Sleep homeostasis in Drosophila: a window on the vital function of sleep
Abstract The function of sleep is one of the enduring mysteries of modern neuroscience. Despite years of concerted research, it is still unclear why all known animals undergo a regular period of unconsciousness and what function this fulfils that simply cannot be performed during wakefulness. Key to identifying this function will be an understanding of the sleep homeostat. This internal bookkeeper measures the need for sleep that accumulates during wakefulness and dissipates during sleep. It therefore directly measures the processes that require sleep. This essay presents evidence that the sleep homeostat in Drosophila induces sleep through the activation of a group of 24 neurons projecting to the dorsal fan-shaped body (dFB) and identifies several sources of input to these effector neurons. I propose that this discovery allows a crucial reframing of the mystery of sleep as a molecular question and suggest experiments by which the dFB neurons can be used to evaluate existing theories of sleep. While current evidence is insufficient to support any one particular theory, this essay argues that future work investigating how the dFB is activated will lead to an understanding of the processes that require sleep and, thus, provide a window on the vital function of sleep. Drosophila melanogaster, sleep homeostasis, sleep pressure, fan-shaped body, ellipsoid body, central complex Introduction Understanding the purpose of sleep has long been a goal of neuroscience. A regular period of unconsciousness renders animals vulnerable to predation and limits time available for profitable activity. However the essential brain function requiring such life-threatening inactivity remains elusive. Sleep is regulated by interacting circadian and homeostatic systems, in a two-process model of regulation (Borbély, 1982). The circadian system regulates the timing of sleep: ensuring wakefulness during the day and sleep at night in diurnal animals, and the converse in nocturnal animals. The homeostatic system tracks the need to sleep (sleep pressure) that accumulates over periods of wakefulness that is, dissipated by sleep. This ensures that sleep occurs after long periods of wakefulness and that extra sleep (rebound sleep) occurs to compensate for sleep deprivation. While the understanding of the circadian clock developed over the past fifty years has been a triumph of modern neuroscience (Konopka and Benzer, 1971; Özkaya and Rosato, 2012; Takahashi, 2015), clues as to the function of sleep will likely come from studying sleep homeostasis, which is much less well understood. Internal changes tracked by the sleep homeostat, and reset by sleep, must reflect sleep pressure; the processes that dissipate these changes during sleep must, therefore, reflect the essential function of sleep. Understanding sleep pressure in cellular and molecular terms will therefore provide insight into sleep’s overall function. Drosophila melanogaster represents an excellent model in which to study sleep. It shows both circadian and homeostatic sleep regulation (Hendricks et al., 2000); importantly sleep lost through mechanical deprivation is recovered through rebound sleep (Shaw et al., 2000; Huber et al., 2004). Sleep deprivation results in cognitive defects (Huber et al., 2004), as in mammals (Youngblood et al., 1997), and can cause death if extended (Shaw et al., 2002), while older Drosophila mirror humans and other mammals in having less sleep, with lower arousal thresholds and more frequent awakening (Vienne et al., 2016). These similarities suggest that the basic function of sleep will have been conserved across evolution and that examining the more tractable Drosophila model will provide insights into the function of mammalian and human sleep. Factors regulating sleep homeostasis One strategy to investigate sleep homeostasis is to investigate genes that give rise to sleep phenotypes when mutated or knocked out. This reveals several candidate factors involved in a range of neural processes. Examining flies with mutations that alter cAMP and CREB activity has shown that the level of cAMP signalling is inversely related to sleep duration (Hendricks et al., 2001), while knockout of enzymes involved in the catabolism of monoamines renders flies unable to effect rebound sleep after sleep deprivation (Shaw et al., 2000). Similar work has implicated factors including steroid hormones (Ishimoto and Kitamoto, 2010), ACh receptors (Shi et al., 2014) and sex peptide receptors (Oh et al., 2014) in sleep regulation. Therefore this strategy is generally effective in unearthing candidate factors involved in sleep regulation. However these candidates are unlikely to be particularly useful in the investigation of sleep homeostasis, as they fail to converge on a specific neuroanatomical locus or cellular mechanism. Processes such as cAMP signalling, ACh neurotransmission and steroid hormone signalling are common to many neurons and do not obviously identify a shared central mechanism of homeostatic sleep control. Therefore, while these results identify a range of factors involved in homeostatic sleep regulation, they fail to provide a mechanistic explanation of how they interact to govern this process or a suggestion of where they have their effect. From principles to circuits: the identification of the effector arm of sleep homeostasis An alternative to this approach is to use common principles of homeostasis to predict the neural design of a sleep homeostat and then search for a circuit displaying these properties. A homeostat compares levels of a factor, in this case sleep pressure, to a defined set point, before effecting the required change, here sleep, to reduce this difference. Identifying the circuit through which the sleep homeostat effects sleep would be useful, as it will represent a point of convergence in the circuitry mediating sleep homeostasis: whatever form sleep pressure takes, it will drive through the effector to induce sleep. This effector circuit would be active in periods of sleep, cause sleep when artificially activated and be necessary for rebound sleep. A group of 24 neurons that project to a region of the fly brain called the dorsal fan-shaped body (dFB) fulfil these criteria. These so-called dFB neurons are both sufficient to cause sleep when activated artificially through temperature-gated cation channels (Donlea et al., 2011) and necessary for normal recovery sleep after deprivation (Donlea, Pimentel and Miesenböck, 2014). Furthermore, their input resistance and membrane time constants rise during wakefulness, rise further during overnight mechanical sleep deprivation and return to baseline after recovery sleep. This translates into increased excitability, with the same depolarising input current driving a greater percentage of cells to a defined firing rate threshold: a change from an OFF to an ON state (Donlea, Pimentel and Miesenböck, 2014). Therefore, the electrical excitability of these neurons tracks sleep pressure until, at a particular threshold of excitability, they fire and effect sleep. Taken together, these results indicate that the dFB neurons comprise the effector arm of the sleep homeostat and that their transition from the OFF to ON state represents a switch being thrown to induce sleep. However, two key questions remain unanswered: What are the sources of input to the dFB? How are changes in the excitability of the dFB neurons triggered in response to sleep pressure? The literature reveals two answers to the first question; I return to the second later. Hands on the switch: R2 and dopaminergic neurons provide input to the dFB neurons Is a group of neurons called the R2 neurons of the ellipsoid body. When artificially activated, they give rise to sleep; when silenced, flies cannot effect rebound sleep after deprivation. Interestingly, they undergo plastic changes in response to time spent awake, both in a normal sleep–wake cycle and after deprivation, increasing synaptic strength through increasing NMDA receptor expression. This leads to an increase in firing rate with increasing sleep pressure (Liu et al., 2016). Crucially the ability of the R2 neurons to induce sleep is dependent on the dFB: this ability is attenuated when the release of neurotransmitter from the dFB is impaired with tetanus toxin (TNT) (Liu et al., 2016). These results suggest that the R2 neurons undergo synaptic strengthening to encode sleep pressure; resulting in a greater firing rate and thus effecting recovery sleep through signalling to the dFB. However, I would argue that more work is required to determine if the dFB is the only effector downstream of the R2 neurons. Despite the TNT silencing of the dFB neurons described earlier, activation of the R2 neurons nonetheless still caused a small increase in sleep in these experiments. There are two possible explanations for these results (Fig. 1 below): TNT does not completely silence dFB output There is another effector circuit downstream of the R2 neurons that is sufficient to effect sleep Figure 1. Open in new tabDownload slide Diagramatic representation of possible explanations of the results of Liu et al. (2016). Original diagram. To evaluate these possibilities, I suggest two further experiments. First, the dFB neurons, treated with TNT, should be activated optogenetically. If they still effect some increase in sleep this would indicate that Explanation 1 is possible. In this case, the silencing method should be optimised, possibly through co-expression of TNT with the inward rectifier potassium channel Kir2.1 (Baines et al., 2001; Joiner et al., 2006). Expression of this channel to hyperpolarise neurons is an established method in Drosophila and should improve the ablation of dFB function when co-expressed with TNT. This improved system could then be used to silence the dFB neurons more effectively while activating the R2 neurons, establishing with confidence whether the dFB neurons are entirely necessary for the R2 neurons to effect sleep. The second known source of input to the dFB is a pair of dopaminergic neurons. Dopamine signalling is arousing in the fly brain. Increased dopamine signalling through activation of all dopaminergic neurons (Liu et al., 2012), genetic mutation of the dopamine transporter (DAT) to increase synaptic dopamine (Kume et al., 2005) or pharmacological reversal of DAT (Andretic, Van Swinderen and Greenspan, 2005) decreases both baseline sleep and rebound sleep after deprivation. By contrast, attenuation of dopaminergic signalling either by expression of a mutant allele of the dopamine receptor DopR (Ueno et al., 2012) or by inhibition of tyrosine hydroxylase (Andretic, Van Swinderen and Greenspan, 2005), has the opposite effect. Two lines of evidence establish that this arousing effect of dopamine is mediated through direct signalling to the dFB. First, expression of DopR in the dFB only, is sufficient to mediate a short-sleeping phenotype in DAT-mutant flies, which have high overall levels of dopamine. The short-sleeping phenotype of DAT-mutant flies can be rescued by crossing with DopR hypomorphic mutants i.e., high levels of synaptic dopamine are irrelevant when the dopamine receptor is ineffective. Re-expression of functional DopR in just the dFB neurons, however, restores the original short-sleeping phenotype, suggesting that the dFB is the main target of arousing dopamine (Ueno et al., 2012). Secondly, the only two dopaminergic neurons in the brain known to mediate wakefulness when activated, project to the dFB. These neurons originate in the PPL1 and PPM3 clusters (Liu et al., 2012; Ueno et al., 2012). Taken together these results show that dopamine’s arousing effect is mediated through the input of two neurons to the dFB. Thus, existing evidence suggests a putative model of the circuitry mediating sleep homeostasis (Fig. 2 below). The dFB neurons comprise the effector arm of the homeostat. These receive input from the R2 neurons, encoding sleep pressure, and dopaminergic neurons from the PPL1 and PPM3 clusters, encoding a drive for wakefulness. Figure 2. Open in new tabDownload slide Putative model of neural circuitry mediating sleep homeostasis. Original diagram. Flicking the switch: what causes the dFB neurons to switch ON and OFF? Having answered the first question posed, by demonstrating two sources of input to the dFB, it is now possible to address the second. Investigating the mechanism by which the R2 and dopaminergic neurons change the excitability of the dFB neurons might provide clues as to how sleep pressure activates this sleep switch. Dopamine has two effects on dFB neurons: generating both an instant hyperpolarisation that prevents firing and a delayed change in excitability, which is generated over a period of minutes and persists beyond the application of dopamine (Pimentel et al., 2016). This latter effect reflects a stable transition from the ON to OFF state of these neurons. These effects are mediated through reciprocal regulation of potassium channels. Dopamine causes downregulation of the channels Shaker and Shab, required for repetitive action potential generation, and upregulates a two-pore-domain potassium channel, Sandman, that mediates greater hyperpolarisation of the neurons (Pimentel et al., 2016). These results are interesting for two reasons. First, the biophysical changes in the dFB neurons reflect the demands of a transition from sleep to wakefulness: arousal must be fast and sustained, just like the hyperpolarisation of the dFB neurons. Second, they establish a mechanism by which the excitability state of the dFB neurons can be altered through modulation of membrane ion channels. While these results do explain how the dFB neurons can be driven into the OFF state to mediate arousal, they do not explain the opposite transition: how the dFB neurons can be driven into the ON state to induce sleep. However, it is reasonable to speculate that the modulation of membrane ion currents might also be relevant. A further clue is that mutation of the Rho-GTPase activating protein crossveinless-c (Cv-c) renders dFB neurons unable to exit the OFF state (Donlea, Pimentel and Miesenböck, 2014). Interestingly, several putative targets of this protein are involved in ion channel regulation (Cachero, Morielli and Peralta, 1998; Bezzerides et al., 2004). Therefore, investigation of how Cv-c is involved in the modulation of membrane current, and how it is itself controlled, should provide insight into how the dFB neurons are switched ON to cause sleep. The R2 neurons are less helpful. There is strong evidence of their functional connectivity to the dFB: when they are artificially activated dFB activity increases slowly over a period of minutes (Liu et al., 2016). However, the mechanism by which they activate the dFB neurons remains unclear. Unexplained roles of other circuits While the dFB is clearly important in sleep homeostasis, other regions also influence this process. These include the mushroom bodies (MBs), the pars intercerebralis (Foltenyi, Greenspan and Newport, 2007; Park et al., 2014) and various octopaminergic neurons (Seidner et al., 2015). However their effects on sleep are inconsistent and show no central mechanism, in sharp contrast to the sleep switch represented by the dFB. This can be illustrated by discussion of the MBs, structures involved in the processing of learning and memory (Heisenberg et al., 1985; Zars et al., 2000) and also in sleep control. Blocking synaptic output from groups of neurons in these structures can either increase or decrease baseline sleep depending on the neurons targeted, while chemical ablation of the entire structure significantly reduces sleep overall (Pitman et al., 2006). Similar experiments using a range of interventions to activate or inhibit different regions of the MBs either increases or decreases baseline sleep (Sawamura et al., 2008; Bushey, Tononi and Cirelli, 2009; Seugnet et al., 2011), while activation of the whole structure gives less sleep overall (Joiner et al., 2006). This is clearly an unexpected result when chemical ablation also gives less sleep. The recent realisation that the MBs can be divided into wake- and sleep-promoting microcircuits, rather than considered a homogenous collection of neurons, might help to unify these results and derive a central mechanism (Sitaraman et al., 2015). Furthermore, in order to build a complete picture of the machinery of sleep homeostasis it is important to understand how these, and other regions, interact functionally with the dFB. So far, however, the function of the MBs in sleep has mainly been studied in isolation. The only evidence of connectivity to other circuits is that the axons of output neurons from the MBs converge in the superior medial protocerebrum (SMP) and crepine (CRE) neuropils, adjacent to the dendrites of the dFB neurons (Sitaraman et al., 2015). This neuroanatomical evidence is insufficient to establish functional connectivity, which would require evidence that artificial activation of one region can have an effect on the other or that the function of one is attenuated when the other is silenced. Experimental approaches to investigate these possibilities have been described above and could be repeated here. Why do we sleep? How the dFB will address the mystery of sleep The literature on the circuitry mediating sleep homeostasis is currently insufficient to determine how the dFB neurons are activated to cause sleep. However, the discovery of these neurons allows a crucial reframing of the investigation of sleep in molecular and cellular terms. The dFB neurons represent the effector arm of the sleep homeostat, thus the factors or circuits that transduce sleep pressure must drive, directly or indirectly, into the dFB neurons. Thus if we look upstream of these neurons and identify what drives them we will be much closer to unravelling the mystery of sleep. Two broad research strategies could exploit this reframing. dFB neurons could be used to investigate existing theories of sleep or as the basis of new screens. The dFB neurons will have an important role in investigating existing theories of the function of sleep. Vyazovskiy and Harris propose one such theory, based on the metabolic recovery of neurons, arguing that sleep might reflect a necessary period of rest for neurons to recover from cellular stress (Vyazovskiy and Harris, 2013). They suggest that accumulation of reactive oxygen species (ROS) and misfolded proteins during periods of intense synaptic activity must be reversed during periods of less intense metabolism. Crucially, they propose that neurons cannot undergo such rest individually; that their organisation into complex and interdependent networks necessitates global synchronisation of rest; achievable only during periods of unconsciousness. A range of results in both flies (Shaw et al., 2002; Naidoo et al., 2007; Brown et al., 2014) and mammals (Naidoo et al., 2005; Terao et al., 2003, 2006) that implicate cellular stress in sleep homeostasis support this hypothesis. Interestingly, the potassium channel Shaker (Cirelli et al., 2005), its regulator quiver (Koh et al., 2008) and its β modulatory subunit Hyperkinetic (Bushey et al., 2007) are expressed in the dFB (Pimentel et al., 2016). Evidence in a variety of systems implicates Shaker and its regulators in oxygen sensing: demonstrating that the activity of the channel can be modulated by ROS (Vega-Saenz de Miera and Rudy, 1992; Duprat et al., 1995; Ciorba et al., 1997; Wang et al., 2000). Can we connect this ROS sensing apparatus in the dFB with the need for sleep and, thus, provide for Vyazovskiy and Harris’ theory of sleep? Current evidence is inconclusive. Work from 2016 has shown that knockdown of Shaker, quiver and Hyperkinetic in the dFB can give rise to decreases in sleep (Pimentel et al., 2016). We can therefore speculate that these proteins might be part of the machinery of sleep homeostasis and that cellular stress might therefore represent sleep pressure. However, current evidence does not establish whether significant levels of ROS are produced in the dFB, whether Shaker does indeed respond to these or whether this is sufficient to drive the transition of dFB neurons from OFF to ON. To investigate this hypothesis I would suggest that ROS levels should be artificially increased and decreased in the dFB, using methods already established in Drosophila (Mast et al., 2008; Owusu-Ansah and Banerjee, 2009), to investigate whether this causes more and less sleep respectively. If increased levels of ROS in dFB neurons caused more sleep, then we must consider two potential explanations. It might be the case that the increased ROS is measured by Shaker, quiver and Hyperkinetic as part of a mechanism to couple ROS levels to increased sleep. Or it might be disrupting normal cellular physiology and thus causing aberrant activity of the neurons. In order to distinguish between these hypotheses, a second set of experiments could be performed. Shaker, quiver and Hyperkinetic should be knocked out and levels of ROS increased. If increased ROS only caused an increase in sleep in the presence of this putative transducer machinery then this would be stronger evidence in support of the role of cellular stress in inducing sleep. If, however, the knockout of Shaker, quiver and Hyperkinetic had no effect on sleep duration in the presence of increased ROS then the alternative hypothesis would be more likely: that ROS was causing an increase in sleep through disrupting cellular physiology. Other theories suggest that sleep is required to consolidate memories (Vorster and Born, 2015), re-normalise synaptic strength after prolonged periods of activity (Tononi and Cirelli, 2006) or replenish energy stores depleted during wakefulness (Benington and Craig heller, 1995). Evidence to date is insufficient to fully support any of these theories. However, just as with the metabolic recovery hypothesis, the identification of the dFB and its associated circuitry provides a new framework for testing these theories. If circuits involved in learning and memory drive into the dFB and increase the excitability of these neurons, this would provide evidence for the memory consolidation hypothesis. Likewise, there are paracrine factors released as cellular energy stores are depleted (Benington and Craig heller, 1995). If these were shown to have a specific and excitatory effect on the dFB this would provide evidence for the energy store hypothesis. Again, the underlying principle is the same. By identifying the dFB as the effector of the sleep homeostat, it is possible to re-frame research by looking upstream of these neurons. While these examples demonstrate how the dFB neurons could be used to test existing theories of sleep, they might also be used in screen experiments to identify new factors and circuits that control sleep pressure. While the screen experiments discussed at the beginning of this analysis did not identify any useful targets, screens using the dFB neurons might be more effective. While existing experiments have been forced to use the blunt outcome measure of whether or not flies sleep more when screening for factors or circuits involved in sleep, future experiments will be able to use changes in excitability of the dFB neurons. This is a much finer outcome measure. It is entirely possible that some molecular factors or circuits involved in transducing sleep pressure, when increased or activated, would cause an increase in the excitability of the dFB neurons, without necessarily having a large enough effect to cause increased sleep. New screen experiments that ask what factors or circuits can thus activate the dFB neurons might, therefore, reveal factors and circuits that have so far remained undetected. Once again, this allows a crucial reframing of the central question of sleep in molecular terms. Instead of asking ‘Why do we sleep?’ we can now ask ‘What activates the dFB neurons, and to what process does this relate?,’ a large step forward in sleep research. Crossing species lines: is there a mammalian homologue of the dFB? Finally, while it is likely that any function of sleep revealed by work in Drosophila will be conserved across species, it is also interesting to ask if the sleep control circuitry will be similar. Three lines of evidence address this question. First, the dFB is a target of general anaesthetics (Kottler et al., 2013), as is the ventrolateral preoptic nucleus (VLPO) (Nelson et al., 2002; Moore et al., 2012), an important sleep-active circuit in mammals (Saper et al., 2010). Second, the sleep-active signal from the VLPO is mediated by cotransmission of GABA and galanin (Sherin et al., 1998) while that from the dFB is mediated by Allatostatin A (AstA). The receptors for AstA and for galanin show a large degree of sequence similarity (Donlea et al., 2018). Finally, dopamine promotes wakefulness in both humans and other mammals (Wisor et al., 2001; Eban-Rothschild et al., 2016) and the dFB is the target of arousing dopamine signalling in flies. These results suggest that elements of sleep control circuitry in flies and mammals might share some degree of homology. However, it remains to be confirmed whether this reflects a deeper functional similarity. Conclusions While a wide range of signalling pathways, neurotransmitters and other factors have been implicated in sleep homeostasis in Drosophila, the most important work in this model has been the identification of the dFB neurons as the effector arm of the sleep homeostat. While several questions remain about how exactly these neurons relate to other sleep-control circuits, this new understanding of the dFB will inform future research, by providing a method to track the output of the sleep homeostat. Regardless of whether the dFB emerges as an exact homologue of mammalian circuits, it will allow future work to identify the factors and processes that comprise sleep pressure, by measuring their ability to activate these neurons. This represents a crucial reframing of the mystery of sleep as a molecular question and in doing so provides a window on the vital function of sleep itself. Acknowledgements The ideas presented in this paper were developed and refined in discussion with Professor Gero Miesenböck of the Centre for Neural Circuits and Behaviour, University of Oxford. I am particularly grateful to him for his guidance during my initial literature searches, for helping me to develop my early ideas and for critiquing the new experimental strategies that I propose. Author biography Christian Holland is currently a fourth-year medical student at the University of Oxford, having spent his third-year intercalating in Medical Sciences. 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