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The Journal of Neuroscience, January 15, 2000, 20(2):617-625
The Transcription Factor DBP Affects Circadian Sleep
Consolidation and Rhythmic EEG Activity
Paul
Franken1,
Luis
Lopez-Molina2,
Lysiane
Marcacci2,
Ueli
Schibler2, and
Mehdi
Tafti1
1 Biochemistry and Neurophysiology Unit, Department of
Psychiatry, University of Geneva, CH-1225 Chêne-Bourg,
Switzerland, and 2 Department of Molecular Biology,
Sciences II, University of Geneva, CH-1211 Geneva, Switzerland
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ABSTRACT |
Albumin D-binding protein (DBP) is a PAR leucine zipper
transcription factor that is expressed according to a robust circadian rhythm in the suprachiasmatic nuclei, harboring the circadian master
clock, and in most peripheral tissues. Mice lacking DBP display a
shorter circadian period in locomotor activity and are less active.
Thus, although DBP is not essential for circadian rhythm generation, it
does modulate important clock outputs. We studied the role of DBP in
the circadian and homeostatic aspects of sleep regulation by comparing
DBP deficient mice (dbp /) with their isogenic
controls (dbp+/+) under light-dark (LD) and
constant-dark (DD) baseline conditions, as well as after sleep loss.
Whereas total sleep duration was similar in both genotypes, the
amplitude of the circadian modulation of sleep time, as well as the
consolidation of sleep episodes, was reduced in dbp/
under both LD and DD conditions. Quantitative EEG analysis demonstrated
a marked reduction in the amplitude of the sleep-wake-dependent
changes in slow-wave sleep delta power and an increase in hippocampal
theta peak frequency in dbp/ mice. The sleep
deprivation-induced compensatory rebound of EEG delta power was similar
in both genotypes. In contrast, the rebound in paradoxical sleep was
significant in dbp+/+ mice only. It is concluded that
the transcriptional regulatory protein DBP modulates circadian and
homeostatic aspects of sleep regulation.
Key words:
sleep homeostasis; non-REM sleep intensity; knock-out
mice; clock-genes; EEG and circadian oscillations; simulation
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INTRODUCTION |
The cells of the suprachiasmatic
nuclei (SCN) constitute the circadian "clock" that drives the daily
fluctuations in many behaviors and physiological parameters and keeps
them entrained to the 24 hr light/dark cycle (Miller et al., 1996 ).
Recently, enormous progress has been made in the understanding of the
molecular basis of circadian rhythm generation in mice. Thus, several
clock elements have been identified (for review, see Dunlap, 1999) of which CLOCK, mPER2, mCRY1, and mCRY2 have been shown to be essential for rhythm generation in mice (Antoch et al., 1997; van der Horst et
al., 1999; Zheng et al., 1999). The mechanisms by which the molecular
signals of the pacemaker are translated into overt rhythms, however,
are less well studied. In Drosophila, several genes
mediating clock output have been proposed (Hall, 1995) and, in the
mouse, the CLOCK:BMAL1 heterodimer can activate vasopressin
transcription (Jin et al., 1999). Another recent example of a murine
circadian output gene is the transcription factor albumin D-binding
protein (DBP) (Lopez-Molina et al., 1997). In addition to the presence of a strong circadian rhythmicity in both DBP protein and mRNA in a
variety of peripheral tissues (Falvey et al., 1995; Fonjallaz et al.,
1996), DBP mRNA also displays a strong circadian rhythm in the SCN, and
the lack of DBP results in a shortening of the period of the circadian
free-running rhythm and in a reduction of locomotor activity
(Lopez-Molina et al., 1997).
The distribution and consolidation of sleep are directly controlled by
the SCN (Dijk and Czeisler, 1995), and lesioning this structure
abolishes the circadian sleep-wake rhythm, particularly the
consolidation of sleep (Edgar et al., 1993). Of equal importance is the
homeostatic control of sleep that, in concert with the circadian
process, regulates the expression of sleep (Borbély, 1982). The
homeostatic process reflects the need or propensity for sleep, which
builds up during wakefulness and dissipates during sleep. For slow-wave
sleep (SWS), EEG delta power is considered a marker of this process
(Daan et al., 1984) because it exhibits a predictive quantitative
relationship with the duration of previous wakefulness (Tobler and
Borbély, 1986; Dijk et al., 1987). For paradoxical sleep (PS), it
is the duration that is homeostatically regulated because a loss of PS
is primarily compensated by an increase in time spent in PS (Kitahama
and Valatx, 1980; Parmeggiani et al., 1980; Franken et al., 1991a,
1993, 1999; Amici et al., 1998). Certain aspects of the
regulation of sleep and the sleep EEG have a strong genetic component
(Beijsterveldt and Boomsma, 1994; Franken et al., 1998, 1999; Tafti et
al., 1999). More specifically, the regulation of PS in mice is
determined by a few genes only (Valatx and Bugat, 1974; Kitahama and
Valatx, 1980). Recently, we confirmed these findings by providing the
first quantitative trait loci (QTL) for PS (Tafti et al., 1997), which
corresponds to a genomic region that contains important candidate
genes, including dbp.
Given the circadian expression of dbp in the SCN, its effect
on circadian period, and the association of this gene with the above
mentioned QTL, we assessed the effects of DBP on the expression and
regulation of sleep under several conditions. We compared sleep and the
EEG of mice lacking DBP with their wild-type controls under light-dark
and constant-dark (DD) conditions, and after a 6 hr sleep deprivation
(SD). Because both strains were otherwise isogenic, differences
in sleep can be attributed to the presence or absence of DBP alone and
not to differences in genetic background (Gerlai, 1996).
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MATERIALS AND METHODS |
The generation of 129/Ola mice carrying a null allele for the
dbp gene (Lopez-Molina et al., 1997) and the methods
concerning the recording and analysis of the EEG in mice (Franken et
al., 1998, 1999) have been described in detail previously. The
experimental protocols were approved by the local veterinary office
(Office Vétérinaire Cantonal de Genève) and the
ethical committee of the University of Geneva.
Eight male 129/Ola mice from a sixth generation intercross of progeny
homozygous for the null allele (dbp/) were included in
the present experiment. Eight male 129/Ola wild-type mice constituted their isogenic controls (dbp+/+). Genotypes did not differ
in weight or age (dbp+/+ vs dbp/; age,
134 ± 8 vs 139 ± 5 d; p > 0.6;
weight, 29 ± 1 vs 31 ± 2 gm; p > 0.3;
t tests), and no differences were noted in gross morphology
or in brain histology. All mice were individually housed in an
experimental room under a 12 hr light/dark cycle. EEG and
electromyogram (EMG) electrodes were implanted under pentobarbital
anesthesia. Mice were allowed 10-14 d of recovery from surgery and
habituation before the experiments.
In a first experiment, EEG and EMG signals were recorded continuously
for four consecutive 24 hr periods, starting at lights-on (8:00
A.M.). Days 1 and 2 were considered baseline (BSL1 and BSL2). On
day 3, starting at lights-on, mice were sleep-deprived for 6 hr by
gentle handling (Franken et al., 1991a, 1999). The remaining 18 hr of
days 3 and 4 were considered recovery (REC1 and REC2). The effect of SD
on sleep and the sleep EEG was assessed by comparing the first 6 hr of
REC1 with the first 6 hr of BSL2. The remaining three 12 hr periods of
recovery were compared with the corresponding 12 hr periods of BSL2. In
a second experiment, the contribution of light on the genotype-specific
differences observed in the first experiment was studied. Four mice of
each genotype were recorded for 88 hr or 3.7 d, starting 2 hr
before lights-on. Ten days passed between the SD in the first
experiment and the first day of the second experiment. Day 1, under the
usual light/dark cycle, served as BSL. The remaining 2.6 d
(DD1-DD3) were recorded under DD conditions. The protocols of the two
experiments are illustrated in Figures 1 and 3.
The analyses presented here are based on 2240 hr of EEG and EMG
recording. Both signals were amplified, filtered, and analog-to-digital converted. The EEG signal was subjected to a Fast-Fourier Transform yielding power spectra between 0.125 and 25.125 Hz, with a 0.25 Hz
frequency resolution per window of 4 sec. The behavior in each of these
2,016,000 4-sec epochs was classified as PS, SWS, or wakefulness (W) by
visual inspection of the EEG and EMG signals. The amount and
distribution of the behavioral states were analyzed by expressing them
as a percentage of recording time. As an amplitude measure of the
changes in sleep and wakefulness across the 24 hr day, the difference
between the mean amount of sleep in the (subjective) light and dark
periods was taken. To further evaluate the distribution of sleep over
the day (i.e., circadian sleep consolidation), episodes in which both
SWS and PS prevailed (i.e., "rest" episodes) were identified
according to previously published criteria (Franken et al., 1999). In
short, PS and SWS time were determined over 2 hr intervals that were
offset by 15 min to produce a running average. Both variables were then
expressed as a fraction of their mean amount in baseline (BSL1 and
BSL2), and the two fractions were averaged, yielding one value per 15 min to which both PS and SWS contributed equally. Fifteen minute
intervals in which this combined value was >1 were counted as
rest. Consolidation of sleep was also assessed at the level of
individual sleep episodes. The distribution of SWS and PS episode
duration was analyzed according to previously published criteria
(Tobler et al., 1997; Franken et al., 1999). According to their length,
episodes were allotted to one of nine bins of logarithmically
increasing size (4, 8-12, 16-28, 32-60, 64-124, 128-252, 256-508,
512-1020, and >1024 sec). The frequency in each bin was corrected for
the total amount of each state by expressing it per hour of SWS or PS.
Mean EEG spectra were obtained by averaging the spectra of all 4 sec
epochs scored as either PS or SWS in baseline (BSL1 and BSL2). The EEG
spectrum during exploratory behavior was analyzed during the first 5 min after a cage change, 3 hr after lights-on on day 3, i.e., the
middle of SD. During this period, all animals were engaged in
exploratory behavior. EEG peak frequency was determined by calculating
the distribution of the frequencies in which EEG power was highest
within each of the 4 sec epochs selected for that state. The prevailing
frequency was determined by fitting a normal distribution to the
frequency distribution (Franken et al., 1998).
As a marker for SWS propensity, EEG delta power was calculated as the
mean power over the frequency range between 1 and 4 Hz (Borbély,
1982; Daan et al., 1984). All SWS delta power measures were first
expressed as a percentage of the individual mean delta power over the
last 900 SWS epochs in the baseline light period to correct for
individual differences in the absolute power. Previous observations in
mice demonstrated that delta power steeply declines in the presence of
SWS (Franken et al., 1999). Therefore, to obtain a better estimate of
its initial level, delta power was averaged over the initial 15 min of
SWS after SD and after light onset in baseline. In addition, for the 2 baseline days of the first experiment and the 3 d of the second
experiment, daily peak and trough values were determined for SWS delta
power by selecting the maximum and minimum delta power values
calculated over 15 min or 225 consecutive (but not necessarily
uninterrupted) 4 sec epochs scored as SWS, with a lag of 5 min (or 75 4-sec epoch scored as SWS). The peak trough difference was taken as the
amplitude of the changes in SWS delta power across the 24 hr day.
All main effects of factors "genotype" (dbp/ vs
dbp+/+), "day" (baseline vs recovery or DD),
"time-of-day" (1, 2, 6, or 12 hr values), and "bin" (bins 1-9
of episode duration distribution) were analyzed by ANOVA for
repeated measures within genotype. Only those factors and interactions
between factors are listed that reached significance levels
(p < 0.05). When main effects were present,
post hoc, two-sided t tests were performed to
further evaluate differences between genotypes and paired t
tests for differences within genotype. When more then two levels per
factor were compared, Tukey's studentized range tests were performed to control the experimentwise error rate.
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RESULTS |
Sleep consolidation
Although the daily amounts of sleep and wakefulness did not differ
between dbp+/+ and dbp/ mice (Table
1), several observations demonstrated
differences in the distribution of sleep over the 24 hr day.
dbp/ mice displayed less sleep during the light or rest
period and tended to have more sleep in the dark or active period
(Table 1, Fig. 1b). Of the
three behavioral states, the distribution of PS was generally affected
the most by genotype. During the light periods of both baseline and
recovery, dbp/ mice displayed significantly less PS. In
addition, during the entire recording period, the expression of PS was
consistently more variable in dbp/ mice (i.e., larger
SEM, see Tables 1, 3).
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Figure 1.
Distribution of sleep and SWS delta power.
A, Individual distributions of rest episodes in BSL1 and
BSL2, the 6 hr SD, and recovery days REC1 and REC2. Black
bars indicate 15 min for which the 2 hr mean percentage sleep
was larger than the baseline mean (see Materials and Methods).
Individuals are indexed 1-8 for dbp+/+ (top 8 bars) and dbp/ (bottom
8 bars). Vertical lines and
horizontal black bars in the top and
bottom mark the dark periods. B, Time
course of PS, SWS, and SWS delta power (DELTA).
Mean ± SEM hourly (or 2 hr for DELTA in the dark
periods) values (n = 8 per genotype). The variables
were affected by genotype (*p < 0.05 indicates intervals with significant genotype differences;
t tests) and SD ( > BSL2 and  < BSL2
indicate intervals in which recovery values differed from BSL2;
p < 0.05; paired t tests; for
dbp+/+ depicted above the reference line below each set
of curves, for dbp/ beneath). Three-way ANOVA;
REC1: factor SD: PS, p < 0.0001 and
SWS, p < 0.02; factor time: p < 0.0001; genotype × SD: DELTA, p < 0.04;
genotype × time: PS, DELTA, p < 0.04 and
SWS, p < 0.002; SD × time: PS, DELTA,
p < 0.0001. REC2: factor SD: PS,
SWS, p < 0.0005; factor time:
p < 0.0001; genotype × time: PS, SWS,
p < 0.01 and DELTA, p < 0.005; SD × time: PS, p < 0.05.
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The altered distribution resulted in a significantly smaller
light-dark amplitude for all three behavioral states (Fig.
2a). The lack of DBP affected
the expression of SWS and PS to the same extent, leaving the PS/SWS
ratio unchanged (Table1, Fig. 2a). These differences were a
consequence of the presence in several of the dbp/ mice
of extended (>2 hr) rest episodes in the dark or active periods (and
extended active episodes in the light or rest periods), whereas in
dbp+/+ mice, rest episodes were restricted to the light
period (Fig. 1a).
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Figure 2.
Daily amplitude in sleep time. a,
Filled (dbp+/+) and open
(dbp/) bars indicate the mean ± SEM difference (n = 8 per genotype) in percentage
recording time, between values in the light and dark periods in
baseline days 1 and 2 for SWS, PS, and total sleep (TS)
(SWS+PS) and the PS/SWS percentage. The light-dark
difference for TS, SWS, and PS was smaller for dbp/
(* p < 0.05; t test). Note that,
for TS and SWS, the scaling is indicated on the left,
and for PS and PS/SWS, the scaling is on the right.
b, Mean ± SEM difference (n = 4 per genotype) between percentage TS in the (subjective) light and
dark periods of BSL and days 1 and 2 under constant darkness (DD1 and
DD2). For DD, it was assumed that circadian timing did not deviate from
baseline (see Fig. 3b). The amplitude was smaller in
dbp/ (* p < 0.02;
t test) and was reduced under DD. Two-way ANOVA; factor
genotype, p < 0.004; factor day (BSL, DD1, DD2),
p < 0.003.
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Under constant-dark conditions, the distribution of sleep remained
remarkably stable in dbp+/+ mice (Fig.
3a). Especially, the timing of
the end of the major rest period did not differ from baseline and
varied little between individuals. In dbp/ mice, the
absence of light resulted in an even more disrupted distribution of
sleep than under light-dark conditions (Fig. 3a), which was
accompanied by a further reduction in the amplitude of sleep time (Fig.
2b).
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Figure 3.
Time course of TS and SWS EEG delta power
(DELTA) for a BSL under 12 hr light/dark, followed by
2.6 d under constant darkness (DD1-DD3). Top curve
in each panel represents dbp+/+ (left
scaling), and the bottom curve represents
dbp/ (right scaling). Thin
lines are repetitions of BSL. > dbp+/+
and < dbp+/+ indicate intervals in which values
differed between genotypes (p < 0.05;
t tests; n = 4 per genotype).
Black horizontal bars indicate dark periods, and
shaded bars indicate the subjective light periods under
DD. a, Hourly TS values as percentage recording
time. Horizontal lines represent the mean TS in BSL,
DD1-DD3. b, Hourly or 2 hr delta values for the
(subjective) light or dark periods, respectively, as a percentage of
the value in the last SWS hour in the BSL light period. Thick
horizontal lines represent the mean level of DELTA in BSL,
DD1-DD3. Peak and trough values are indicated by open
squares (±SEM for time and magnitude). Only peak values and
amplitude (peak trough) varied with genotype [two-way ANOVA; factor
genotype (amplitude, peak), p < 0.03; factor day,
p > 0.6]. The difference between peak or trough
times of consecutive days did not deviate from 24 hr (paired
t tests).
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A reduction in sleep consolidation was not only evident at a circadian
level but also at the level of individual sleep episodes. Significantly
more SWS episodes <2 min (and significantly less >2 min) were counted
in dbp/ (Fig.
4a). Similar, but less robust, differences were observed for PS (Fig. 4b).
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Figure 4.
Frequency distribution of episode duration in
baseline for SWS (a) and PS
(b) over nine consecutive time bins (only lower
bin limits are indicated). Filled
(dbp+/+) and open
(dbp/) vertical bars indicate
mean ± SEM number of episodes per bin expressed per hour of SWS
or PS (n = 8 per genotype). Frequency differed with
genotype for both behavioral states (two-way ANOVA; factor genotype,
p < 0.05; factor bin, p < 0.0001; interaction, p < 0.005;
*p < 0.05 indicates bins with significant genotype
differences; t tests).
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EEG delta power in SWS
The SWS EEG is characterized by thalamocortical oscillations in
the delta (1-4 Hz) frequency range (Steriade et al., 1993). Spectral
analysis quantifies the contribution of specific frequencies to the EEG
signal in terms of power (in V2 units).
Power in the delta range varies according to the distribution of sleep
and wakefulness in that it decreases in the presence of SWS and
increases in the absence of SWS (Borbély, 1982; Daan et al.,
1984). In accordance with this, SWS delta power decreased in the course
of the light periods, i.e., the main rest period for mice, and
increased in the dark periods (Fig. 1b). For
dbp+/+ mice, these changes were highly significant within
both periods (one-way ANOVA factor time, p < 0.0001).
For dbp/, the amplitude of the daily changes in delta
power was strongly reduced. Calculated as the difference between peak
and trough delta power in baseline, the overall amplitude was
two-thirds of that observed in dbp+/+ (dbp+/+ vs
dbp/, 88 ± 9 vs 59 ± 6%; p < 0.02; t test). With this reduction in amplitude, several
other delta power measures differed accordingly between genotypes.
Thus, the increase in delta power during the dark period was not
significant in dbp/ (one-way ANOVA factor time,
p > 0.5 vs p < 0.0001 in
dbp+/+); the mean value reached in the last 2 hr of the dark
periods was almost half of that for dbp+/+ (Fig.
1b); the initial level at light onset (i.e., in the first 15 min of SWS) was lower (dbp+/+ vs dbp/,
150 ± 6 vs 128 ± 7%; p < 0.03;
t test); the decrease in the course of the light period was
only marginally significant (p < 0.05 vs
p < 0.0001 in dbp+/+; one-way ANOVA); and
finally, the overall mean baseline level of SWS delta power was lower
(mean over 48 1 hr intervals per mouse; dbp+/+ vs
dbp/, 145 ± 7 vs 124 ± 4%;
p < 0.02; t test).
The genotype differences in amplitude of the daily changes in SWS delta
power persisted in the absence of light and were smaller for
dbp/ because of lower peak values reached at the
end of the subjective dark periods (Fig. 3b). The analysis
of peak and trough delta power further revealed that, for both
genotypes, neither the magnitude nor timing changed under constant
darkness, despite the profound effect of this condition on the
sleep-wake distribution in dbp/ (Fig.
3a).
The lower delta power values reached for dbp/ in the
last 6 hr of the dark periods of baseline did not result from an
overall suppression of the amplitude of the EEG signal but rather from specific changes in the 1.0-9.5 Hz range (Fig.
5a). This frequency range
coincided with that in which EEG power in SWS decreased in the
course of the baseline light period (Fig. 5b). These changes in the SWS EEG were paralleled by highly frequency-specific changes in
the waking EEG. In the last 6 hr of the dark period, power in the
1.25-5.0, 6.5-9.0, and 17.0-19.25 Hz ranges of the waking EEG was
significantly higher in dbp+/+ (Fig. 5c). Between
2.75 and 3.25 Hz, this increase was highly significant
(p < 0.001; t test). Similar
observations were made within genotypes when the waking EEG spectrum of
the last 6 hr of the dark period was compared with that of the
remaining 18 hr of baseline (analyses not shown). However, these
changes were significant only for dbp+/+ mice, whereas the
waking EEG spectra in these 18 hr did not differ between genotypes.
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Figure 5.
EEG spectra in the last 6 hr of the baseline dark
(D) period. a, Difference in SWS
EEG spectra between dbp+/+ and dbp/
(n = 8 per genotype). Individual spectra were first
expressed relative to spectral values in the last hour of SWS in the
baseline light (L) period. Subsequently, the
spectral profile for dbp+/+ (dots) was
expressed as a percentage of that of dbp/ mice
(circles). Frequency bins with significant genotype
differences in EEG power are indicated by filled horizontal bars
below the curves (p < 0.05;
t test). b, For comparison, the EEG
changes over the light period are depicted as the difference
between the SWS EEG spectra in the first recording hour and the last
hour of SWS (100%) in the baseline light period for
dbp+/+ (dots) and dbp/
(circles). Significant differences are indicated by
filled (dbp+/+) and open
(dbp/) horizontal bars
(p < 0.05; paired t tests).
c, Genotype differences in the waking
(W) EEG in the last 6 hr of the baseline
dark period. Individual spectra were expressed first relative to the
values in the remaining 18 hr of baseline. Symbols as in
a.
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Theta peak frequency
Hippocampal theta oscillations (5-10 Hz) mark the EEG of both PS
and exploratory behavior and are associated with learning, memory
consolidation, and long-term potentiation (Vinogradova, 1995; Shors and
Matzel, 1997; Vertes and Kocsis, 1997). The analysis of the EEG
spectral profiles revealed that the prevailing theta frequency in both
PS and exploratory behavior was ~0.25 Hz higher for
dbp/ (Table 2). A
nonsignificant higher and more narrow theta peak in dbp+/+
added to the changes observed between the EEG profiles of these two
behavioral states, suggesting a slower but more regular theta rhythm
for dbp+/+. The EEG of SWS displayed (nonsignificant)
changes in peak frequency in the same direction for both theta (Table
2) and delta (data not shown). Theta peak frequency during PS varied
with time-of-day (two-way ANOVA with repeated measures: light or dark
period, p < 0.0001; genotype, p < 0.005; interaction, p > 0.7), but the dark to light
difference did not vary with genotype (dbp+/+, +0.30 Hz,
p < 0.001; dbp/, +0.33 Hz,
p < 0.0005; paired t tests), and the
dbp/ to dbp+/+ difference did not vary with
time-of-day (light period, +0.24 Hz, p < 0.005; dark
period, +0.26 Hz, p < 0.02; t tests).
Compensatory rebound after sleep loss
Although especially toward the end of SD brief SWS episodes could
not be avoided (dbp+/+ vs dbp/, 7.5 ± 1.8 vs 4.3 ± 1.1%; p > 0.1; t test)
(Fig. 1b), the SD did result in a substantial reduction in
sleep time relative to the corresponding 6 hr of BSL2 (88 vs 91% for
dbp+/+ and dbp/, respectively) (Table 1).
After the SD, as in baseline, dbp/ mice slept less in
the light periods and somewhat more in the dark periods of recovery (Table 3, Fig. 1b). However,
in the light period of recovery day 1, the differences observed in the
baseline light-period were amplified by the SD, and significant
differences between genotype were now reached for all three behavioral
states (Table 3). In the light period of recovery day 2, the genotype
differences were again limited to PS.
Compared with the initial 6 hr of BSL2, in the initial 6 hr of recovery
day 1, a robust 140% increase in PS was observed in dbp+/+
mice at the cost of the amount of wakefulness (Table 3, Fig.
1b). For dbp/, none of the behavioral states
were significantly affected in this period, and the relative PS rebound
tended to be larger for dbp+/+ (p < 0.07; t test). In the dark period of recovery day 1 also,
SWS time was above baseline for dbp+/+, but the relative
increase in PS was still more important judged by the continued
increase in the PS/SWS ratio (Table 3). For dbp/, only
for PS a modest increase was present. Interestingly, whereas for
dbp+/+ values no longer deviated from baseline in the light period of recovery day 2, for dbp/, SWS time was now
significantly below baseline. This was illustrated by the distribution
of the rest episodes (Fig. 1a).
Immediately after the SD, high values of SWS delta power were reached
for both genotypes (Fig. 1b), but in contrast to the findings in baseline, delta power in the initial 15 min of SWS did not
differ between genotypes (dbp+/+ vs dbp/,
193 ± 11 vs 175 ± 12%; p > 0.3;
t test). In addition, the highly significant increase in SWS
delta power in these 15 min (as a percentage of delta power in the
initial 15 min of SWS in baseline; dbp+/+, 129 ± 8%,
p = 0.008; dbp/, 136 ± 6%,
p = 0.0002; paired t tests) did not vary
with genotype (p > 0.5; t test).
Delta power rapidly decreased in the presence of SWS, which was
illustrated by its short-lasting positive rebound (only significantly
larger than baseline in the first 15 and 25 min of SWS for
dbp+/+ and dbp/, respectively) and its steep
decline during the first 6 hr of recovery. For dbp+/+, even
an undershoot below baseline ("negative rebound") (Franken et al.,
1991a; Feinberg and Campbell, 1993) was observed after 3 hr, and delta
power tended to remain below baseline thereafter (Fig. 1b).
As a consequence, the large genotype difference in delta power observed
in the second half of the baseline dark periods was absent in the first
recovery day and still reduced in the second recovery day (Fig.
1b). Previously, it has been demonstrated that a negative
rebound in SWS delta power is a consequence of the increased SWS time
after SD (Franken et al., 1991a,b). The absence of a negative rebound
in dbp/ (Fig. 1b) can thus result from the
significantly smaller amount of SWS in the first 6 hr of recovery day 1 compared with dbp+/+ (Table 3).
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DISCUSSION |
DBP affects circadian sleep consolidation
The most conspicuous difference between dbp wild-type
and mutant mice is the decreased circadian amplitude of the
distribution of sleep observed in the latter. This attenuated amplitude
in dbp/ mice was observed under both light-dark and
constant-dark conditions and can be attributed mainly to the occurrence
in dbp/ of important rest episodes in the active periods
and of active episodes in the rest periods. Similar to what has been
observed in several other inbred strains (Franken et al., 1999), in
dbp+/+ mice, rest and active episodes were confined to the
light and dark periods, respectively. The reduction in sleep
consolidation was not only evident at the circadian level but also at
the level of individual sleep episodes. These findings suggests that
DBP, in addition to changing the period of the circadian clock
(Lopez-Molina et al., 1997), modifies the strength of the SCN output
signal, which governs the distribution and consolidation of sleep and wakefulness over the day (Edgar et al., 1993; Dijk and Czeisler, 1995).
DBP affects the time course of SWS delta power
SWS delta power is thought to reflect a homeostatic process
underlying the regulation of SWS propensity (Borbély, 1982; Daan et al., 1984) because it decreases in the course of the major rest
period and increases in the major active period in a variety of
mammals, including mice and men (Dijk et al., 1987; Franken et al.,
1999). Furthermore, sleep deprivation results in an increase in SWS
delta power that is proportional to its duration (Tobler and
Borbély, 1986). Thus, the lower level of SWS delta power reached
in the second half of the dark period suggests that, in dbp/ mice, SWS propensity was lower. This notion was
supported by the frequency-specific differences between genotypes in
the EEG of both SWS and wakefulness in this period. The genotype
differences in the SWS EEG in the second half of the dark period
strongly resembled the EEG changes that typically accompany the
decrease in SWS propensity during the major rest period (Fig.
5b) (Franken et al., 1991a; Dijk et al., 1997). Sleep
propensity can also be monitored in the waking EEG (Cajochen et al.,
1999, and references therein). In the rat, EEG power in wakefulness
gradually increases as time awake progresses, and these changes are
most pronounced in the delta (3-5 Hz) and theta (6-10 Hz) frequency
range, and in the 16-19 Hz range (Franken et al., 1991a, 1993). Only
in these three frequency bands, the waking EEG of dbp+/+
mice was increased compared with dbp/ in the last 6 hr
of the baseline dark period.
The daily variations in SWS delta power are thought to be primarily
"driven" by the distribution of sleep and wakefulness and are not
directly under circadian control (Dijk and Czeisler, 1995; Dijk et al.,
1997). Therefore, the genotype differences have to be interpreted first
as a secondary effect of the reduced circadian amplitude of SWS.
dbp/ mice did display 33 min more SWS in the dark
period, although only in one 1 hour interval was this difference
significant. Can this small increase in SWS time explain the large
decrease in the level of SWS delta power, or does a lack of
dbp decelerate the build-up of SWS propensity during wakefulness? The changes in SWS delta power after the sleep deprivation demonstrated that delta power decreases rapidly in the presence of SWS;
within 25 min of SWS, the positive rebound was dissipated. Using
computer simulations, the relationship between the build-up of delta
power in the absence of SWS and its exponential decrease in its
presence have been quantified in the rat (Franken et al., 1991a, 1993).
According to similar simulations in dbp+/+ and
dbp/ mice, we estimated the time constant of the
increase at 5.5 hr and that of the decrease at 2.1 hr and found no
differences between genotypes (analyses not shown). With this decrease
rate, it can be calculated that, within 22 min of (continuous) SWS,
delta power can be reduced by 50%. These analyses suggest that the
accumulation of SWS propensity in the course of the dark period can, to
a large extent, be discharged by the extra SWS time present in
dbp/ and that the rate of accumulation does not differ
between genotypes. The latter statement was further supported by the
fact that, after 6 hr of enforced wakefulness, the initial level of SWS
delta power did not differ between genotypes. The importance of the
small amount of SWS present in the active period to preclude a large build-up of SWS propensity was also demonstrated in the rat (Franken et
al., 1993). Depriving rats of the 2 hr of SWS normally present during
the 12 hr active period resulted in a large increase in SWS delta
power, comparable with that observed after a 24 hr sleep deprivation
(Franken et al., 1991a).
The reduced amplitude in SWS delta power for dbp/
resulted in a lower level of delta power across baseline. This suggests that, in dbp/ mice, SWS propensity is generally lower,
which is supported by the observation that their SWS is more
fragmented. Negative correlations between the level of SWS delta power
and SWS fragmentation have been reported previously in the rat (Franken et al., 1991a, 1993) and the mouse (Tobler et al., 1997; Franken et
al., 1999).
DBP may act through its effect on locomotor activity
It has been reported previously that dbp/ mice
display less spontaneous locomotor activity, resulting mainly from a
profound reduction in the last part of the dark period in which
dbp+/+ mice display maximum activity levels (Lopez-Molina et
al., 1997) (Fig. 6). In mice, a reduction
of locomotor activity can result in a reduced amplitude in the
circadian distribution of sleep and in a more fragmented sleep (Welsh
et al., 1988; Edgar et al., 1991a), which corresponds with the
observations made in the present study. Furthermore, the distribution
of spontaneous motor activity across the active period affects the
free-running period of the circadian clock (Edgar et al., 1991b). Given
the different distribution of locomotor activity in the two genotypes
(Fig. 6), the shortening of tau observed in dbp/
(Lopez-Molina et al., 1997) could be explained by this "activity
feedback" to the circadian pacemaker (Edgar et al., 1991b). In
addition, results from several studies suggest a causal relationship
between the level of activity during wakefulness and the amount of SWS
with high delta power during subsequent sleep (Horne and Moore, 1985;
Mistlberger et al., 1987). In the present study, the levels of both SWS
delta power and locomotor activity in dbp/ started
deviating in parallel from those in dbp+/+ (Fig. 6). This
suggests that, in dbp/, the reduced activity might have
contributed to the difference in the level of SWS delta power.
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|
Figure 6.
Time course of wakefulness
(%W), SWS delta power
(DELTA), and spontaneous locomotor activity
(ACTIVITY) in baseline. Hourly values of time
course of wakefulness and delta power from the present experiment (mean
over 2 baselines) are aligned with hourly activity values [number of
infrared beam
breaks · hr 1 · d1;
mean over 10 d; dbp+/+, n = 33;
dbp/, n = 25; from Lopez-Molina
et al. (1997) with permission]. Whereas time course of wakefulness did
not significantly differ between genotypes, activity during waking was
reduced for most of the dark period in dbp/ mice.
This reduction paralleled the decrease in delta power in
dbp/. The relationship between activity during
waking and delta power during SWS was underscored by a highly
significant correlation (linear regression,
r2 = 0.59; p < 0.0001; 24 1 hr values per genotype) that further improved when
delta power was correlated with activity in the preceding hr
(r2 = 0.76).
|
|
DBP may act through its effect on target genes
Because DBP is a transcriptional factor, its effects are likely to
be mediated through its target genes. Thus far, genes whose expression
is influenced by DBP have been identified only in the liver (Lavery et
al., 1999, and references therein). The role of these, if any, in the
regulation of sleep is unknown. Possibly, the cytochrome testosterone
15 -hydroxylase may be of relevance because it could affect the level
of testosterone, which has pronounced effects on locomotor activity and
the period of circadian rhythms in mice (Daan et al., 1975). Database
screening for DBP-binding sites (5' RTTATGTAAY) (Falvey et al., 1996)
in promoters of other sleep-related genes revealed a 90% nucleotide
match for such a sequence in the gene encoding tryptophan hydroxylase
(TPH). TPH is the rate-limiting enzyme in the synthesis of the
neurotransmitter serotonin, which has been implicated in sleep,
especially in the regulation of PS (Jouvet, 1984; Boutrel et al.,
1999). This is of special interest because the lack of DBP affected the
expression and regulation of PS the most, as evidenced by the
significant reduction in PS in the light periods, the higher
variability in PS time, and the lack of a significant PS rebound after
sleep loss. Serotonin has been further associated in the activity
feedback signal that can modify circadian period (Mistlberger et al.,
1998) and in locomotor activity per se (Reuter et al., 1997). Moreover, reduced serotonergic output from the raphe nuclei can increase the
frequency of hippocampal theta (Vinogradova, 1995). Whether or not DBP
can act on various sleep parameters via the modulation of TPH gene
expression remains to be examined.
Based on various observations, Lopez-Molina et al. (1997) have
concluded that dbp is a clock output gene rather than an
essential clock gene. However, recent evidence suggests that circadian
dbp transcription is controlled by the same molecular
components that establish self-sustained oscillations in the expression
of essential clock genes (J. A. Ripperger, L. P. Shearman,
S. M. Reppert, and U. Schibler, unpublished observations).
In the present study, we have demonstrated that DBP mostly affects
those aspects of sleep that are known to be under direct circadian
control but leaves the homeostatic, circadian-independent regulation of
SWS unaffected. Hence, dbp links the molecular clockwork
generating self-sustained circadian oscillations to complex circadian
outputs, such as locomotor activity and sleep. The precise mechanisms
by which DBP operates are still unknown, and their dissection will require the identification of relevant target genes.
| |
FOOTNOTES |
Received Aug. 12, 1999; revised Nov. 3, 1999; accepted Nov. 4, 1999.
This study was supported by the Swiss National Science Foundation
Grants 31.45751.95 to M.T. and 31.47314.96 to U.S.
Correspondence should be addressed to Dr. Paul Franken, HUG
Belle-Idée, Biochemistry and Neurophysiology Unit, Department of
Psychiatry, University of Geneva, Chemin du Petit-Bel-Air 2, CH-1225
Chêne-Bourg, Switzerland. E-mail: paul.franken{at}hcuge.ch.
Dr. Lopez-Molina's present address: Laboratory of Plant Molecular
Biology, Rockefeller University, New York, NY 10021.
| |
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TOP
ABSTRACT
INTRODUCTION
