Circadian Rhythms and Sleep in Drosophila melanogaster

Molecular mechanism of the clock

Transcription-based feedback loops:

The finding that per RNA and protein are expressed cyclically and rising levels of protein are associated with declining levels of the mRNA led to the postulate that the PER protein negatively regulates its own transcription to generate an autoregulatory circadian loop (Siwicki et al. 1988; Hardin et al. 1990; Zerr et al. 1990). Subsequent studies with tim supported this idea, showing that the two mRNAS cycle in phase and the PER and TIM proteins interact directly and affect their own transcription (Gekakis et al. 1995; Sehgal et al. 1995). The negative feedback loop thus generated constitutes the basis of overt rhythms in Drosophila. As described below, maintenance of the loop requires the activity of additional components (Figure 2).

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Figure 2

The molecular feedback loop is formed by the negative feedback of Period (PER) and Timeless (TIM) on their own transcription. Delays exist between transcription of per and tim mRNA and the localization of these proteins in the nucleus, where they can interact with transcriptional activators Clock (CLK) and Cycle (CYC). These delays are thought to be important for allowing the molecular clock to cycle with a period of ∼24 hr. Critical regulators have been identified at several steps of the cycle that are necessary for accurate timing and strength of molecular rhythms. Degradation of PER and TIM allows the cycle to start anew. Not pictured is the second feedback loop formed by PDP1 and Vrille, which produces cycling of Clk mRNA. This secondary feedback loop is thought to reinforce molecular oscillations, although cycling of the CLK protein is not necessary for rhythms. CKII, Casein Kinase II; SGG, shaggy; PP2A, Protein Phosphatase 2A; PP1, Protein Phosphatase 1; DBT, doubletime.

Levels of per and tim mRNA rise during the day and are highest in the early evening. At this time, the two proteins start to accumulate, initially in the cytoplasm, and then around the middle of the night in the nucleus (reviewed in Zheng and Sehgal 2012). TIM stabilizes PER in the cytoplasm, and is required to transport it to the nucleus. Nuclear localization of the two proteins is also regulated by specific importins (Jang et al. 2015) and appears to be temporally regulated (Curtin et al. 1995; Meyer et al. 2006). Nuclear localization of PER and TIM coincides with the decline of their mRNA levels due to negative autoregulatory feedback by the proteins. PER and TIM cannot bind DNA, but regulate transcription by inhibiting their transcriptional activators, Clock and Cycle. Although the mechanisms of negative feedback are not completely understood, biochemical data support sequestration of the CLK–CYC complex from DNA by PER and a model where PER recruits the kinase DBT (a CK1ε homolog) to promote CLK phosphorylation (Lee et al. 1999; Kim and Edery 2006; Kim et al. 2007; Nawathean et al. 2007). The role of TIM in negative feedback is less clear. In the early morning, TIM levels decline sharply, and, by midafternoon, PER expression is also greatly diminished, lifting the negative feedback and allowing a new cycle of transcription.

In addition to the major feedback loop described above, the Drosophila clock contains a second loop interlocked with the first. In this loop, the CLK–CYC complex drives sequential expression of a transcriptional activator, PDP1, and a repressor, Vrille (VRI), of Clk expression (Cyran et al. 2003; Glossop et al. 2003). By feeding back on Clk expression in a rhythmic fashion, PDP1 and VRI maintain rhythmic expression of Clk mRNA. However, given that CLK protein levels do not cycle, the purpose of the mRNA cycling is unclear (Houl et al. 2006). It is thought that the second loop stabilizes the system and provides greater precision (Cyran et al. 2003; Glossop et al. 2003). Other proteins, such as Clockwork Orange, KAYAK-α, and E75, are also implicated in transcriptional control (Kadener et al. 2007; Lim et al. 2007; Matsumoto et al. 2007; Ling et al. 2012; Kumar et al. 2014).

The clock cell network

Behavioral rhythms of rest:activity require clock activity in the brain, which was suggested even by early experiments of Konopka in which he restored short period rhythms in per null flies by transplanting brains of per^short mutants into the abdomen—an experiment that suggests some secreted humoral signal from the brain as a final output from the clock, which has not yet been identified (Handler and Konopka 1979). In Drosophila, the core molecular clock components are coexpressed only in a restricted set of ∼150 neurons, which serve a function similar to the mammalian superchiasmatic nucleus (SCN) in regulating circadian rhythms in behavioral activity. These neurons are clustered into discrete groups: specifically three groups of dorsal neurons (DN1, 2, and 3) and four groups of lateral neurons (LNs) (Figure 3). The lateral neurons consist of two ventral clusters—large and small ventrolateral neurons (lLNvs and sLNvs)—one group of dorsolateral neurons (LNds), and the lateral posterior neurons (LPNs). There is heterogeneity both between and within clusters; like cells in the mammalian SCN (reviewed in Welsh et al. 2010; Bedont and Blackshaw 2015), these cells express different neurotransmitters, neuropeptides, and receptors, and serve different functional roles, as discussed below. Clock cells also exhibit cycling neuronal activity (Cao and Nitabach 2008; Sheeba et al. 2008b; Flourakis et al. 2015), and the peak of neuronal activity, as reflected by intracellular calcium levels, occurs at different phases for different groups (Liang et al. 2016). However, in wild-type flies kept in conditions that approximate the natural world (including LD and early DD), the core molecular clocks in nearly all groups of clock neurons cycle approximately in phase with each other (Yoshii et al. 2009a; Roberts et al. 2015). The mechanisms through which the cycling of the molecular clock generates complex and flexible behavioral outputs are thus probably related to these other properties of the clock circuit, rather than to differences in the cycling of the molecular clock itself.

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Figure 3

Clock cells, which express the core components of the molecular clock, are depicted on the right. These cells are interconnected and heterogeneous between and within clusters, allowing cells to serve different functions, and respond to different environmental conditions. On the left, two groups of output neurons that do not express the molecular components of the clock, but have cycling neuronal activity and are important for behavioral activity rhythms, suggesting clock input. DN, dorsal neurons; LN, lateral neurons (lLNv, large ventral lateral neurons; sLNv, small ventral lateral neurons; LNd, dorsal lateral neurons); LPN, lateral posterior neurons; LHLK, lateral horn leucokinin neuron; DH44, Diuretic Hormone 44.

Entrainment

To act as reliable time-keepers, clocks must not only maintain a 24 hr rhythm, but also entrain to environmental cues. Light is the dominant entraining stimulus (zeitgeber) for circadian rhythms, although temperature can also entrain clocks in both cold-blooded animals and in mammals. Clock adaptation to zeitgebers can be studied by measuring behavioral and molecular adaptation to a shift in the timing of a zeitgeber, or by studying the effect of a brief pulse of a zeitgeber at an unexpected time of day. The latter approach, used with light or heat, will either accelerate or delay the phase of clock cycling and behavior, depending on when the pulse is delivered. These responses can be summarized by a phase-response curve (PRC), which shows the magnitude of phase shifting when the zeitgeber is delivered at different times of day. Clocks can be entrained to environmental zeitgebers either cell-autonomously or through neural circuits. TIM is rapidly degraded in response to light pulses, and is thought to confer environmental cues to the rest of the molecular clock (Hunter-Ensor et al. 1996; Myers et al. 1996; Zeng et al. 1996). Interestingly, there is now evidence that both cell autonomous and non-cell-autonomous mechanisms for entrainment in Drosophila converge on TIM degradation as a mechanism of resetting the clock.

Peripheral clocks

Rhythms are observed not just in locomotor behavior, but in many physiological processes throughout the body, and these rhythms rely on the brain clock to various extents. Rhythms of eclosion are mediated by a clock in the prothoracic gland (PG), located in the thoracic region of the developing pupa (Myers et al. 2003). This gland produces the hormone ecdysone, which declines in levels, presumably in response to circadian signals, just prior to eclosion. Although weak autonomous oscillations of clock genes in PG cells can be seen in the absence of brain input, input from the central nervous system (dependent on PDF and LNs) dramatically amplifies and synchronizes oscillations in this tissue (Myers et al. 2003; Morioka et al. 2012). On the other hand, a clock in the Malphigian Tubules, equivalent to the kidney, appears to be entirely autonomous and directly entrained by light, presumably by CRY expression in this tissue (Giebultowicz et al. 2000). The Drosophila fat body, which serves functions of the liver and adipose tissue, does not require the brain clock for molecular clock cycling in LD cycles, but dampens rapidly in constant darkness in the absence of PDF (Erion et al. 2016). Providing an additional layer of complexity, only some cycling transcripts in the fat body rely on the fat body clock, while others are driven by secreted signals from neuronal clocks. Finally, PDF also influences the phase and period of the clock in oenocytes, cells responsible for sex pheromone production in insects, although, curiously, PDF and PDFR have opposite effects on the speed of the clock in these cells (Krupp et al. 2013).

Measuring sleep

Based on initial studies of arousal threshold, sleep in Drosophila is commonly defined as a period of inactivity lasting 5 min or longer (Shaw et al. 2000; Huber et al. 2004; Andretic and Shaw 2005). Sleep is typically monitored through the same Drosophila Activity Monitoring System (DAMS) used to analyze circadian behavior. This system relies on an active fly crossing the center of the locomotor tube to break the infrared beam passed across the middle, but this system is generally sufficient to differentiate sleep from activity in young, healthy flies, where activity levels are high enough that it is unlikely that a 5-min or greater period of inactivity would be recorded by chance.

In old or sick flies with reduced overall activity, it may be useful to use a more sensitive method of evaluating sleep behavior. There is also the possibility that extended feeding behavior, in which a fly would dwell at the end of the tube with food and fail to cross the center beam, could be misconstrued as sleep (discussed in Cavanaugh et al. 2016). There are two alternatives to traditional single-beam DAMS monitors that can be used to address these concerns. Multi-beam DAMS monitors, where 17 infrared beams along the length of a locomotor tube are used to monitor activity, provide a similar environment to the traditional locomotor tube set up but offer increased sensitivity (Garbe et al. 2015). Video monitoring systems have also been set up to monitor the activity of individual flies (Zimmerman et al. 2008; Donelson et al. 2012; Gilestro 2012; Faville et al. 2015; Garbe et al. 2015). Video monitoring systems, while potentially offering increased sensitivity, also present a difficulty in that no standard for the sensitivity to motion for these systems has been agreed upon. A very sensitive system may detect leg twitches or imaging artifacts during sleep and inappropriately read these as activity. Video monitoring could also introduce another potential confounding factor if it uses small arenas instead of the typical locomotor tube, as this can result in differences in behavior (Garbe et al. 2015). Thus, while different results can sometimes be observed between video systems and traditional DAMS monitors, these results should be interpreted with caution.

When observing a fly with reduced or elevated overall levels of sleep, it can be conceptually useful to determine how sleep bout architecture is changed (Andretic and Shaw 2005). For example, short-sleeping mutants may initiate fewer bouts of sleep, or may be unable to maintain sleep over long bouts, which implies different mechanisms of action for these genes. Most software used for automated analysis of sleep behavior allows for study of sleep bout architecture in addition to total sleep time.

Sleep depth is an additional dimension of sleep that DAMS monitoring alone does not detect, although automated systems to probe sleep depth have been developed (Faville et al. 2015). While initial characterizations of sleep depth suggested that sensory unresponsiveness plateaus after 5 min of inactivity, subsequent studies have demonstrated that sleep depth varies predictably over longer bouts of sleep as well. Troughs in arousability have been observed after 15 and 30 min of sleep, and protocerebral local field potential recordings show variation in neuronal activity based on length of sleep bouts, in some ways resembling the changes in sleep depth (“sleep stages”) that occur during bouts of sleep in mammals (van Alphen et al. 2013). Depth of sleep also differs between day and night, such that daytime “siesta” sleep in flies is generally a lighter sleep state. Increased sleep depth is also a component of the homeostatic response to sleep deprivation (Huber et al. 2004; van Alphen et al. 2013; Dubowy et al. 2016), and mutations can affect sleep depth in ways that would not be predicted by changes in sleep amount (Faville et al. 2015).

Genetic tools for sleep research

Sleep research in Drosophila, like a lot of molecular neuroscience in this model organism, has drawn heavily on both the study of mutations that lead to aberrant sleep behavior, as well as the use of a genetic toolkit for manipulating neuronal activity. One successful strategy for identifying novel regulators of sleep is to conduct forward genetic screens for mutants with very extreme phenotypes. Another strategy is manipulation of different neuroanatomic loci labeled by Gal4 drivers by activating or suppressing neuronal firing. Researchers can use a variety genetic tools to manipulate neuronal activity. The bacterial sodium channel NaChBac (Luan et al. 2006; Nitabach et al. 2006) and the potassium channel Kir2.1 (Baines et al. 2001) can be driven either throughout fly development or in a time-restricted manner using inducible binary expression systems to activate or silence cells, respectively. Thermogenetic tools, such as the heat-activated depolarizing channel TrpA1 (Hamada et al. 2008; Parisky et al. 2008), or the temperature-sensitive dominant negative allele of shibire used to block synaptic transmission (Kitamoto 2001), as well as optogenetic tools, such as the light-activated depolarizing CsChrimson channel (Klapoetke et al. 2014) are also frequently used for conditional manipulation of neurons.

In many cases, gene- and circuit-based approaches intersect. Many, though not all, genes that regulate sleep have been shown to function in specific neuroanatomic loci, in some cases identifying novel sleep regulating areas of the fly brain. Important advances have also come from studying interactions between genes that regulate sleep, as well as studying genes that produce sleep phenotypes and have unknown or unappreciated roles in controlling neuronal activity. Studying sleep in Drosophila then not only leads to insight into sleep-regulatory mechanisms that may extend to mammals, but also identifies novel regulators of neuronal function, and provides new insight into brain signaling and metabolism. In this review, we present a thorough discussion of the genetics and neuroanatomy of sleep, with an emphasis on how sleep-regulating genes act in the context of sleep-regulating brain regions, and how different sleep regulating genes and brain areas interact with each other.

Sleep regulation through global modulation of neuronal activity

The Shaker potassium channel (Cirelli et al. 2005; Bushey et al. 2007) and its modulator sleepless (Koh et al. 2008) were two early hits with extreme short-sleeping phenotypes from large-scale genetic screens. Both genes are expressed throughout the fly brain (Wu et al. 2009), and neither of these phenotypes has been fully mapped to specific neuroanatomic loci, suggesting that they exert widespread effects on brain activity or metabolism that feed back onto sleep regulation. Shaker is a voltage-gated potassium channel involved in membrane repolarization. sleepless is a Ly6 neurotoxin-like molecule that, in the years since its discovery as a sleep regulator, has been found to promote Shaker expression and activity and inhibit nicotinic acetylcholine (nAChR) function, such that loss of sleepless might lead to increased neuronal activity through multiple mechanisms (Wu et al. 2009; Shi et al. 2014; Wu et al. 2014). The molecular functions of these genes therefore suggest a mechanism of sleep regulation where wakefulness is produced by broadly increasing neuronal excitability. Indeed, broadly inhibiting cholinergic transmission partially suppresses both the Shaker and sleepless phenotypes, and RNAi knockdown of the nAChRα3 subunit suppresses the sleepless phenotype (Wu et al. 2014). However, recent work has revealed a more complicated role for these genes. Although it has typically been assumed that the Shaker phenotype results from increased neuronal activity of wake-promoting cells, a recent study found that knocking down Shaker in sleep-promoting cells actually lengthens the inter-spike interval and reduces neuronal activity in these populations to favor wake (Pimentel et al. 2016). Another study found that, in contrast with the generally wake-promoting effects of cholinergic neurotransmission in the fly brain (Wu et al. 2014; Seidner et al. 2015), a specific nAChR subunit, redeye, is strongly sleep-promoting (Shi et al. 2014). Genetic evidence suggests that sleepless also interacts with the redeye subunit, in this case acting as a wake-promoting rather than sleep-promoting molecule, consistent with sleepless inhibiting nAChRs regardless of subunit composition. Recent studies of sleepless have also suggested that it in part regulates sleep by non-cell-autonomously promoting metabolism of GABA in glia, perhaps also through its effect on neural activity (Chen et al. 2014; Maguire et al. 2015). Shaker and sleepless thus both seem to interact in a nonstraightforward way with sleep-regulatory genes and cells in the nervous system, and work with sleepless suggests a potential connection between neuronal activity and metabolism of neurotransmitters, although the details of this connection remain unclear.

The mushroom body

The mushroom body is the center of olfactory memory in the fly brain and as a result of years of intense research, there is detailed anatomic and functional data available for mushroom body circuits (reviewed in Guven-Ozkan and Davis 2014; Owald and Waddell 2015). The mushroom body consists primarily of ∼2000 Kenyon cells, most of which receive input from an average of six stochastically connected projection neurons, with each projection neuron encoding input from a single type of odorant receptor neuron. Each Kenyon cell projects axons to a subset of lobes of the mushroom body, forming three classes: those that project to the α and β lobes, the α’ and β’ lobes, or the γ lobe. Within each lobe there exist several compartments, defined by the dendrites of different mushroom body output neurons (MBONs) and axonal projections of different dopaminergic neurons, which respond to aversive or appetitive unconditioned stimuli like electric shock or sugar. At least some MBONs have an inherent valence, which is correlated with neurotransmitter expression; flies will act to avoid optogenetic activation of aversive glutamtergic MBONs, and act to prolong activation of attractive MBONs, which can be cholinergic or GABAergic (Aso et al. 2014b). A simple model of learning and memory in the mushroom body posits that within a mushroom body compartment, the strength of the synapses between Kenyon cells, which encode odor, and MBONs, which encode valence, is modulated by dopaminergic neurons in response to pairing of an odor with an aversive or appetitive stimulus (Owald and Waddell 2015). MBONs project to largely uncharacterized protocerebral areas of the fly brain (Aso et al. 2014a). In addition to the neurons described above, mushroom bodies also receive octopaminergic input, and are innervated by the dorsal paired medial (DPM), anterior paired lateral (APL), and dorsal anterior lateral (DAL) pairs of neurons (Guven-Ozkan and Davis 2014). These neurons may serve functions in memory consolidation or in fine-tuning olfactory coding.

The mushroom body was also the first neuroanatomic structure identified as a regulator of sleep in Drosophila (Joiner et al. 2006; Pitman et al. 2006). Conditional approaches were used to block synaptic transmission, perturb PKA signaling, or manipulate excitability of mushroom body neurons, primarily using the relatively broad Gal4 drivers that were available at the time, but also taking advantage of methods that could target the mushroom body specifically. These approaches suggested that the mushroom body contains both sleep-promoting and wake-promoting cells: for example, flies slept less when hydroxyurea was used to ablate α/β and α’/β’ mushroom body lobes, suggesting a sleep-promoting role for these cells, but slept more when a relatively restricted mushroom body GeneSwitch line was used to silence specific cells in adulthood.

Later work using more restricted split-Gal4 lines identified specific mushroom body circuits that underlie both wake- and sleep-promoting effects (Aso et al. 2014b; Sitaraman et al. 2015a). Several MBONs are capable of regulating sleep behavior, and interestingly these same MBONs are also necessary for certain types of learning and memory (Aso et al. 2014b). The sleep- and wake-promoting characteristics of MBONs seem to correlate with their aversive or appetitive nature, such that the two identified wake-promoting groups of MBONs are aversive and glutamatergic, whereas two identified sleep-promoting MBONs are appetitive and cholinergic or GABAergic; an unusual MBON with dendritic projections in the calyx and no identified neurotransmitter or valence is also sleep-promoting (Aso et al. 2014b). Wake- and sleep-promoting characteristics of different Kenyon cell populations then seem to reflect which of the sleep-controlling MBONs the Kenyon cells in question target most prominently (Sitaraman et al. 2015a). For example, neural epistasis experiments suggest that the wake-promoting glutamatergic MBONs (γ5β’2a/β’2mp’/β’2mp_bilateral) are downstream of a wake-promoting α’β’ KC driver and a broad wake-promoting KC driver that encompasses γ-dorsal, γ-main, and α/β KCs (γ-dorsal KCs are sleep-promoting on their own, but γ-main KCs are wake-promoting). On the other hand, blocking the sleep-promoting cholinergic γ2α’1 MBONs makes the wake-promoting effects of the broad KC driver that encompasses γ-dorsal, γ-main, and α/β KCs even stronger, suggesting that γ2α’1 MBONs receive sleep-promoting input from these cells, even if the net effect of the KC driver conferred by other downstream MBONs is wake-promoting. Likewise, the DPM neurons, which are proposed to provide inhibitory input to α’β’ KCs via GABA and/or serotonin, are strongly sleep-promoting when activated, consistent with an overall wake-promoting effect of α’β’ KCs (Haynes et al. 2015). Loss of the d5-HT1 serotonin receptor in mushroom bodies also produces a weak short-sleep phenotype, which can be rescued with expression driven by the MB-GeneSwitch driver (Yuan et al. 2006). This finding is consistent with serotonin released from DPMs acting to inhibit wake-promoting α’β’ KCs.

Dopamine and the dorsal fan-shaped body

Perhaps the strongest parallel between mammalian and Drosophila sleep regulation identified so far is the strong wake-promoting effects of the monoamine neurotransmitters dopamine and octopamine (the insect homolog of norepinephrine, discussed in the next section). fumin, one of the first short-sleeping mutants identified, is a mutation in a dopamine transporter that presumably results in elevated dopamine levels throughout the fly brain (Kume et al. 2005), and dopaminergic neurons are strongly wake-promoting when activated (Shang et al. 2011; Liu et al. 2012). Conversely, mutants deficient for the CNS-specific isoform of tyrosine hydroxylase (TH), the rate-limiting enzyme for dopamine synthesis, have increased sleep throughout the day (Riemensperger et al. 2011).

One site of dopaminergic action is the central complex, an area of the brain that has been hypothesized to serve a basal ganglia-like function in action selection based in part on the input it receives from protocerebral areas and its functional role in motor output (Strausfeld and Hirth 2013). Thermogenetic activation of the ExF/2 neurons in the dorsal fan-shaped body, a region of the central complex, is strongly sleep-promoting (Donlea et al. 2011). Sleep deprivation changes the electrophysiologic properties of these neurons to favor activity, suggesting they may play a role in output of homeostatic sleep signals (Donlea et al. 2014). Dopamine provides a wake-promoting stimulus by silencing these neurons, although there is some disagreement regarding the relevant cluster of dopaminergic neurons as well as the relevant D1-like dopamine receptor. A MARCM approach to target single dopaminergic neurons indicated that individual PPM3s with projections to the fan-shaped body exert small but significant effects on sleep behavior, while a separate study comparing expression of wake-promoting and non-wake-promoting Gal4 drivers suggested that PPL1s with projections to the fan-shaped body provide wake-promoting input (Liu et al. 2012; Ueno et al. 2012). It is possible that both groups of cells provide wake-promoting dopaminergic input to this brain area. Likewise, it was initially thought that Dop1R1 was the relevant receptor for wake-promoting dopaminergic signaling in the brain. Dop1R1 mutations suppress the fumin phenotype, and, unlike wild-type flies, Dop1R1 mutants do not experience severe sleep reduction when fed L-DOPA (Liu et al. 2012; Ueno et al. 2012). These effects can be rescued with Dop1R1 expression driven by the relatively specific fan-shaped body driver 104y-Gal4. However, more recent work shows that RNAi knockdown of the related receptor Dop1R2 in the dorsal fan-shaped body ExF/2 neurons is sufficient to prevent both short-term hyperpolarization and longer-term silencing of these cells by dopamine, and this manipulation also produces a long-sleep phenotype (Pimentel et al. 2016).

Dopaminergic neurons with projections to the mushroom body also have wake-promoting effects (Sitaraman et al. 2015b; Nall et al. 2016). Neurons of the PAM cluster, as well as a subset of neurons of the PPL1 cluster distinct from those that project to the fan-shaped body, project to specific compartments of the mushroom body (MB). Recent work has suggested that MB-PAM neurons and MB-PPL1 neurons can be wake-promoting when thermogenetically activated. The wake-promoting effects of caffeine are also mediated by the PAM cluster of neurons (Nall et al. 2016). However, although Split-Gal4 drivers have been used to elegantly identify specific mushroom body circuits that control sleep, the PAM and PPL1 neurons that promote wake do not seem to neatly correspond to these circuits (Sitaraman et al. 2015b). It is possible that diffusion of dopamine or functional interconnectivity between dopaminergic neurons (Cohn et al. 2015) contributes to these results.

Genetic knock-outs and experiments silencing dopaminergic neurons show that endogenous dopamine plays an important role in daily sleep regulation; however, it is interesting that increases in global dopamine levels can be compensated with loss of the Dop1R1 receptor to achieve approximately normal amounts of daily sleep (Ueno et al. 2012). Thermogenetic activation of dopaminergic neurons produces a sleep rebound once activation is stopped, suggesting that these wake-promoting neurons are upstream of neuronal machinery capable of producing homeostatic responses to extended wakefulness (Seidner et al. 2015; Dubowy et al. 2016). Alterations in dopamine signaling are also implicated in sleep regulation by developmental or environmental cues; the increased sleep amounts that young flies experience have been attributed to decreased dopaminergic input to ExF/2 neurons, and dopamine has also been proposed to play a role in the adaptation of sleep amount to changing social environments (Ganguly-Fitzgerald et al. 2006; Kayser et al. 2014). In addition to inhibitory, wake-promoting input from dopamine, ExF/2 neurons may also receive input from unidentified sleep-promoting neurons labeled by the 201y-Gal4 driver (Cavanaugh et al. 2016). Thus, the dorsal fan-shaped body is well-positioned to act as an integrator and output for many sleep-regulatory signals.

In addition to fumin, other short-sleeping mutants also appear to depend on dopamine or the fan-shaped body for their mechanisms of action. The Rho-GAP crossveinless c is a sleep-promoting molecule that disrupts the physiological membrane properties of the ExF/2 neurons when mutated, resulting in reduced sleep (Donlea et al. 2014). The 2-pore potassium channel Sandman is necessary for dopamine-mediated silencing of these neurons, and knockdown of this channel in these neurons also produces a short sleep phenotype (Pimentel et al. 2016). The spatial requirements for the sleep-promoting ubiquitin ligase component Cullin 3, and its interacting BTB adaptor insomniac have not been established (Stavropoulos and Young 2011), but pharmacologically blocking dopamine synthesis blocks the short-sleeping phenotypes of these mutants, suggesting that Cullin 3-mediated protein turnover and dopamine signaling may interact to regulate sleep (Pfeiffenberger and Allada 2012).

Octopamine, the pars intercerebralis, and the pars lateralis

Octopamine, the insect homolog of norepinephrine, is another wake-promoting monoaminergic neurotransmitter (Crocker and Sehgal 2008). Mutating the enzymes responsible for octopamine synthesis or silencing octopaminergic neurons increases daily sleep amount, while activating octopaminergic neurons or feeding flies octopamine decreases sleep (Crocker and Sehgal 2008; Seidner et al. 2015). Although octopamine provides input to the mushroom body, the wake-promoting effects of octopamine do not appear to be mediated by this structure. Instead, a MARCM approach identified the octopaminergic ASM neurons, which project to the pars intercerebralis (PI), as sufficient to drive increased wake when chronically activated, and the PI insulin-like peptide (ILP)-secreting neurons as downstream mediators of octopamine signaling (Crocker et al. 2010). The effect sizes observed when ASM or ILP-secreting neurons are manipulated are somewhat smaller than those observed with manipulation of all octopaminergic neurons, so it is possible that other neurons important for the wake-promoting effects of octopamine have not yet been found. Unlike dopaminergic neurons, activating octopaminergic neurons produces strong sleep loss without an apparent rebound the next day, suggesting that octopaminergic neurons provide a wake-promoting stimulus that is able to circumvent sleep homeostasis (Seidner et al. 2015). This work suggests that octopaminergic neurons may be a neural substrate for environmental factors that promote wake without any apparent homeostatic compensation.

In addition to the ILP-expressing neurons, distinct sets of neurons in the PI expressing EGFR ligands and SIFamide are also sleep-promoting (Foltenyi et al. 2007; Park et al. 2014). rhomboid (rho), an enzyme necessary for the production of EGFR ligands, is expressed prominently in the PI, and manipulating its activity using Gal4 drivers with expression in the PI produces sleep when rho is overexpressed, and wake when rho is knocked down (Foltenyi et al. 2007). SIFamide is a sleep-promoting insect neuropeptide expressed in four PI neurons; ablation of these neurons or knockdown of the peptide with RNAi decreases sleep (Park et al. 2014). Both rho and the SIFamide receptor (SIFaR) are required in c767-Gal4 labeled neurons for normal sleep amounts, suggesting that SIFamide acts through inter-PI signaling, and implicating EGFR ligands as a possible output from this circuit (Foltenyi et al. 2007; Park et al. 2014). However, c767-Gal4 also drives expression outside the PI, and so a function of these molecules outside the PI cannot be excluded. EGFR signaling in clock neurons may mediate the effects of social enrichment on sleep, although it is not clear if this is functionally related to the release of EGFR ligand from the PI (Donlea et al. 2009).

A separate, but related neuroendocrine structure, the pars lateralis (PL) (de Velasco et al. 2007), was recently identified as a site of action for cell cycle regulators that modulate sleep in adult postmitotic neurons. Two cell cycle regulators, Rca1 (Regulator of Cyclin A) and taranis (tara, a Trip-Br family transcriptional coregulator), were independently identified in genetic screens for short-sleeping mutants, and, following the identification of Rca1, it was found that knocking down Cyclin A (CycA) itself in neurons produces an equally strong short-sleeping phenotype (Rogulja and Young 2012; Afonso et al. 2015). CycA and tara genetically interact, and TARA binds to and post-transcriptionally promotes stable expression of Cyclin A in PL neurons (Afonso et al. 2015). Postmitotic expression of Cyclin A is relatively restricted in the fly brain, but includes ∼14 neuroendocrine cells in the pars lateralis, and knocking down tara in this structure partially recapitulates the short sleeping phenotype of tara mutants. Experimentally activating and silencing these neurons supports a wake-promoting role. No mechanism has yet been identified for the involvement of these cell cycle regulators in neuronal activity or sleep, but this will be an interesting area of future research.

Clock regulation of sleep

The circadian clock is essential for restricting sleep to environmentally advantageous times of day. A role for the circadian clock has been established in flies in both putting flies to sleep at night once the dark period has begun, and waking them up in advance of dawn (Kunst et al. 2014; Liu et al. 2014). Interestingly, these pathways seem to mechanistically diverge, suggesting that circadian regulation of sleep is not driven by continuous oscillation of a single sleep- or wake-promoting factor, but is rather driven by time-of-day specific modulation of distinct sleep- and wake-promoting mechanisms. Clock cells also have broader noncircadian roles in sleep regulation as mediators of the effects of temperature and social enrichment on sleep (Donlea et al. 2009; Parisky et al. 2016).

One particularly well-studied mechanism of circadian sleep regulation regulates sleep around the time of lights-off, and is driven by cyclic expression of a gene that regulates responsiveness to neuronal signals in a specific set of clock neurons. The large ventral lateral neurons (lLNvs) are a wake-promoting population of neurons with neuronal activity that fluctuates over the course of the day, such that firing frequency is reduced around the time of lights-off as well as later in the night in an LD cycle (Cao and Nitabach 2008; Parisky et al. 2008; Shang et al. 2008; Sheeba et al. 2008a,b; Liu et al. 2014). Manipulations of lLNv activity produce broad effects on sleep and wake throughout the day, but effects are particularly pronounced at night, with clear effects on length to sleep onset (sleep latency) after lights-off. Genetic and pharmacological studies suggest that the silencing of these neurons during this time is mediated by GABA-A receptor Rdl, and, indeed, broadly silencing GABAergic neurons in the brain substantially lengthens the sleep latency after lights-off in flies (Agosto et al. 2008; Parisky et al. 2008; Chung et al. 2009). The positive and negative arms of the molecular clock also oppositely regulate sleep latency, such that Clock and cyc mutants have increased sleep latency after lights-off, while per and tim mutants have shortened sleep latency after lights-off (Liu et al. 2014). Thus, changes in activity in this circuit seem to drive sleep in response to time of day around the transition to darkness.

A key molecular mediator of these changes in activity was initially discovered in a genetic screen for short-sleeping mutants. Although wide awake (wake) mutant flies were found to have reduced sleep across the day and night, which may be due to activity of wake in other neuroanatomic loci, the increased latency to sleep in wake mutants could be anatomically mapped to the lLNvs (Liu et al. 2014). Transcription of wake was found to cycle in the lLNvs, with increased transcription and protein levels at dusk, and WAKE physically and genetically interacts with the Rdl GABA-A receptor. Crucially, wake mutants did not display rhythms in lLNv firing frequency, and GABA-induced inhibitory currents in lLNvs were dampened. Thus, clock-driven expression of wake in lLNvs appears to be the key time-of-day driven regulator that induces sleep after lights-off.

A distinct mechanism in a different set of clock neurons is invoked to promote wake at the end of the night, just before lights-on. Diuretic Hormone 31 (DH31) is a wake-promoting neuropeptide expressed in the DN1 clock neurons, and manipulating its expression in these cells produces sleep phenotypes specifically from ZT21-24; knockdown of the peptide in DN1s increases sleep during this time period, while overexpression of the peptide in these cells decreases it (Kunst et al. 2014). Expressing a tethered PDF peptide in the DN1s, which should produce PDFR activation in these cells, also reduces sleep specifically during late night, as does pan-neuronal expression of tethered DH31. The time-specific effects of DH31 might therefore be gated both by time-specific PDF responsiveness in DN1s, and by time-specific DH31 responsiveness in downstream neurons.

However, DN1s are sleep-promoting at other times of day; optogenetically or thermogenetically activating these cells increases daytime sleep, suppressing the normal “evening” peak of activity, while silencing them decreases sleep during early night (Guo et al. 2016). The sleep-promoting effects of DN1s during the day can be blocked with RNAi targeting mGluR in “E” cells, suggesting “E” cells might also have a role in sleep regulation. The role of sleep-promoting signals from DN1s in normal daily sleep regulation remains unclear, although the authors propose that variations in activity of DN1s may explain sexually dimorphic sleep patterns and regulation of sleep by high temperature.

Metabolic regulation of sleep

Food availability is a potent environmental regulator of sleep in fruit flies. Starvation strongly suppresses sleep, perhaps so that flies can devote more time to foraging for food (Keene et al. 2010; Thimgan et al. 2010). Mechanisms that regulate sleep at baseline and in response to food availability have some overlap with pathways that regulate metabolic energy storage, but these pathways are ultimately separable, such that sleep phenotypes do not depend on differences in metabolic stores (Erion et al. 2012; Masek et al. 2014; Murakami et al. 2016). Pharmacological evidence suggests that the suppression of sleep in response to starvation can be mimicked by feeding flies a glycolysis inhibitor, but not an inhibitor of fatty acid β-oxidation, suggesting that the suppression of sleep with starvation is related to reduced metabolic mobilization of glucose, not the taste of sugars or to lipid metabolism (Murakami et al. 2016).

An essential molecular mediator for these effects was recently identified in the nucleotide binding protein translin (Murakami et al. 2016). translin is highly upregulated upon starvation, and translin knockdown completely prevents starvation-induced sleep loss in flies. However, other sleep and starvation-related behaviors, such as sleep at baseline, sleep after sleep deprivation, preference for sucrose or yeast after starvation, and the proboscis extension reflex following starvation, are completely unaffected, and there is no evidence that translin knockdown alters energy stores. The effects of this molecular mediator were mapped to neurons expressing the neuropeptide leucokinin. Like translin knockdown, silencing leucokinin neurons prevented sleep suppression in response to starvation.

Although pharmacology suggests that sleep suppression in response to starvation is related to glucose metabolism, and is mechanistically distinct from the response to mechanical sleep deprivation, which induces a homeostatic response, a separate body of work suggests that genes involved in lipid metabolism can specifically modulate the rebound response to mechanical sleep deprivation (Thimgan et al. 2010, 2015). However, a mechanism through which lipid metabolism modulates sleep following sleep deprivation and neuronal substrates of this process remain unknown, and it is still unclear whether lipid metabolic stores are directly related to these phenotypes, or whether lipid metabolism and sleep homeostasis share common pathways.

Homeostatic response to sleep deprivation

Sleep homeostasis ensures that flies sleep the proper amount by recovering lost sleep after periods of extended wakefulness. Sleep homeostasis is often conceptualized as a continuous build-up of sleep need over periods of wakefulness and dissipation over periods of sleep, such that the same mechanisms should be invoked both when flies are spontaneously waking and during periods of forced wakefulness (sleep deprivation). However, recent work in Drosophila has called this view into question.

A disconnect between regulation of sleep following spontaneous wakefulness and sleep following sleep deprivation is supported by a number of observations. While it is true that many short-sleeping mutants have impaired sleep rebound, these phenotypes may arise from the general inability to initiate or maintain sleep in these flies, such that even high sleep pressure cannot overcome these deficits; it is also difficult to interpret rebound data from short-sleeping flies because their habitual short sleep means they have less sleep to lose. Indeed, the converse relationship does not seem to hold: a number of genetic perturbations have been identified that specifically affect sleep after sleep deprivation with little to no effect on baseline sleep, suggesting that sleep following sleep deprivation is produced by an independent mechanism (Seugnet et al. 2011b; Seidner et al. 2015; Thimgan et al. 2015; Dubowy et al. 2016; Liu et al. 2016).

Likewise, it seems that the nature of sleep deprivation matters for the homeostatic response. Activating different populations of wake-promoting neurons in the fly brain produces varying amounts of rebound the following day, ranging from no rebound response at all, as is seen with activation of octopaminergic neurons, to a rebound that in some cases far exceeds the amount of sleep lost (Seidner et al. 2015; Dubowy et al. 2016). Different environmental stimuli used to keep flies awake can also produce the varying effects. Particularly strikingly, one group has reported that starving flies produces equivalent amounts of sleep loss as mechanical sleep deprivation without producing any observable sleep rebound (Thimgan et al. 2010). It is possible that even different mechanical sleep deprivation approaches invoke different neural pathways to keep flies awake, which may explain why so few mutants with impaired sleep rebound have been validated across laboratories.

Despite these challenges, there is a picture emerging about the relevant circuitry for sleep homeostasis. Groups of wake-promoting cells that do produce sleep rebound after activation include dopaminergic neurons, as well as at least one restricted set of cholinergic cells, which produce a particularly strong rebound with even short periods of activation (Seidner et al. 2015; Dubowy et al. 2016). In addition, electrophysiology suggests that the sleep-promoting dorsal fan-shaped body neurons have reduced input resistance and reduced membrane time constants, suggesting greater activity, following sleep deprivation (Donlea et al. 2014); as discussed previously, this brain area is well-positioned to act as an integrator or output for multiple sleep regulatory signals, including, it seems, the response to sleep deprivation. It has also been suggested that silencing MBON-γ2α’1 neurons can block sleep rebound, although the data do not exclude the possibility that this is due to a general wake-promoting effect of silencing MBON-γ2α’1 neurons during the early day when rebound occurs (Sitaraman et al. 2015a).

A recently identified element of sleep-regulatory circuitry with an apparently specific role in sleep homeostasis is the ellipsoid body R2 neurons (Liu et al. 2016). These neurons were initially of interest because they produce a persistent sleep-promoting signal when thermogenetically activated; while no changes in sleep are reported at the time of activation, which can be as short as 30 min, a dramatic rebound-like increase in sleep is observed for the next 12 hr. Structural plasticity in the R2 neurons seems to underlie the phenotype, as circuit-specific analysis of Bruchpilot expression showed greater synapse number and size for R2 neurons after sleep deprivation, and genetic manipulations that block this plasticity partially block sleep rebound. A neuronal epistasis experiment suggests that these cells are upstream (although not necessarily directly connected to) the dorsal fan-shaped body. The manipulations of R2 neurons that affect sleep rebound have no effect on sleep at baseline, however, again supporting the idea that regulation of the homeostatic response to sleep deprivation is mechanistically different from the regulation of baseline sleep.

Function of sleep

Sleep affects neurobehavioral performance across the animal kingdom, and flies are no exception. Sleep has a bidirectional relationship with learning and memory; sleep deprivation in adult flies has been shown to interfere with both short- and long-term memory, while inducing sleep allows memories to form in contexts where an experience would ordinarily be forgotten (Ganguly-Fitzgerald et al. 2006; Seugnet et al. 2008; Donlea et al. 2011; Berry et al. 2015; Dissel et al. 2015). Sleep loss also has consequences for social behavior in flies; in adult flies, acute sleep loss results in impaired aggressive behavior (Kayser et al. 2015). There also appears to be a critical window during development where sleep loss produces long-lasting deficits in courtship behavior and short-term memory that persist into adulthood (Seugnet et al. 2011a; Kayser et al. 2014). Precisely how these deficits arise, however, remains an outstanding question in the field.

One general line of thought supposes that there are brain-wide molecular pathways that are different during sleep and wake, and perturbed by sleep loss, that underlie these behavioral changes. Indeed, molecular characterization comparing sleeping, spontaneously waking, and sleep-deprived brains has found widespread differences in gene expression between different behavioral states (Cirelli 2006; Zimmerman et al. 2006; Williams et al. 2007). The types of changes observed appear to be conserved across organisms: broadly, genes involved in synaptic plasticity/function, cellular stress, and metabolism are affected by sleep and wake across species studied (Mackiewicz et al. 2009).

One hypothesis based on this data, put forth by Tononi and Cirelli (2006), proposes that global synaptic downscaling occurs during sleep to counteract overpotentiation that might occur during wake. Work from these authors shows evidence that, broadly and within specific circuits of the adult fly brain, synaptic markers increase after wake or sleep deprivation and decrease following sleep, suggesting changes in the number or size of synapses (Gilestro et al. 2009; Bushey et al. 2011). Several shared regulators of learning, synaptic plasticity, and sleep have been identified, but a direct link between synaptic plasticity and either sleep regulation or neurobehavioral performance has been difficult to establish (Bushey et al. 2009; Bai and Sehgal 2015; Robinson et al. 2016). In some cases, it seems that the effects of sleep and synaptic plasticity can in fact be separated; for example, in the learning-impaired mutant dunce, inducing sleep improves learning even though the global changes in synaptic markers typically associated with sleep are not observed (Dissel et al. 2015).

Another hypothesis states that sleep is a time where metabolic functions such as macromolecule biosynthesis can be carried out in the brain in the absence of the more urgent metabolic demands of waking. This may also explain why extended sleep loss results in induction of cellular stress genes. As with learning and synaptic plasticity, shared regulators of metabolic or cellular stress and sleep regulation or function have been identified (Shaw et al. 2002; Naidoo et al. 2007; Thimgan et al. 2010, 2015; Maguire et al. 2015), and flies increase sleep following a heat pulse that induces a cellular stress response (Lenz et al. 2015), but a direct link that would establish cellular metabolism as an essential function of sleep has not yet been shown.

Some progress in understanding neurobehavioral changes with sleep comes from examining specific neurotransmitter systems or circuits that are perturbed by sleep loss. In the case of learning deficits with sleep loss, performance can be restored by overexpressing Dop1R1 or pharmacologically promoting dopamine signaling (Seugnet et al. 2008). In the case of loss of aggression after sleep loss, feeding flies the dopamine precursor L-DOPA does not improve behavior, but instead an octopamine agonist is effective at restoring aggression (Kayser et al. 2015). Studying the mechanisms that allow increased sleep to promote memory have also yielded insights; in particular, recent work suggests that inducing sleep may promote the formation of an aversive olfactory memory by suppressing a dopamine-dependent “active forgetting” process that occurs when flies are awake and moving (Berry et al. 2015). Whether these changes in neurotransmitter pathways are downstream of global metabolic or plasticity pathways that are altered during sleep will be an interesting area of future research.

We thank Sehgal laboratory members for critical reading of the manuscript and helpful comments. The laboratory is supported by National Institutes of Health (NIH) grant R37NS048471. C.D. was supported by T32-MH017168 and T32-GM007229.