temporal gating that consolidates behavior around dawn and dusk is absent in
flies lacking slo function (Fig.
6).
View larger version (24K):
[in this window]
[in a new window]
|
Figure 6.
Mutations in slowpoke disrupt the
consolidation of activity around the transitions. Average activity
plots for CS, slo 4, and slo I mutant
flies are shown. Activity records of the LD portion of the experiment
for 53 wild-type, 28 slo 4, and 56 slo I
flies were used for the analysis. To superimpose the separate animal
records, the levels of activity were normalized per fly per day. Each
vertical bar represents the mean ± SD.
|
|
| |
DISCUSSION |
Although a description of the core circadian oscillator exists in
flies and mammals, a connection between the molecular basis of
rhythmicity and physiology under clock control has been lacking. Because many clock mutants are transcription factors, a central approach in linking core mechanism to physiology is to examine mRNAs
for rhythmic expression. To accomplish this on a whole genome scale, we
and others have examined steady-state mRNA levels using high-density
oligonucleotide arrays for circadian patterns of expression in the fly
head and body. This analysis has identified several genes known to be
rhythmically expressed, as well as several hundred genes of known and
unknown function that are likewise under clock control. We found that a
number of aspects of fly physiology ranging from basic cellular
metabolism to neurotransmission, stress resistance, and detoxification
appear to be under the control of the biological clock. Importantly, as
in the mouse, few genes cycled in both heads and bodies, suggesting
that tissue specificity is an important component of circadian
transcriptional regulation. Furthermore, several cycling genes in the
fly were also cycling in the mouse, suggesting that these were
conserved output mediators, or perhaps even core clock components.
Recently, three groups reported the use of a similar strategy to
identify output genes in Drosophila heads (Claridge-Chang et
al., 2001; McDonald and Rosbash, 2001; Ueda et al., 2002). Each study
identified >100 transcripts that cycle within a 24 hr period in the
fly head, and the expression of these transcripts was affected in
arrhythmic backgrounds. Similarly to our work, Claridge-Chang et al.
(2001) confirmed a subset of the cycling genes by performing Northern
blots on independent time courses. Instead, Ueda et al. (2002) used
quantitative RT-PCR to confirm a number of cyclers. A direct comparison
of the four datasets yields a surprising result: the overlap among
lists represents a small fraction of the total number of cycling genes
(14, among those the previously characterized clock components). Is
this a reflection of differences in the experimental approaches,
genotypes used, or variability of the techniques used? The four
experiments have two major aspects in common: the use of total RNA
prepared from fly heads and Affymetrix Genechips, although different
genotypes have been used (y w, CS, cn bw,
and w). Jackson and Schroeder (2001) proposed that the use
of different methods to identify the cycling genes is responsible for
the variability observed. We favor that hypothesis, coupled with the
subtly different experimental designs used in each case [we performed
independent analysis on 48-hr-long LD and DD time courses as did Ueda
et al. (2002); meanwhile Claridge-Chang et al. (2001) concatenated
three independent time courses, each representing the last day of
entrainment and the first in free-running conditions (LD + DD), and
McDonald and Rosbash (2001) only looked at a single day in DD)].
In that regard, it appears particularly informative to contrast our
observations with those of Ueda et al. (2002) given the similarity in
experimental approaches. If we grouped the cycling genes identified by
COSOPT (with p < 0.01) in the LD and DD fly head
experiments as in Ueda et al. (2002), class I (LD and DD), class II
(only LD), and class III (only DD) subsets would contain 24, 96, and
321 genes, respectively. The overlap between the two datasets is high,
ranging from ~40% in class I to 20% in class II genes. Conversely,
the overlap between the genes that cycle exclusively in DD is minimal,
which might allude to problems in the detection of low-amplitude
cyclers or the dampening of the signal, which is characteristic of gene
expression under free-running conditions. Nevertheless, the ultimate
comparison that will address the limits of the experimental approach is
to use a single algorithm to analyze all datasets. As in Ueda et al.
(2002), we found a large proportion of cycling genes in DD that were
not identified as such in the LD experiment. A direct effect of light
on gene expression (by masking or suppression) could account for such an observation.
The important question to ask then is the following: how many of these
transcripts are truly cycling? The answer can be derived only from
independent observations. In that sense it is encouraging to find that
~67 of the cycling genes were identified in at least two datasets,
which together with those that have been shown to cycle by independent
means brings the number of confirmed cyclers to >80 in the fly head.
Our results have also shown that looking into different tissues will
help address the issue of which proportion of the genome is under clock
control, because there is very minimal overlap between the ones that
cycle in the head and the body, as is the case in mammals (Panda et
al., 2002; Storch et al., 2002).
Microarray experiments are extremely powerful in their
scope and should be taken as a starting point to delve into the
specifics of different aspects of physiology that appear to be under
control of the clock. We identified several genes potentially linked to behavior. Follow-up of one of them, slo, implicates it as a
central regulator of locomotor activity, because a null mutation
(slo 4) in this locus results in behavioral arrhymicity
without a major change in total activity levels. Several scenarios
could account for these observations. A mutation in slo
could cause arrhythmicity if it directly affects the output pathway
controlling behavior by affecting the excitability of the neurons that
control it, although if such were the case we would expect hyperkinetic
or hypokinetic flies. Alternatively, the mutation could act at the level of the pacemaker neurons by reducing the synchronous firing between the lateral neurons, which would also cause the observed lack
of behavioral rhythmicity. slowpoke could also be
"gating" (McWatters et al., 2000) fly locomotor activity that would
be regulated by additional unidentified components. The observation that slo 4 mutants lack the consolidation of behavior around
dawn and dusk clearly favors this hypothesis, although additional work will be required to rule out other plausible scenarios, such as its
involvement in the light input pathway that conveys environmental information to the clock or the core oscillator itself.
The notion that a potassium channel is involved in the generation of
rhythmic activity was proposed a number of years ago after the analysis
of membrane conductance changes in isolated retinal neurons of the
mollusk Bulla (McMahon and Block, 1987; Michel et al., 1993). This
observation, together with the finding that potassium currents are
under circadian regulation in the mouse (D. McMahon, unpublished
observations) and that expression of Kcnma1, the slowpoke
mouse ortholog, cycles (Panda et al., 2002), strongly suggests
that this mechanism of control of rhythmic activity could play a role
in more complex organisms as well.
| |
FOOTNOTES |
Received Feb. 14, 2002; revised Aug. 2, 2002; accepted Aug. 16, 2002.
This work was supported by National Institutes of Health Grant MH51573
to S.A.K. We thank Karen Wager-Smith, Alejandro Schinder, and Jeff Hall
for invaluable discussions; Stacey Harmer and Trey Sato for helpful
comments on this manuscript; Camilo Orozco for assistance with data
analysis; Andrew Su for his assistance with data visualization; Nigel
Atkinson (University of Texas, Austin, TX) for providing
slo 4 mutant flies; and the Bloomington Stock Center for
the slo I flies. We are also grateful to Dr. I Levitan (University of Pennsylvania, Philadelphia, PA) for the
anti-slo antibodies. We also thank Doug McMahon for
sharing unpublished observations and Hiroki Ueda for sharing raw data.
Correspondence should be addressed to Steve A. Kay, The Scripps
Research Institute, ICND-216, 10550 North Torrey Pines Road, La Jolla,
CA, 92037. E-mail: stevek{at}scripps.edu.
M. Fernanda Ceriani's present address: Instituto de Investigaciones
Bioquímicas, Fundación Instituto Leloir, Avenida
Patricias Argentinas 435, 1405 Buenos Aires, Argentina.
| |
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TOP
ABSTRACT
INTRODUCTION
