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Howard Hughes Medical Institute, Department of Neuroscience, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
1 Corresponding author: Department of Genetics, North Carolina State University, Campus Box 7614, Raleigh, NC 27695.
E-mail: susan_harbison{at}ncsu.edu
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Quiescence in the fruit fly Drosophila melanogaster possesses all of the behavioral characteristics of mammalian sleep (HENDRICKS et al. 2000; SHAW et al. 2000). Flies exhibit a diurnal sleep–wake cycle regulated by the circadian clock. Videotape analysis shows that flies sometimes change posture during sleep (standing with the head drooping down) and prefer to sleep near a food source (HENDRICKS et al. 2000). Like mammals, flies that are asleep are less responsive to outside stimuli than normal (HENDRICKS et al. 2000; SHAW et al. 2000). These observations enable the use of Drosophila, a classic genetic model organism, to rapidly identify candidate genes involved in this elusive behavior.
Thus far, few candidate genes that alter sleep phenotypes in flies have been identified. The neurotransmitters serotonin and dopamine have been implicated in sleep. Serotonin appears to promote sleep through the d5-HT1A receptor, while increased extracellular dopamine lowers the arousal threshold and may promote waking (KUME et al. 2005; YUAN et al. 2006). Flies bearing mutations of the molecular circadian clock genes cycle and Clock sleep less in both standard day/night cycles and in constant darkness (HENDRICKS et al. 2003). Alterations in cAMP signaling in a specific region of the fly brain affect sleep duration (HENDRICKS et al. 2001; JOINER et al. 2006). Heterozygous null mutations in the immune response gene Relish reduce day and night sleep in females and night sleep in males (WILLIAMS et al. 2007). In addition, mutations in Shaker, the 
-subunit of a voltage-dependent potassium channel, and Hyperkinetic, the β-subunit of the same channel, reduce sleep (CIRELLI et al. 2005a; BUSHEY et al. 2007). Intriguingly, background modifiers mitigated the effect of Shaker mutant alleles on sleep in older stocks; outcrosses to two wild-type backgrounds restored the short-sleeping phenotype (CIRELLI et al. 2005a).
To minimize genetic background effects when comparing across lines, we examined sleep in a collection of 136 P-element insertion lines created in an isogenic background (BELLEN et al. 2004). Theoretically, each line differs only by the P-element insertion and can be compared to the isogenic parent line as a control. A line carrying a single P-element insertion in the first exon of Calreticulin showed a reduction in sleep by as much as 289.2 min (4.82 hr)/24 hr. In contrast, a line carrying an insertion in the third exon of malic enzyme displayed an increase in sleep of as much as 424.8 min (7.08 hr). Precise excisions of the P-element tagging Calreticulin increased the short-sleeping phenotype back to wild type, while precise excisions of the insertion in malic enzyme reverted the long-sleeping phenotype. Furthermore, measurements of whole-body energy stores (protein, triglycerides, and glycogen) in all 136 lines enabled us to quantify the relationship between sleep and energy stores. Significant mutational genetic correlations between sleep and energy storage parameters are present and sex specific. The wide variety of biological processes attributed to sleep candidate genes and the extensive pleiotropy observed in the lines tested suggest that sleep has more than one function.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Flies were reared and maintained on standard medium in a 25°, 12-hr light/dark cycle incubator. For all assays, adult virgins were collected and maintained at 30 flies to a single-sex vial until the time of assay. This protocol ensured that each insertion line was exposed to identical levels of social enrichment, which can alter sleep (GANGULY-FITZGERALD et al. 2006). Specifically, flies held in vials containing <30 flies have lower daytime sleep than when they are held in vials containing 30 flies or more (GANGULY-FITZGERALD et al. 2006). Since we were particularly interested in mutations that reduce sleep, we purposely maintained our flies in groups of 30 to a vial to bias our study toward finding increased daytime sleep. Furthermore, maintaining virgins at constant density provided equal access to food. Sixteen flies of each sex were assayed per line, which gives a statistical power of 80% to detect a 1.75-hr difference between the insertion line and the control based on pilot studies. Flies were 4–7 days old at the time of the sleep assay. We measured energy stores in a separate group of virgin flies that were age matched to those in the sleep assay.
Baseline sleep and activity assays:
Activity and sleep behavior were monitored using the Drosophila Activity Monitoring System (Trikinetics, Waltham, MA) (HO and SEHGAL 2005). With this system, each fly is loaded into an activity monitor tube. An activity count is recorded by a computer each time a fly crosses an infrared beam that bisects the monitor tube. Activity counts were recorded at 1-min intervals. Seven continuous days of sleep and activity were recorded for each P-element insertion line. After 7 days, flies were visually examined; any flies that died during the course of the experiment were removed from the analysis. Sleep was defined as any period 
5 min without an activity count, as previously determined (HENDRICKS et al. 2000; SHAW et al. 2000; HUBER et al. 2004; HO and SEHGAL 2005). An in-house C++ program was used to calculate duration of sleep in minutes per day, numbers of sleep bouts per day, average sleep bout in minutes, and the number of activity counts per waking minute (or waking activity). As males sleep more during the day than females (HUBER et al. 2004), sleep times and bout numbers were divided into daytime/nighttime sleep and daytime/nighttime bout number.
Measurement of energy stores:
Flies were weighed and homogenized on ice in 0.01 M KH2PO4, 1 mM EDTA, pH 7.4, buffer as previously described (CLARK and KEITH 1988), using 25 µl of homogenizing buffer per fly. Homogenates were used immediately to measure protein, glycogen, and triglycerides. Each assay is colorimetric; spectrophotometer measurements were made using a Perkin-Elmer V3 plate reader (Waltham, MA). Protein in micrograms per fly was determined via Bradford's method (BRADFORD 1976) with BSA used for the protein standard curve. Total glycogen in micrograms per fly was measured as previously described (CLARK and KEITH 1988). Briefly, glycogen from the homogenates was broken down into glucose by adding 0.1 unit/ml amyloglucosidase enzyme slurry (Sigma, St. Louis) to 1.5-µl samples of homogenate in a 96-well plate. Total glucose was then determined using the PGO enzymes kit (Sigma) (CLARK and KEITH 1988). This measure is effectively the amount of whole-body glycogen, as free glucose is estimated at <5% of the amount of glycogen stored (CLARK and KEITH 1988). Glucose concentrations were determined using a glucose standard curve run on the same plate. Known concentrations of glycogen were used to assess the expected recovery of glycogen (ZIMMERMAN et al. 2004); if <95% of glycogen was recovered, the samples were rerun. Triglyceride measurements were determined using an enzymatic assay kit (serum triglyceride determination kit, Sigma) (MCGOWAN et al. 1983). The true serum triglyceride in micrograms per fly was determined from blank and glycerol standards run with each plate. Homogenates were then stored at –80°, and measurements were repeated the next day.
Statistical analysis of P-element insertion lines:
For each fly, all measures of sleep, activity, and energy stores were first expressed as a deviation from the contemporaneous w1118; Canton-S line mean assayed in each experimental block. This calculation has two benefits. First, the variation between experimental blocks due to random environmental fluctuations is mitigated. Second, some measures of sleep and activity (average sleep bout length and waking activity) are not normally distributed; however, when computed as deviations from the control mean, their distribution is normal.
Mutational effects were evaluated using analysis of variance (ANOVA). Two-way ANOVA models were performed for each trait using the model y = µ + L + S + (L x S) + E, where µ is the overall mean, L is the random effect of line, S is the fixed effect of sex, and E is the among-fly variance. A reduced version of this model was also performed for each sex separately. For glycogen and triglyceride measures, body weight and protein were added into the ANOVA model as covariates; for protein measures, body weight was included as a covariate in the ANOVA model. To account for the removal of dead flies from the data set, variance components were estimated using the restricted maximum-likelihood method, which accounts for unbalanced data. The total variance for each trait was estimated as the sum of the L, L x S, and E components in the combined-sex model and of L and E in the reduced model.
Broad-sense mutational heritability, 
, was estimated for each trait as 
2G/2P, where 2G is the genetic variance component and 2P is the phenotypic variance (FALCONER and MACKAY 1996). 2G was estimated as 2L + 2LS and 2P as 2L + 2LS + 2E from the line, line x sex, and environmental variance estimates of the combined-sex ANOVA, while 2G = 2L and 2P = 2L + 2E from the reduced ANOVAS for each sex (SAMBANDAN et al. 2006).
To identify candidate insertions for retesting, confidence limits were computed as ±zP/(n)1/2, where z is the value of the normal distribution at significance level (0.05), P is computed from the total phenotypic variance estimate determined above, and n is the number of flies assayed per line (NORGA et al. 2003; HARBISON et al. 2004). Confidence intervals were calculated at the 95, 99, and 99.9% level. Candidate lines of interest were chosen from lines that exceeded these thresholds.
Twenty-two of the most extreme short- and long-sleeping lines were retested using the same protocol as for the original test. Results were pooled for both tests and analyzed using the ANOVA model y = µ + G + S + T + (G x S) + (G x T) + (T x S) + (G x S x T) + E, where G, S, and T are the fixed effects of genotype (parental control or P-element insertion), sex, and experimental test (original screen or retest), and E is the residual among-fly variance. Insertion lines having significant (P < 0.05) G or G x S terms were interpreted as candidate genes for 24-hr sleep.
Partial Pearson product-moment correlations were determined for each phenotypic measurement as cov12/(2L1 x 2L2)1/2, where cov12 is the covariance between traits 1 and 2, 2L1 is the estimate of line variance for trait 1, and 2L2 is the estimate of line variance for trait 2. The SAS CORR procedure was used to estimate the covariance matrix between traits. The restricted maximum-likelihood estimate of 2Line from the ANOVAs described above was used to estimate the line variance for each trait. The P-values in Tables 4, 5, and 7 represent how significantly different each correlation is from zero. For correlations involving glycogen and triglycerides, protein and body weight were included as covariates; correlations involving protein included body weight as a covariate (CLARK and KEITH 1988). All statistical analyses were carried out using the SAS software package (SAS Institute, Cary, NC).
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integrin, 5'-TGGTGCACGGACAAGGAATAC-3' and 5'-ACATCTAGGACCGGCTGGTTCT-3'; and Defense repressor I, 5'-GGCCAAAAGATGTGGTGCAT-3' and 5'-TGATGTTCATTGCGCGACA-3'. SYBR Green chemistry (Applied Biosystems) was used for the quantitative PCR reaction in an ABI 7000 thermal cycler (Applied Biosystems) under default PCR protocol conditions. Resultant RNA quantities were normalized to actin measured in each respective sample. Samples were compared to the contemporaneous w1118; Canton-S control using the Kruskal–Wallis nonparametric test (SAS Institute). RNA samples were also obtained from selected Calreticulin revertant lines and assayed as described above.
Construction and verification of revertant lines:
The P[GT1] construct was mobilized by crossing w, isoCanton-S; isoCanton-S; P[GT1] females to w/Y; wg[Sp-1]/CyO; ry[506] Sb[1] P[ry[+t7.2]=
2-3]99B/TM6 males. To maintain background integrity, third chromosome balancer stock constructed from the w1118; Canton-S parent (gift of Akihiko Yamamoto and Trudy Mackay) was used to create these revertants (w, isoCanton-S; isoCanton-S; H/TM3). Male offspring with the genotype w, isoCanton-S; CyO/isoCanton-S; P[GT1]/ry[506] Sb[1] P[ry[+t7.2]=2-3]99B were mated to w, isoCanton-S; isoCanton-S; H/TM3 females. Single males without the P[GT1] insertion were mated to w, isoCanton-S; isoCanton-S; H/TM3 females, and the resulting progeny were used to make homozygous P[–] excision stock.
PCR was used to identify those P[–] lines that were precise excisions. Putative Calreticulin precise excisions were verified with DNA sequencing. Primers were chosen to surround the P-element insertion region and produce a PCR product of 
500 bp. PCR products were run on a 1.5% agarose gel, and PCR product sizes were verified with a DNA ladder. Primers used for the Calreticulin revertants were 5'-CCTGGCCGGTGAAAAAGA-3' and 5'-TCCTTTCGTTATTCATTGAAGG-3' to amplify a 391-bp region containing the P-element insertion site inside the first exon. For the malic enzyme revertants, primers 5'-ATCAGCGCATTTCAAAGGTT-3' and 5'-GTTGCTGTTTCTCTTCGTGTAA-3' were used to amplify a 497-bp region in the third exon surrounding the P-element insertion site.
Statistical analysis of revertant lines:
The revertant lines were assayed for sleep phenotypes as described above. A mixed ANOVA model was used to determine whether the revertant line sleep was the same as that of the w1118; Canton-S parent using y = µ + G + B(G) + E, where µ is the overall mean, G is the genotype (P insertion or wild type), B is the random effect of experimental block, and E represents the within-fly environmental variance.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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In contrast, the correlation between sleep and bout number for females tended to be positive. The strongest correlations were between 24-hr/daytime bout number and all three measures of sleep time. We observed nonsignificant negative correlations between sleep and nighttime bout number in females. Generally, increased sleep in females implies increased numbers of sleep bouts, indicating that longer sleep might be less consolidated. These observations suggest that mutations affecting sleep bout number can have opposing effects on male and female sleep. Furthermore, the relationship between nighttime sleep and nighttime bout number may be fundamentally different from the relationship between bout number and daytime sleep; nighttime sleep and nighttime bout number are not significantly correlated in either males or females.
Correlation between sleep parameters and waking activity:
To better examine the genetic relationship between sleep and activity, we decoupled sleep time from activity by calculating waking activity as the number of beam crossings per minute spent awake (ANDRETIC and SHAW 2005). Table 5 shows the correlations between sleep phenotypes and waking activity. Male and female sleep times were significantly negatively correlated with waking activity. Thus, in the select group of lines examined in this study, more active flies tended to sleep less and less active flies tended to sleep more. Average sleep bout length was negatively correlated with waking activity, although the correlation was not significant in females. While waking activity was not correlated with bout number in males, it was strongly negatively correlated in females. The lack of correlation between waking activity and sleep bout number indicates that sleep consolidation can be genetically perturbed independently of activity in males. Mutations affecting female sleep bout number, however, may potentially impact waking activity.
Correlation between male and female sleep phenotypes:
The genetic correlation between males and females, rGS (the cross-sex genetic correlation), was calculated for each sleep trait (supplemental Table 3). With the exception of daytime bout number, all male and female sleep phenotypes were significantly and positively correlated, but not unity. Thus, some, but not all, genes that influence sleep in males overlap with genes that influence sleep in females. The variance due to the line x sex interaction term from the initial combined-sex ANOVA was partitioned into two groups (supplemental Table 3): the contribution due to differences in male and female among-line variance components [(LM – LF)2] and the contribution due to sex-specific effects of the insertions [LM x LF (1 – rGS)] (ROBERTSON 1959). For each sleep phenotype, the relative contribution due to sex-specific effects of the insertions accounted for the greatest percentage of the line x sex variance, underscoring the tendency of P-element insertions to have sex-specific phenotypic effects (HARBISON et al. 2004). Effects tended to be sex specific rather than sex antagonistic (significant effects in both sexes, but of opposite sign; see supplemental Table 1 for examples of sex-antagonistic effects).
Transcript expression for selected insertion lines:
Transcript abundance relative to the w1118; Canton-S parental line was examined in four insertion lines. Comparisons were made using age- and sex-matched lines harvested at the same circadian time of day. Gene transcript levels were normalized to actin transcript levels in each sample and compared to w1118;Canton-S using the nonparametric Kruskal–Wallis test. Transcript abundance was examined in BG02566 (Calreticulin), BG01037 (βintegrin), and BG01565 (Defense repressor I), which had reduced sleep; transcript level was also assayed in an insertion line that increased sleep, BG01628 (malic enzyme). Whole-body Calreticulin expression was reduced to 21.5% of wild-type levels in females and to 38.5% of wild-type levels in males (Figure 4A). We assayed transcript abundance in females only for lines BG01037 (βintegrin), BG01565 (Defense repressor I), and BG01628 (malic enzyme). Malic enzyme has two transcript isoforms, A and B; both were reduced to near zero in female mutants as compared to the wild-type parent (Figure 4B). Female βintegrin RNA actually increased relative to w1118; Canton-S by 170% (Figure 4C). Finally, Defense repressor I transcripts were moderately reduced (by 44%) in females as compared to the wild-type parent (Figure 4D). Thus, transcript levels in all four insertion lines examined were significantly different from the w1118, Canton-S parent.
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One insertion listed in Table 6 also had a significant effect on 24-hr sleep: BG02009, which putatively maps to the first intron of taiman, had significantly increased sleep in both males (115.8 min) and females (187.2 min). This insertion also resulted in a decrease of 16.68 µg/fly of glycogen in males and a decrease of 6.12 µg/fly of triglycerides in females, relative to w1118; Canton-S.
Genetic correlations between energy stores:
The genetic correlations among glycogen, triglycerides, and protein were computed for both males and females. Genetic correlations between the energy storage traits were positive and significant, but they were not unity, indicating that some, but not all, genes are common to all three traits. The correlation between triglycerides and glycogen was 0.4198 (P < 0.0001) for males and 0.2989 (P = 0.0004) for females. The correlation between triglycerides and protein was 0.5395 (P < 0.0001) and 0.5561 (P < 0.0001) for males and females, respectively. In addition, the correlation between glycogen and protein was 0.7348 (P < 0.0001) for males and 0.1873 (P = 0.0290) for females. Interestingly, the correlations tended to be lower in females than in males. Thus, although mutational heritability was high for energy storage traits in females, implying high genetic variation among the insertion lines, the covariance between traits was relatively low, suggesting that the genetic variation observed for each trait stems from a different set of insertions.
The cross-sex genetic correlations for energy stores were also calculated (supplemental Table 3). Male and female energy stores were positively correlated, although the correlations were not unity. As with the cross-sex genetic correlations on sleep phenotypes, much of the line x sex variance component reflects sex-specific effects of the insertions on energy stores.
Correlations between sleep and energy stores:
To assess whether genes impacting endogenous sleep also affect triglyceride and glycogen stores, we calculated the mutational genetic correlations among sleep phenotypes, glycogen, and triglycerides. Table 7 shows the correlations for both males and females. These correlations show a striking sex-specific pattern. Glycogen stores were positively correlated with all measures of sleep duration in males. The significant positive correlation implies that males that sleep longer have higher levels of glycogen. Glycogen stores were also negatively correlated with sleep bout number and positively correlated with average sleep bout length in males. Thus, males with fragmented sleep tend to have lower levels of glycogen.
In female flies, however, the pattern was different. Neither glycogen nor triglycerides were correlated with sleep time in females. Instead, triglycerides were negatively correlated with 24-hr and nighttime bout number, but not with daytime bout number. In addition, triglycerides were positively correlated with average bout length in females. Females with fragmented sleep, therefore, would tend to have lower levels of triglycerides. These results indicate that a genetic connection is present between endogenous sleep and energy stores in flies and that it is sex specific. Importantly, neither males nor females exhibited significant correlations between energy storage measures and waking activity, indicating that these correlations are specific to sleep phenotypes.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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) of 24-hr sleep time (see Tables 1 and 2). In wild-type lines, broad-sense heritability for sleep phenotypes appears to be higher than these estimates (S. T. HARBISON and T. F. C. MACKAY, unpublished results). Furthermore, human twin studies have estimated the narrow-sense heritability of 24-hr sleep time as 0.303 (DE CASTRO 2002) and 0.44 (PARTINEN et al. 1983), and heritability of the number of wake-ups (i.e., 24-hr sleep bout number) as 0.256 (DE CASTRO 2002). By definition, however, the broad-sense mutational heritability estimates in this study contain dominance and interaction variance (FALCONER and MACKAY 1996) and are likely higher than a narrow-sense estimate would be. Thirty-two candidate insertions that putatively disrupt genes impacting energy stores were also identified. Unlike sleep, broad-sense mutational heritability for energy stores was sexually dimorphic: females had much higher H2M than males for protein, glycogen, and triglycerides (Table 2). Thus, high levels of genetic variation for both sleep and energy storage phenotypes are present; female energy stores were more sensitive to genetic effects than were males. Candidate insertions identified for 24-hr sleep time and energy stores have effects on genes involved in physiology, development, and behavior. Each P-element insertion is assumed to disrupt the function of the nearest identified candidate gene. To prove that the P-element insertions disrupt sleep phenotypes, they will need to be excised/reverted, tested by complementation to deficiencies or mutant alleles, and exhibit phenotypic rescue when a wild-type construct is introduced. The first step toward this proof has been taken through the creation of precise excision lines that restored the normal sleep phenotype from two P-element lines: BG02566 (Calreticulin) and BG01628 (malic enzyme).
We observed reduced sleep in males and females of the BG02566 (Calreticulin) line; precise excision of the P-element insertion restored wild-type sleep. Calreticulin has many proposed functions, including a role in neural development (PROKOPENKO et al. 2000; NORGA et al. 2003), protein folding, and calcium signaling and homeostasis (KRAUSE and MICHALAK 1997; JOHNSON et al. 2001). Calreticulin mutant flies have normal exploratory behavior and geotaxis (STOLTZFUS et al. 2003), but are sensitive to ether anesthesia (GAMO et al. 1998). Furthermore, Calreticulin mutants exhibit impaired olfactory avoidance (STOLTZFUS et al. 2003; SAMBANDAN et al. 2006), yet respond normally to attractants (STOLTZFUS et al. 2003). Calreticulin transcripts also increase in sleep-deprived flies relative to flies in recovery sleep after sleep deprivation (WILLIAMS et al. 2007). Another molecular chaperone, BiP, which acts in the same molecular pathway as Calreticulin (JOHNSON et al. 2001), was recently shown to be involved in sleep homeostasis. BiP protein increases after sleep deprivation and decreases with recovery sleep; however, transgenic animals overexpressing BiP or producing a dominant negative construct of BiP have wild-type sleep (NAIDOO et al. 2007). The P[GT1] insertion in the first exon of Calreticulin reduced, but did not totally eliminate, whole-body transcript levels in both males and females. Other mutant alleles of Calreticulin tested in flies are not null mutations (GAMO et al. 1998; STOLTZFUS et al. 2003); indeed, it may not be possible to create a Calreticulin null mutation in flies as homozygous Calreticulin null mice die in the early embryonic stages (MESAELI et al. 1999). Future studies will determine what role Calreticulin has in sleep and waking.
Some of the candidate insertions identified have previously been implicated in metabolic pathways. We observed an increase in sleep in males and females of line BG01628, which has a P[GT1] insertion in the third exon of malic enzyme. Malic enzyme encodes a malate dehydrogenase, which functions in the citric acid cycle and malate metabolism. In a study of transcript abundance in the heads of wild-type flies, malic enzyme transcript increased significantly during both spontaneous wakefulness and sleep deprivation relative to sleep (CIRELLI et al. 2005b). Malic enzyme whole-body transcript expression was essentially zero in BG01628 females. Sleep was reduced to wild-type levels when this P element was precisely excised, suggesting that malic enzyme has a role in sleep and waking. In addition to malic enzyme, other metabolic pathways were implicated in sleep: glycolysis (HORTON et al. 1996) (BG02003, 6-phosphofructo-2-kinase), electron transport (BG01007, CG8776, and BG02419, escargot), and glucosamine metabolism (HAINES and IRVINE 2005) (BG02565, β4GalNAcTA). One insertion line had effects on both 24-hr sleep and energy stores in this experiment: the insertion in this line, BG02009, is located in the first intron of taiman. taiman is thought to be involved in signal transduction, activates transcription by binding to the ecdysone receptor, and is required for migration of border cells in the developing oocyte (STARZ-GAIANO and MONTELL 2004). Both males and females of the BG02009 insertion line had increased 24-hr sleep; however, males had decreased glycogen, while females had decreased triglycerides. The observation that genes involved in metabolism may also have a function in sleep is intriguing as recent human studies have linked short sleep times to obesity and diabetes (reviewed in CIZZA et al. 2005), indicating a potential molecular relationship between the two.
escargot encodes a zinc-finger protein with a role in the fusion of tracheal tubes (ZELZER and SHILO 2000; GHABRIAL et al. 2003) and PNS development (NORGA et al. 2003). Line BG02419, which has the P-element insertion 1 kb from the 5'-end of escargot, had significantly reduced sleep in both males and females (Table 3). We also observed a significant decrease in 24-hr sleep in BG01971 females at the 95% confidence interval; this insertion maps to 250 bp from the 5'-end of escargot. However, a significant effect on 24-hr sleep was not present in BG02297 flies, which have an insertion 200 bp from the 5'-end of escargot. Interestingly, male-specific decreases were observed in all three energy storage parameters in line BG01971, and line BG02297 had largely female-specific decreases (Table 6). No significant energy storage phenotypes were observed in BG02419. If each of these phenotypes maps to escargot, this observation would underscore the importance of the location and orientation of transposable element insertions in their effects on complex trait phenotypes (ROLLMANN et al. 2006). Merely changing the orientation of a single P-element insertion within an intergenic region can alter behavioral and physiological traits such as taste preference and life span (ROLLMANN et al. 2006).
To our knowledge, this is the first study to systematically analyze the genetic correlation among multiple sleep measures in flies. Numbers of sleep bouts can be thought of as the number of transitions from sleeping to waking states; abnormal numbers of transitions from waking to sleep are the hallmark of some sleep disorders in humans, such as sleep apnea and narcolepsy. Significant correlations between sleep times and sleep bout number exist in both males and females; however, the significant correlations were negative in males and tended to be positive in females. Thus, mutations that increase sleep bout number in both sexes may decrease sleep time in males and increase sleep time in females. Two interpretations of this result are possible. First, genetic perturbations that affect the transition between sleep and waking states will have opposing effects in males and females. Alternatively, the genetic basis of the forces that promote sleep and stimulate waking might be different in male and female flies. Interestingly, studies of human twins have revealed a positive correlation (0.183) between sleep time and number of wake-ups (bout number) (DE CASTRO 2002), similar to the pattern observed in female flies.
We also examined the correlation between waking activity and sleep phenotypes (Table 5). The negative correlations that we observed between waking activity and sleep suggest a genetic relationship between both measures. The correlations were not unity, however; thus, mutations affecting sleep will not necessarily affect waking activity. A recent study of the gene Hyperkinetic illustrates this point: two separate Hyperkinetic alleles reduced sleep without affecting waking activity (BUSHEY et al. 2007). Furthermore, a recent study of EMS-induced mutants suggests that the correlation between waking activity and sleep is weak (WU et al. 2008). Some of the lines screened in our study have been associated with effects on starvation resistance (HARBISON et al. 2004); we speculate that genes affecting a physiological measure of this sort are more likely to affect both sleep and waking activity.
The mutational correlation between triglycerides and glycogen has been previously assessed in male flies and was high and positive (CLARK et al. 1995). In our study, high positive correlations between triglycerides and glycogen were also found for both males and females. Yet the genetic correlations between males and females for energy stores were relatively low, implying that genes affecting energy stores are different in males and females (supplemental Table 3). Recent QTL studies support this notion, as sex-specific QTL for triglycerides (DE LUCA et al. 2005) and X-linked QTL for glycogen (MONTOOTH et al. 2003) have been identified.
Mutations that affect endogenous sleep affect body metabolic stores as well. Male flies had positive genetic correlations between glycogen and sleep time. This would suggest that genes involved in male sleep behavior impact glycogen synthesis, storage, or expenditure. Glycogen stores in males were also negatively correlated with sleep bout number and positively correlated with sleep bout length. Taken together, these data imply that genes affecting glycogen levels in males affect total sleep time and sleep consolidation. This finding contrasts with that for females, whose glycogen stores were not correlated with any measure of sleep time; instead, triglycerides were negatively correlated with 24-hr and nighttime sleep bout number and positively correlated with average bout length, indicating that triglycerides are associated with sleep consolidation. For females, therefore, triglyceride levels may have a more critical relationship to sleep patterns, while male sleep is linked to glycogen.
Our correlation data suggest that the differences in male and female sleep patterns may be related to sex-specific differences in metabolic needs (MONTOOTH et al. 2003; MORGAN et al. 2003; DE LUCA et al. 2005). The relatively high genetic correlations that we observed between sleep and energy stores may reflect the fact that large metabolic changes occur with high probability with the introduction of single P-element insertions in flies, implying that large numbers of genes influence metabolism (CLARK et al. 1995). We found that our correlations were driven by insertions having effects that were greater than or less than the 95% confidence interval. Few insertions had large effects on both sleep and energy stores simultaneously, however. Thus, the high correlations that we observed may also indicate that large numbers of genes impact sleep behavior.
Our correlations suggest that increases in whole-body glycogen and triglycerides are associated with an increase in sleep or sleep consolidation. This is in contrast to the human association data mentioned previously, which generally indicate that decreased sleep is associated with obesity (reviewed in CIZZA et al. 2005), although several studies have noted a link between considerably longer sleep times and obesity (KRIPKE et al. 2002; TAHERI et al. 2004) and diabetes (PATEL et al. 2004). The decreased sleep in the human data could have resulted from chronic sleep deprivation rather than endogenous sleep need. Further studies will determine which genes play a combined role in endogenous sleep and energy stores, and whether or not these pathways have been conserved across taxa.
Pleiotropy has become a recurring theme in behavioral analyses. Four other studies examined these same P-element insertion lines for bristle number (NORGA et al. 2003), starvation resistance (HARBISON et al. 2004), olfaction (SAMBANDAN et al. 2006), and aggression (EDWARDS et al. 2006). Table 8 shows the overlap between candidate alleles for these traits, energy stores (this study), and the alleles identified for 24-hr sleep in this study. Table 8 provides clear evidence for pleiotropy as lines bearing the same mutation (i.e., alleles) have effects on as many as four different phenotypes. Additional evidence for pleiotropy stems from the genetic correlation analysis. The relatively high correlations observed between sleep phenotypes suggest that many of the same genes will affect different aspects of sleep. However, none of the correlations between sleep phenotypes are unity, implying that multiple molecular pathways impact sleep. Thus, when considering sleep, specificity of the candidate genes appears to be the exception, not the rule (GREENSPAN 2001). However, sleep can be dissected from other phenotypes in a pleiotropic gene. The escargot insertion lines suggest that both sleep and energy stores can be affected by different locations of the P element and that the effects of the insertion location can be sex specific. Thus, although many genes are pleiotropic, subtle mutations allow the identification and separation of polymorphisms that are specific to each phenotype (SOKOLOWSKI 2001). This separation was previously achieved for life-history traits in natural populations of D. melanogaster: independent polymorphisms in the gene Catsup have effects on life span, bristle number, and locomotion (CARBONE et al. 2006) and may be maintained by balancing selection. A similar situation between sleep and other important traits in natural populations, if present, would potentially explain why genetic variation for sleep exists.
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) (FALCONER and MACKAY 1996) on 24-hr sleep was quite large—21. This finding suggests the presence of a large mutational target for sleep; only a few such mutations of opposite effect would be needed to mask a more severe mutation, as was observed in the background of long-standing mutations in Shaker (CIRELLI et al. 2005a). This phenomenon may explain why the function of sleep, which up until recently was studied in largely wild-type populations of humans and mammals, remains elusive. |
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