Many of us have experienced feelings of sleepiness after a large meal. However, there is little scientific evidence that this “food coma” effect is real. If it is, it may vary between individuals, or depend on the type of food consumed. This variability makes it difficult to study the causes of post-meal sleepiness.

Murphy et al. have now developed a system that can measure fruit fly sleep and feeding behavior at the same time. Recordings using this system reveal that after a meal, flies sleep more for a short period before returning to a normal state of wakefulness. The sleep period lasts around 20-40 minutes, with flies that ate more generally sleeping more.

Further investigation revealed that salty or protein-rich foods promote sleep, whereas sugary foods do not. By using genetic tools to turn on and off neurons in the fly brain, Murphy et al. identified a number of brain circuits that play a role in controlling post-meal sleepiness. Some of these respond specifically to the consumption of protein. Others are sensitive to the fruit fly’s internal clock, reducing post-meal sleepiness only around dusk. Thus, post-meal sleepiness can be regulated in a number of different ways.

Future experiments are now needed to explore the genes and circuits that enable meal size and the protein or salt content of food to drive sleep. In nature, sleep is likely a vulnerable state for animals. Thus, another challenge will be to uncover why post-meal sleep is important. Does sleeping after a meal boost digestion? Or might it help animals to form memories about a food source, making it easier to find similar food in the future?

Despite accumulating evidence of interactions between sleep and metabolism, few studies have documented an increased propensity for sleep that animals might experience after meals. Results on human postprandial behavior have shown an increased sleep propensity following a meal, a lagging response, or no effect at all (Orr et al., 1997; Zammit et al., 1995; Wells et al., 1998; Harnish et al., 1998; Stahl et al., 1983). It has been proposed that postprandial sleep is not invariable; it may be absent in particular individuals or heavily dependent on specific food properties (Stahl et al., 1983). While the behavior likely exists, these intrinsic variabilities have limited our ability to study its molecular basis. A thorough examination of this behavior would be facilitated by use of an animal model although, at present, no clear model exists (Watson, 2014).

In Drosophila there is a well-documented sleep-metabolism interaction in which flies suppress sleep or increase locomotion when starved (Lee and Park, 2004; Thimgan et al., 2010; Keene et al., 2010). However, the acute effects of food consumption on sleep have not been tested, largely because there is no system available to do so. Here we establish a system for simultaneous measurement of discrete feeding events and sleep, allowing us to examine the mechanisms underlying their interaction.

To simultaneously measure sleep and feeding of individual adult flies, we designed the Activity Recording CAFE (ARC), a system that couples machine vision tracking of capillary-based food consumption and animal motion (Figure 1A–C) (Ja et al., 2007; Donelson et al., 2012; Deshpande et al., 2014). Previous studies have shown that flies inactive for 5 min exhibit multiple hallmarks of sleep (Shaw et al., 2000; Huber et al., 2004; Hendricks et al., 2000). Using this standard, we extracted sleep parameters by defining sleep as periods of inactivity >5 min. Despite different housing and food consistency, we found sleep architecture to be similar to previous studies using high resolution object tracking, a method which has greater resolution of total sleep and sleep duration than the infrared-beam based alternative (Figure 1C, Figure 1—figure supplement 1A–D) (Donelson et al., 2012). Flies appeared to move less after meals than before—an effect more pronounced with larger meals—suggesting that sleep might also be greater after eating (Figure 1D). Animals also positioned themselves closer to the food following each meal (Figure 1E–F), consistent with observations that isolated flies sleep close to food (Hendricks et al., 2000) and the spatial tendency of flies driven to sleep by activation of neurons expressing short neuropeptide F, a satiety signaling hormone (Shang et al., 2013; Lee et al., 2004).

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To examine the acute effects of feeding on sleep, we quantified the probability of sleep (P_sleep) relative to individual feeding events. The animals experienced a sharp decline in P_sleep in the time approaching each meal, as expected since flies cannot sleep and feed simultaneously. Subsequent to the meal, P_sleep rose rapidly and exceeded the respective pre-meal P_sleep (Figure 2A). To quantify changes in sleep subsequent to each meal, we calculated the change in P_sleep of respective time points before and after each event (i.e. t⁺¹ – t⁻¹, t⁺² – t⁻², …). This analysis revealed a rise in sleep lasting approximately 20–40 min with a maximum ΔP_sleep amplitude of 0.15 and 0.2 for the control strains, w¹¹¹⁸ and Canton-S, respectively (Figure 2B). The effect was observed in both males and females (Figure 2—figure supplement 1A). While the duration of sleep events prior to a meal is inherently limited by the time until an animal wakes to eat, sleep initiation is not limited and was more probable following a meal (Figure 2—figure supplement 1B).

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In addition to periods of quiescence, increased arousal threshold is regularly used to demonstrate sleep in flies. To test if arousal threshold is increased after a meal, we adapted methodology from the Drosophila arousal tracking system in which animals are exposed to a series of increasing vibrational stimuli (0.8–3.2 g) once every hour in order to measure the intensity at which inactive animals respond (Figure 2C) (van Alphen et al., 2013; Faville et al., 2015). Consistent with previous studies, we found that arousal threshold initially increases with the amount of time that a fly is inactive, with the greatest change occurring in the first 5 min (Figure 2D) (Shaw et al., 2000; Huber et al., 2004; van Alphen et al., 2013; Faville et al., 2015). Since the maximum duration of inactivity is lower following a meal, irrespective of sleep probability (Figure 2E), we filtered arousal events to compare animals in similar stages of sleep. While animals inactive for more than 5 min are experiencing change in sleep depth, animals inactive for 0–5 min are transitioning into sleep. In this transition state animals showed a significantly greater arousal threshold in the 20, 40, and 60 min post-meal compared to animals during the respective pre-meal times (Figure 2F). Interestingly, the correlation between arousal threshold and postprandial sleep grew weaker when comparing animals in deeper stages of sleep (Figure 2—figure supplement 2A–B). This suggests that food intake transiently influences sleep induction rather than depth, which might be shaped solely by the amount of time that an animal is inactive.

We further considered the possibility that increases in immobility-derived sleep and arousal threshold could be artifacts of grooming behavior. To test this, we manually scored videos of animals during an ARC recording and annotated grooming events. Falsely calculated sleep, in which animals were grooming rather than immobile, accounted for ~7% of sleep surrounding meals and had statistically indistinguishable occurrence before and after a meal (Figure 2G, Figure 2—figure supplement 3). Re-analysis of sleep surrounding a meal, where sleep was defined as periods of immobility and a lack of grooming exceeding 5 min, lowered total sleep but did not alter the rise in postprandial sleep (Figure 2H).

We next asked how the quantity of food consumed might influence postprandial sleep. We plotted P_sleep or ΔP_sleep for different consumption volumes and found that sleep following meals increased as a function of meal size, while pre-prandial sleep did not (Figure 3A, Figure 3—figure supplement 1A). Since average postprandial sleep lasted between 20–40 min, and since arousal threshold differences were most apparent in the first 20 min, we re-calculated ΔP_sleep for the 20 min surrounding each meal. We found a significant correlation between ΔP_sleep and consumption volume when meals were analyzed individually (Figure 3—figure supplement 1B) or binned (Figure 3B). Since this analysis used individual meals rather than individual animals, we tested whether animals that consumed meals more frequently could bias the observed effect. We performed a Monte Carlo simulation by iteratively recapitulating the analysis with an equal number of randomly selected meals from each animal and found that equal sampling accurately portrays the full data set (Figure 3—figure supplement 2A–B). Since sleep is derived from periods of inactivity, we considered whether increased consumption could affect mobility, which might in turn appear to be sleep. The change in movement rate after a meal (Δ_speed) showed a much weaker correlation to meal size than ΔP_sleep (Figure 3—figure supplement 2C). Since speed is a strong indicator of mobility, we reasoned that immobility is not likely responsible for the sleep effect.

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Although meal size has an apparent impact on postprandial sleep, the standard diet contains numerous physiological and nutritive components. To determine the potential contribution of individual components to postprandial sleep, we designed a paradigm that allowed us to test a range of feeding volumes or of ingested nutrients. To test volumetric effects, animals were fed a low protein concentration food (0.25% tryptone) to elicit feeding without impacting baseline sleep, since sucrose and sweet tasting substances have been shown to modulate sleep (Keene et al., 2010; Linford et al., 2012). We found a highly significant correlation between meal volume and 20 min ΔP_sleep (Figure 4A, Figure 4—figure supplement 1A–B). Larger meal volumes are also associated with a strong but transient increase in ΔP_sleep amplitude (Figure 4A, Figure 4—figure supplement 2A).

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To determine if individual nutrients could modulate postprandial sleep, we fed animals a concentration series of protein (tryptone), salt (NaCl), or sucrose. All diets contained a base of 0.25% protein to induce feeding while minimizing effects on baseline sleep that might arise by differences in sucrose consumption. Within each series we compared nutrient consumption within a narrow volume range (0.02–0.04 µl, representing 33–38% of recorded meals) to limit the contribution of ingested volume on sleep. We found that ΔP_sleep was significantly correlated to both protein and salt ingestion (Figure 4A, Figure 4—figure supplement 1A–B). Interestingly, despite reported effects of sucrose on total sleep (Keene et al., 2010; Linford et al., 2012), we found no correlation between ingested sucrose and ΔP_sleep (Figure 4A, Figure 4—figure supplement 1A–B). For all analyses, only the meal parameter of interest varied, while all others were kept within a restricted range (Figure 4B). To compare the kinetics of ΔP_sleep for each effective driver, we quantified maximum ΔP_sleep amplitude and the total ΔP_sleep during its decay. For any given increase in total ΔP_sleep during the decay, meal volume induced a greater rise in amplitude than protein or salt (Figure 4—figure supplement 2B). We hypothesize that multiple mechanisms operating on different time scales might underlie postprandial sleep regulation by the various drivers.

We next sought to identify a neuronal mechanism by which feeding drives postprandial sleep. A previous study showed that Lk was involved in meal size regulation, suggesting that this system acts rapidly during feeding to signal a behavioral shift (Al-Anzi et al., 2010). To test if Lk signaling regulates postprandial sleep, a subset of Lkr neurons labeled by the line, R65C07 (hereafter referred to as Lkr^GAL4), were silenced by overexpressing Kir^2.1, an inward rectifying K⁺ channel (Johns et al., 1999). This manipulation eliminated any rise in postprandial sleep and instead caused slight arousal (Figure 5A). There was no significant change in postprandial activity (Δ_motion for 20 min after meal: Kir^2.1 / w^CS, −0.006 ± 0.003; Lkr^GAL4 / w^CS, −0.001 ± 0.002; Kir^2.1 / Lkr^GAL4, 0.004 ± 0.004; p=0.28, Kruskal-Wallis test followed by Dunn’s multiple comparisons). The expression pattern of Lkr^GAL4 recapitulates that of Lkr neurons which were found to regulate meal size (Al-Anzi et al., 2010), revealing several cell populations stemming from the lateral horn which innervate the dorsal fan-shaped body of the central complex—a region of the brain which functions in sleep homeostasis, visuomotor function, and hunger-related behavior (Figure 5B) (Donlea et al., 2011, 2014; Seelig and Jayaraman, 2013; Krashes et al., 2009). This circuitry suggests that the Lk system may act directly on the sleep and motor controllers of the fly brain to regulate postprandial sleep.

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We hypothesized that Lkr circuitry could receive convergent signals from volume, protein, and salt to drive postprandial sleep. Alternatively, it could specifically relay the signals of a single driver to some point of convergence. We first tested whether the subset of Lkr neurons labeled by Lkr^GAL4mediated the sleep response to meal volume by feeding a low nutrient diet to Lkr^GAL4-silenced animals. Silenced animals and their controls showed similar ΔP_sleep in response to ingested volume, indicating that the Lkr^GAL4 neurons are not responsible for the volumetric modulation of postprandial sleep (Figure 5C). To test the role of Lkr neurons in the postprandial sleep response mediated by nutrients, we supplemented a sucrose diet with either salt or tryptone. Lkr^GAL4-silenced animals and their controls showed statistically indistinguishable ΔP_sleep responses to salt manipulation (Figure 5D). In contrast, while protein ingestion increased postprandial sleep in controls, Lkr^GAL4-silenced animals showed a strong waking response, or a negative shift in postprandial sleep (Figure 5D). Interestingly, this waking response exceeded that of control animals on sucrose alone, indicating that protein can also drive wakefulness in the absence of Lkr^GAL4 neuronal activity. We did not observe changes in meal size, total sleep, or total consumption, indicating that these behaviors are independent of Lkr influence on postprandial sleep (Figure 5—figure supplement 1A–C). To test whether this phenotype was caused by changes in development, we conditionally silenced the cells by co-expressing a temperature sensitive GAL4 suppressant protein, GAL80^ts, throughout development (Figure 5E). Inactivation of GAL80^ts to silence Lkr^GAL4-labeled cells in adulthood was sufficient to induce a postprandial sleep defect (Figure 5F).

Subsets of Lk neurons have been found in the lateral horn, with the diffuse puncta in close proximity to the cell bodies of the Lkr neurons, suggesting a neuromodulatory connection between these cells (Al-Anzi et al., 2010; de Haro et al., 2010; Cavey et al., 2016). Recent findings also show that incubating Lkr neurons with Lk inhibits their response to the cholinergic agonist carbachol, suggesting that Lk inhibits Lkr neuronal activity (Cavey et al., 2016). We first sought to confirm that cells labeled by the Lk^GAL4 driver express Lk. Indeed, immunostaining of Lk overlapped with Lk^GAL4-labeled cells in the lateral horn, as well as in two cells in the suboesophageal ganglion (SOG) (Figure 6A). Using thermogenetic activation of these cells with the transient receptor potential channel Trp^A, we found that postprandial sleep was significantly reduced upon activation (Figure 6B), furthering the notion that these cells inhibit Lkr neurons. To see if Lk was necessary to drive this inhibitory effect, we used two independent RNAi lines to knockdown Lk in cells labeled by Lk^GAL4. We found that this knockdown increased postprandial sleep, consistent with the idea that expression of Lk in Lk^GAL4-labeled cells is necessary for the inhibitory effect on Lkr neuronal activity (Figure 6C).

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The broad arborizations of the Lkr neurons suggest that these cells might receive inputs from a number of spatially or type distinguished cells in the brain. R20G03 (20G03), a line expressing GAL4 driven by an enhancer fragment 316 base pairs upstream of Lk, labels cell bodies in the lateral horn and SOG, as well as a subset of mushroom body output neurons (MBONs) (Figure 7A), all of which coincide with Lkr neurons labeled by the Lkr^GAL4driver (Figure 7B). The cholinergic MBONs of 20G03 have been shown to play a role in short-term appetitive memory following food intake (Aso et al., 2014). These features, paired with their neuroanatomical distribution, suggested that these cells might play a role in postprandial sleep. To test this, we again employed electrical silencing with Kir^2.1 and found that constitutive and adult-restricted silencing of these neurons increased postprandial sleep, suggesting that these cells might directly inhibit the activity of proximal Lkr neurons (Figure 7C–D). Interestingly, immunostaining of Lk did not co-localize with any of the 20G03 cells, nor did downregulation of Lk in these cells affect postprandial sleep (Figure 7—figure supplement 1A–B), suggesting that their inhibitory action stems from an alternative neuropeptide or neurotransmitter. By analyzing behavior throughout a 24 hr period, we found that the increase in postprandial sleep driven by 20G03 silencing was most pronounced at ZT 12 (Figure 7E–F). Closer examination within this period showed that, rather than raising sleep evenly within ZT 10–14, the sleep after but not before each meal was increased (Figure 7G), implying that these cells activate in response to food intake.

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The ARC is the first platform capable of simultaneously measuring sleep and resolving discrete feeding events in individual flies. Using this tool, we found that flies exhibit a rise in sleep following a meal. Interestingly, consumption of a meal did not necessarily affect the depth of sleep when controlling for time inactive. There are many instances in which genes or neural processes have been found to influence particular features of sleep while leaving others unaffected (Harbison et al., 2009; Shi et al., 2014; Shang et al., 2011). Similarly, our work suggests that sleep probability and sleep depth can also be dissociated. By further examination of the shift in sleep probability, we found that it increased as a function of meal size. This may be paralleled in humans where cranial EEG power increases with meal size and during certain stages of sleep (Reyner et al., 2012).

In examining individual meal components, we identified volume, protein, and salt as effectors of postprandial sleep. While human studies have been unable to resolve a link between meal volume and measures of wakefulness (Landström et al., 2001), artificial distension of the gut has been shown to increase sleep in rats (Lorenz et al., 1998). Although there are no studies on the effect of protein on postprandial sleep, protein consumption modulates sleep in Drosophila and positively correlates with napping in humans (Catterson et al., 2010; Grandner et al., 2010). Similarly, although there are no studies on the influence of salt on postprandial sleep, salt intake increases mammalian oxytocin signaling, which has been implicated in sleep-wake behavior (Krause et al., 2011; Lancel et al., 2003). Sucrose has been shown to play a critical role in long-term sleep homeostasis and architecture in flies (Keene et al., 2010; Linford et al., 2012), although our findings indicate that it does not modulate postprandial sleep. Although we demonstrate the effectiveness of these dietary components in isolation, we note that meals typically incorporate multiple elements. Thus, further work is necessary to identify the mechanisms by which each constituent is sensed and the potential interactions between them. Lipids are also present in many fly food sources and future examination of their influence on postprandial sleep might be facilitated by adding fat solubilizers to the liquid food used in the ARC.

We demonstrate that the Lk system plays a role in postprandial sleep (Figure 7H). A subset of Lkr neurons were necessary to initiate postprandial sleep in the presence of protein. While we expected that animals defective in protein sensing would experience postprandial sleep similar to that of animals fed only sucrose, we found instead that they had a waking response. This suggests that ingested protein promotes both sleep and wakefulness, and that the wakefulness is normally counterbalanced by Lkr neuronal activity. While this study does not specifically identify the waking output for protein, it was previously shown that Translin acts through Lk-expressing cells to drive wakefulness in response to sucrose starvation (Murakami et al., 2016). While this does not explain rapid wake promotion in response to protein ingestion, it does demonstrate that Lk-expressing cells are capable of promoting wakefulness.

Another recent finding identified Lkr neurons as circadian output neurons, which modulate activity rhythmically. The same study found that incubation of Lkr cells with Lk shunts their calcium response to the cholinergic agonist carbachol (Cavey et al., 2016), suggesting an inhibitory connection between Lk and downstream Lkr neurons. By stimulating these same Lk neurons, we observed a reduction of postprandial sleep matching the effect of Lkr silencing, providing additional evidence that the lateral horn harbors an inhibitory node between Lk and Lkr neurons. We also found that knockdown of Lk in these cells increased postprandial sleep, supporting this notion. However, it has been suggested that Lk may have additional receptors (Terhzaz et al., 1999), and there is still a lack of definitive evidence for the inhibitory mechanism between Lk and Lkr neuronal activity in the lateral horn. Additional genetic tools, including lines with restricted expression patterns, will be needed to fully uncover the genetic interactions and neuronal connectivity of this system.

We also found a subset of cells labeled by 20G03 that modulate postprandial sleep during the period near ‘dusk’ or ‘lights-off’. Interestingly, the subset harbors acetylcholine MBONs—cells necessary for the short-term associative memory between sucrose consumption after starvation and an odor (Aso et al., 2014). This suggests that these cells activate following a feeding event, in agreement with our results. This is further supported by the anatomical distribution of cells in the SOG, a region of the fly brain that regulates feeding behavior. The distribution also shows overlap with Lkr neurons and regions of the neuronal clock network, suggesting a diversity of regulatory inputs and outputs. Further studies might employ GFP reconstitution across synaptic partners (GRASP) (Gordon and Scott, 2009) or neuronal co-labeling to demonstrate these connections. The apparent diversity of neuronal modulators of postprandial sleep opens the door to future work on determining the various genes and circuits involved.

The tachykinin family to which Lk belongs has been implicated in the regulation of satiety and sleep, however any physiological connections between these behavioral outputs has not been explored (Massi et al., 1988; Zielinski et al., 2015). The orexin system is regulated by a diversity of signaling molecules (Scammell and Winrow, 2011) and has already shown promise as a target for sleep therapeutics. Such genetic diversity and therapeutic potential may also be true of systems governing postprandial sleep. Accordingly, the ARC provides a platform for future studies aimed at uncovering additional genes, circuitry, and physiological roles of postprandial sleep. Furthermore, the ability to measure both sleep and feeding in individual animals may shed new light on the fundamental relationship between these behaviors in other paradigms.

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Author details

1. Keith R Murphy

   1. Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, United States
   2. Program in Integrative Biology and Neuroscience, Florida Atlantic University, Jupiter, United States
   3. Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   KRM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Sonali A Deshpande

   Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, United States

   Contribution

   SAD, Designed and carried out experiments, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

3. Maria E Yurgel

   Program in Integrative Biology and Neuroscience, Florida Atlantic University, Jupiter, United States

   Contribution

   MEY, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. James P Quinn

   Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, United States

   Contribution

   JPQ, Designed and carried out experiments, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

5. Jennifer L Weissbach

   Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, United States

   Contribution

   JLW, Designed and carried out experiments, Acquisition of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

6. Alex C Keene

   Program in Integrative Biology and Neuroscience, Florida Atlantic University, Jupiter, United States

   Contribution

   ACK, Designed and carried out experiments, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. Ken Dawson-Scully

   Program in Integrative Biology and Neuroscience, Florida Atlantic University, Jupiter, United States

   Contribution

   KD-S, Developed the ARC, Conception and design

   Competing interests

   The authors declare that no competing interests exist.

8. Robert Huber

   1. Radcliffe Institute for Advanced Study, Harvard University, Cambridge, United States
   2. JP Scott Center for Neuroscience, Mind and Behavior, Bowling Green State University, Bowling Green, United States

   Contribution

   RH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

9. Seth M Tomchik

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   SMT, Conception and design, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

10. William W Ja

    1. Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, United States
    2. Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

    Contribution

    WWJ, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    wja@scripps.edu

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0002-4003-7356

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