All relevant data are within the paper and in its Supporting Information files.

Introduction {#sec001}
============

Behavioral decisions in animals are formed by integrating internal states with external stimuli and prior experience. The need for sleep and food are two such internal states and satisfying both of these homeostatic processes seems equally important for survival \[[@pbio.3000548.ref001]--[@pbio.3000548.ref005]\]. Yet sleeping and feeding are also mutually exclusive: they cannot happen at the same time. Under environmentally challenging conditions, mutually exclusive behaviors therefore need to be prioritized in order to maximize survival.

Both sleep and feeding regulation have been extensively studied in different animal models, as well as in humans \[[@pbio.3000548.ref006]--[@pbio.3000548.ref012]\]. Yet how their pathways intersect and influence each other remains unclear. Given the alarming increase in the number of people with both sleep and metabolic disorders \[[@pbio.3000548.ref013]--[@pbio.3000548.ref015]\], to understand how these two processes interact at the level of neural circuits and molecular pathways is of significant interest. *Drosophila melanogaster* has been a pivotal model system to study both sleep and feeding regulation \[[@pbio.3000548.ref009],[@pbio.3000548.ref016]--[@pbio.3000548.ref023]\]. Sleep in flies has been shown to fulfill key criteria for identifying sleep in other animals, such as increased arousal thresholds and homeostatic regulation \[[@pbio.3000548.ref024],[@pbio.3000548.ref025]\], so the fly is a promising avenue for understanding sleep and feeding regulation at a circuit level. Additionally, cognitive readouts such as visual attention paradigms are increasingly available for *Drosophila* research \[[@pbio.3000548.ref026]--[@pbio.3000548.ref028]\], providing relevant functional assessments of manipulations that could impact sleep and feeding.

Generally, the effect of feeding on sleep has been studied by altering dietary components, or by more severe interventions such as starvation \[[@pbio.3000548.ref029]--[@pbio.3000548.ref032]\]. However, studying this relationship via nutritional manipulations introduces numerous secondary factors (e.g., metabolic processes or energy levels), confounding any analysis of potential interactions between satiety/starvation signals and sleep processes. We therefore decided to utilize a genetic strategy in *Drosophila* to down-regulate a cytokine secreted from the fly fat body (FB), unpaired 2 (*upd2*) \[[@pbio.3000548.ref033],[@pbio.3000548.ref034]\]. By down-regulating *upd2* and its receptor *domeless* (*dome*) \[[@pbio.3000548.ref035],[@pbio.3000548.ref036]\], we aimed to potentially mimic a "starved" state in flies, which allowed us to assess the effect of this metabolic signaling pathway on sleep and attention simultaneously. *upd2* has been suggested as a candidate ortholog for vertebrate leptin, and has similar structure to type-I cytokines \[[@pbio.3000548.ref037],[@pbio.3000548.ref038]\]. Similar to leptin, secretion of *upd2* is dependent on nutrient intake \[[@pbio.3000548.ref037],[@pbio.3000548.ref039]\], and it is secreted from the fly counterpart of adipocytes, or FBs.

Starvation has several consequences on the behavior of animals, with one of the most striking ones being suppression of sleep \[[@pbio.3000548.ref029],[@pbio.3000548.ref032],[@pbio.3000548.ref040]--[@pbio.3000548.ref043]\]. Normally, sleep deprivation in flies and other animals leads to an increase in sleep drive and a homeostatic sleep rebound \[[@pbio.3000548.ref024],[@pbio.3000548.ref025],[@pbio.3000548.ref044]\], as well as impaired cognitive capacities such as those measured by visual learning \[[@pbio.3000548.ref045],[@pbio.3000548.ref046]\] and attention \[[@pbio.3000548.ref047]\]. Yet starvation-induced sleep loss seems to absolve animals from sleep need and some of the functional consequences of sleep loss \[[@pbio.3000548.ref047]--[@pbio.3000548.ref049]\]. The mechanisms supporting this surprising effect are unclear, and it is unknown what aspects of cognition are preserved under this regime. We used *upd2* mutants and tissue-specific knockdown of *upd2* and its receptor to address possible consequences of a chronic starved-like state. We demonstrate that reduced *upd2* signaling disrupts daytime sleep and leads to increased feeding-related behaviors, such as nighttime hyperphagia (increased feeding at night). While sleep deprivation typically impairs attention, we found that *upd2* knockdown animals had improved attention, even though they slept less. Finally, we show that *upd2* regulates sleep and attention via cells expressing *insulin-like peptide 2* (Ilp2) signaling in the brain. Our results highlight a role for peripheral signaling in co-regulating cognition and sleep as a function of nutrition.

Results {#sec002}
=======

*upd2* mutants have irregular feeding and fragmented sleep {#sec003}
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Homozygous *upd2* deletion mutants (*upd2*^*Δ*^*)*, which lack the 5′ UTR and the first 89 amino acids of the protein \[[@pbio.3000548.ref033]\], have been shown to be smaller and slimmer than control animals \[[@pbio.3000548.ref037]\]. We first measured the food intake of mutant animals to address whether the difference in their body size ([Fig 1A](#pbio.3000548.g001){ref-type="fig"}) was due to a decrease in feeding, which would be inconsistent with a starvation cue. We used an optimized version of the capillary feeding (Café) assay \[[@pbio.3000548.ref050]\] and tracked their food consumption over 24 hours. In agreement with previous findings, there was no significant change in total food consumption in *upd2*^*Δ*^ mutants, compared with controls ([Fig 1B](#pbio.3000548.g001){ref-type="fig"}) \[[@pbio.3000548.ref037],[@pbio.3000548.ref051]\]. However, when we looked at day and night feeding separately, we noticed that the mutants were mostly feeding during the night, which was opposite to the feeding rhythm of the background controls ([Fig 1C](#pbio.3000548.g001){ref-type="fig"}). To determine if light entrainment might be driving the altered feeding behavior, we conducted the same experiment in the dark. We found that in the absence of light cues, *upd2*^*Δ*^ mutants still fed more during their subjective night, although feeding during their subjective day was not different from controls ([S1 Fig](#pbio.3000548.s001){ref-type="supplementary-material"}). This shows that *upd2*^*Δ*^ animals are well fed, although their feeding times seem dysregulated.

![*upd2* mutants have altered feeding behavior and fragmented sleep.\
(A) Homozygous *upd2* deletion mutant females (right, red), compared with their background controls (left, black). (B) Food intake was measured with Café assay, with 5 flies/chamber over 24 hours (8--9 chambers, *n* = 40--45 flies per genotype). The bottom of the chamber had 1% agar to prevent desiccation. Liquid food (5% sucrose/water) was presented in a microcapillary. Total food consumption of *upd2* mutant flies (red) was similar to their background control (black). (C) Mutants had lower consumption during daytime. However, they had a significant increase in their nighttime feeding. (D) *Drosophila* arousal tracking (DART) was used to measure sleep duration in *upd2* mutants. Three- to five-day-old female virgins were placed in glass tubes and sleep was tracked over 3 days (*n* = 31--32, per genotype). (E) Average position preference heatmaps show that *upd2* mutants have an increased presence at the food site at night (black bars), whereas controls remain in the center. (F) The 24-hour sleep profile of *upd2* mutants compared with control. White and black bars represent light and dark periods, respectively. (G) *upd2* mutants had a significant reduction in average sleep duration for both day and night. (H) The number of sleep bouts was reduced during daytime and increased during nighttime. (I) The average bout duration of *upd2* mutants was reduced for both day and night. (J) Bout number plotted against bout duration is reflective of sleep quality. Sleep is more fragmented when bout durations are short and bout numbers are high, whereas sleep is consolidated with low bout numbers and longer bout durations. (K-L) Total bout number plotted against average bout duration (minutes) showed that *upd2* mutants had fragmented day and night sleep. Student *t* test for normally distributed data or Mann-Whitney U rank-sum test for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. Café, capillary feeding; DART, *Drosophila* arousal tracking; n.s., nonsignificant; *upd2*, unpaired 2.](pbio.3000548.g001){#pbio.3000548.g001}

Nutritional state has been shown to influence sleep duration, as well as sleep quality \[[@pbio.3000548.ref029],[@pbio.3000548.ref052]\]. We used the *Drosophila* arousal tracking (DART) system \[[@pbio.3000548.ref053]\] to monitor sleep duration in *upd2*^*Δ*^ flies that were housed in small glass tubes over multiple days and nights ([Fig 1D](#pbio.3000548.g001){ref-type="fig"}). In accordance with our Café results ([Fig 1C](#pbio.3000548.g001){ref-type="fig"}), position preferences of flies in the tubes revealed that *upd2* mutants consistently stayed near the food during the night, unlike the background controls ([Fig 1E](#pbio.3000548.g001){ref-type="fig"}). *upd2*^*Δ*^ displayed a regular day-night sleep profile (sleeping less during the day and more at night, [Fig 1F](#pbio.3000548.g001){ref-type="fig"}); however, they slept significantly less than control flies, especially during the day ([Fig 1G](#pbio.3000548.g001){ref-type="fig"}). Closer analysis revealed a decreased number of sleep bouts, which were shorter in duration during the day ([Fig 1H and 1I](#pbio.3000548.g001){ref-type="fig"}). During nighttime, however, mutants had increased sleep bout numbers, which were shorter in duration ([Fig 1H and 1I](#pbio.3000548.g001){ref-type="fig"}). These results also indicate that sleep in *upd2* mutants is more fragmented than in control animals, both during both day and night ([Fig 1J, 1K and 1L](#pbio.3000548.g001){ref-type="fig"}). Overall, the observed decrease in sleep duration, the increased sleep fragmentation, and the misregulation of feeding suggested a maladjusted nutritional cue in these mutants, presumably resulting from the absence of a cytokine signal that normally results from adequate feeding. Closer examination of the flies' walking speed revealed that the mutants were just as active as controls ([S2 Fig](#pbio.3000548.s002){ref-type="supplementary-material"}). Starving *upd2* mutants did not further decrease their sleep, which is already almost floored during the day ([S3 Fig](#pbio.3000548.s003){ref-type="supplementary-material"}).

Upd2 secretion from FBs regulates feeding and sleep quality {#sec004}
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The *Drosophila* FB is the main tissue for energy storage, fulfilling functions similar to mammalian adipose tissue and liver \[[@pbio.3000548.ref054],[@pbio.3000548.ref055]\]. In *Drosophila*, Upd2 is mainly secreted from the FB \[[@pbio.3000548.ref033]\]. To test the role of adult Upd2 secretion on sleep and feeding, we used RNA interference (RNAi), expressed via a FB-specific driver line (*yolk-GAL4*), which expresses only in adult flies \[[@pbio.3000548.ref056]\]. We measured food consumption in flies in which *upd2* had been down-regulated in the FB, specifically, to determine if this recapitulated the effects seen in *upd2* mutants. This is indeed what we found: nighttime feeding was significantly increased (hyperphagia) compared with both genetic controls ([Fig 2A](#pbio.3000548.g002){ref-type="fig"}). Daytime feeding was not different from controls, although a trend towards overall increased feeding was noted ([S4 Fig](#pbio.3000548.s004){ref-type="supplementary-material"}). Importantly, this shows that *upd2*-down-regulated flies are not undernourished, compared with controls. Also similar to the *upd2* mutant phenotype, FB-specific *upd2* knockdown resulted in a significant suppression of daytime sleep ([Fig 2B and 2C](#pbio.3000548.g002){ref-type="fig"}), which was also more fragmented ([S5 Fig](#pbio.3000548.s005){ref-type="supplementary-material"}). Together with the preceding results, this suggests that the cytokine signal affecting sleep and feeding emanates from the FB.

![*upd2* expression in the FB is required for sleep and feeding regulation.\
(A) Daytime feeding (left panel, white) in flies with FB *upd2* knockdown (orange) was similar to genetic controls; *yolk-GAL4/+* (black) and *UAS-upd2RNAi/+* (gray). Nighttime feeding (right panel, black) was significantly increased in knockdown flies. (B) The 24-hour sleep profile of flies with *upd2* knockdown (orange line) compared with parental controls (black, *yolk-GAL4/+*; gray, *UAS-upd2RNAi/+*) (mean ± SEM, *n* = 24--28 per genotype). (C) Sleep duration in knockdown flies was significantly reduced during the day against both controls. Nighttime sleep was only reduced compared with *UAS-upd2RNAi/+*. (D) Muscle-specific knockdown of *upd2* by using *24B-GAL4* (maroon) did not alter day or nighttime food intake or (E-F) sleep duration. (G) Knockdown of *upd2* pan-neuronally using *R57C10-GAL4* (purple) also had no effects on food intake or (H-I) on sleep (*n* = 16--17). One-way ANOVA (with Tukey's post hoc test) for normally distributed data or Kruskal-Wallis test (with Dunn's multiple comparison test) for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. White and black bars indicate day and night, respectively. In A, D, and G, *n* = 5 flies per data point (chamber). The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. FB, fat body; GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.](pbio.3000548.g002){#pbio.3000548.g002}

*upd2* is also found to be expressed in muscle tissue \[[@pbio.3000548.ref033],[@pbio.3000548.ref057]\], so it remained possible that the cytokine was not localized to the FB. To determine whether the effect on sleep and food consumption was specific to expression in the FB, we assessed the effect of *upd2* knockdown in muscle tissue. For this, we employed a muscle-specific driver, *24B-GAL4* \[[@pbio.3000548.ref058]\]. We found that muscle-specific knockdown of *upd2* had no effect on food consumption or sleep behavior ([Fig 2D, 2E and 2F](#pbio.3000548.g002){ref-type="fig"}; [S4 Fig](#pbio.3000548.s004){ref-type="supplementary-material"}). We then asked whether down-regulation of *upd2* in neuronal tissue could lead to a starved-like state. One of the other *unpaired* ligands, *upd1*, is expressed in a small cluster of cells in the brain \[[@pbio.3000548.ref051]\], but it is currently unknown if *upd2* is expressed in neurons. Pan-neuronal knockdown (via *R57C10-GAL4* \[[@pbio.3000548.ref059]\]) of *upd2* had no effect on food consumption ([Fig 2G](#pbio.3000548.g002){ref-type="fig"}, [S4 Fig](#pbio.3000548.s004){ref-type="supplementary-material"}). We noted significantly decreased daytime sleep compared with one of the genetic controls ([Fig 2H and 2I](#pbio.3000548.g002){ref-type="fig"}), suggesting a possible neural source for the cytokine's effect on sleep. However, our combined data so far support the conclusion that the most robust effects of *upd2* on both sleep and feeding result from its secretion from the FB. We therefore subsequently focused on Upd2 signaling from the FB.

Decreased sleep duration does not necessarily imply decreased sleep quality; flies could be sleeping more deeply in shorter bouts and thereby still achieving key sleep functions \[[@pbio.3000548.ref053],[@pbio.3000548.ref060]\]. We therefore next investigated sleep intensity in *upd2*-down-regulated flies. To measure sleep intensity, we delivered a series of vibration stimuli every hour and analyzed the proportion of sleeping flies responding to the stimulus ([Fig 3A](#pbio.3000548.g003){ref-type="fig"}). We binned the flies into 10-minute sleep duration groups, which describes how long flies were asleep prior to the vibration stimulus ([Fig 3A](#pbio.3000548.g003){ref-type="fig"}). For example, if a fly had last moved 25 minutes before the stimulus, that fly would be placed in the 21--30-minute bin, in order to determine average behavioral responsiveness (i.e., sleep intensity) for all flies in that time bin. Thus, all flies received the exact same number of stimuli, although their responsiveness varied as a function of their prior sleep duration. As in previous studies, behavioral responsiveness was qualitative, meaning that any movement above a minimum threshold was indicative of a fly having been awakened (see [Materials and methods](#sec010){ref-type="sec"}). To accurately assess sleep intensity, we ensured that all sleep duration groups had a sufficient number of arousal-probing events in all of our genetic variants ([Fig 3B](#pbio.3000548.g003){ref-type="fig"}). Because daytime sleep bouts were rarely longer than 30 minutes, there were comparatively fewer probing events possible for longer daytime sleep bouts ([Fig 3B](#pbio.3000548.g003){ref-type="fig"}). We found that down-regulating *upd2* in the FB resulted in significantly lighter daytime and nighttime sleep for almost all sleep durations, compared with genetic controls ([Fig 3C and 3D](#pbio.3000548.g003){ref-type="fig"}). This suggests that lack of a cytokine signal from the FB results in overall decreased sleep intensity, in addition to decreased sleep duration and increased feeding. One interpretation of these results is that these animals sleep less so that they can look for food instead.

![*upd2* down-regulation decreases sleep intensity.\
(A) A vibration stimulus was presented to flies to measure sleep intensity. A stimulus train consisted of five 0.2-second pulses presented once every hour. Flies were binned according to their pre-stimulus sleep duration (10-minute bins). Reaction proportion represents the percent of immobile animals that responded to the stimulus train (see [Materials and methods](#sec010){ref-type="sec"}). (B) The number of animals in each sleep duration bin for both day and night were similar in both genetic controls (black and gray) and in knockdown flies (orange). (C) Knockdown flies slept more lightly for most sleep durations, for both day and (D) night; *n* = 24--28 per genotype. Flies analyzed in this figure are from the same data set as in [Fig 2A--2C](#pbio.3000548.g002){ref-type="fig"}. One-way ANOVA (with Tukey's post hoc test) for normally distributed data or Kruskal-Wallis test (with Dunn's multiple comparison test) for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. White and black bars indicate day and night, respectively. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. GAL4, galactose-responsive transcription factor; n.s., nonsignificant; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.](pbio.3000548.g003){#pbio.3000548.g003}

Tracking sleep and feeding behavior in individual animals {#sec005}
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In our preceding experiments, we found that *upd2* down-regulation has correlated effects on sleep and feeding behavior, although these observations were made using different assays for different sets of flies. To confirm our findings and to further our understanding of relationship between sleep and feeding, we devised a novel open-field paradigm wherein we could monitor sleep and feeding-related behaviors in the same animals. In this setup, individual flies were housed in circular arenas provisioned with standard (solid) fly food in the center of each chamber ([Fig 4A](#pbio.3000548.g004){ref-type="fig"}). The protocol for sleep tracking was the same as in our previous experiments in small glass tubes, except flies were tracked in two dimensions (see [Materials and methods](#sec010){ref-type="sec"}). Importantly, we confirmed that the sleep profile of *upd2* knockdown flies was similar in this open-field setup, with a significant sleep reduction and fragmentation during the day ([Fig 4B and 4C](#pbio.3000548.g004){ref-type="fig"}; [S6 Fig](#pbio.3000548.s006){ref-type="supplementary-material"}). This shows that the reduced sleep phenotype manifests itself in different types of chambers (circular versus linear).

![Simultaneous tracking of sleep and feeding-related behavior in an open-field arena.\
(A) Schema of the open-field tracking system. Flies are housed individually in circular arenas, radius = 16 mm. Each arena has a food cup in the center (radius = 5 mm). Fly activity is monitored via a webcam and analyzed with DART. (B) The 24-hour sleep profile of flies with *upd2* knockdown (orange line) compared with genetic controls. Sleep was tracked for 3 days (*n* = 15--17 per genotype) (black, *yolk-GAL4/+*; gray, *UAS-upd2RNAi/+*). (C) FB-specific *upd2* knockdown in open-field arena showed a similar phenotype as in tubes (orange bars). (D) Exemplary average 2D heatmap position preference plots for day and night, warmer colors indicate a higher probability of flies being in that position. (E) Number of feeding events for each genotype over 3 days. Total feeding events in knockdown flies was only significant compared with one of the parental controls, *UAS-upd2RNAi/+*. (F) *upd2* knockdown flies had an increase in feeding events during nighttime. Daytime feeding count was increased, as well, but was only significantly different from one of the parental controls (*UAS-upd2RNAi/+*). One-way ANOVA (with Tukey's post hoc test) for normally distributed data or Kruskal-Wallis test (with Dunn's multiple comparison test) for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. White and black bars indicate light and darkness, respectively. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. DART, *Drosophila* arousal tracking; FB, fat body; GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.](pbio.3000548.g004){#pbio.3000548.g004}

The circular arena setup allowed us to track the absolute location of flies in two dimensions, to show if they had any place preference. Heat plots revealed that flies frequently visited the food cup located in the center of the arena ([Fig 4D](#pbio.3000548.g004){ref-type="fig"}), presumably to feed. We devised an automated system to quantify feeding-related behavior over multiple days (see [Materials and methods](#sec010){ref-type="sec"}), to complement sleep tracking in the same individuals. Our automated analysis was optimized following visual observation, with a fly considered to be engaged in feeding-related behavior if it fulfilled four key criteria: (1) it was located at the food, (2) its speed at the food was less than 1 mm/second \[[@pbio.3000548.ref061]\], (3) it remained at the food for at least 30 seconds, and (4) it was not sleeping (i.e., immobile). Together, these criteria ensured that flies were actively interacting with the food rather than just walking past the food or sleeping at the food. In order to confirm that we were indeed tracking feeding-related behavior, we conducted a number of additional experiments. When we prevented access to the food source by covering the food cup with parafilm, this significantly decreased the number of "feeding" visits to the center of the arena ([S7A Fig](#pbio.3000548.s007){ref-type="supplementary-material"}). When we introduced starved flies into the arena, this doubled the number of visits to the food cup ([S7B Fig](#pbio.3000548.s007){ref-type="supplementary-material"}). Visual inspection of fly videos taken from the above experiment confirmed that flies were indeed visiting the food cup to feed and that our behavioral criteria (e.g., the \>30-second cutoff for individual feeding events) were accurate ([S7C](#pbio.3000548.s007){ref-type="supplementary-material"}, [S7D and S7E Fig](#pbio.3000548.s007){ref-type="supplementary-material"}). When we provided diluted food, the number of feeding events did not increase as it did with starvation, but flies slept less ([S7F and S7G Fig](#pbio.3000548.s007){ref-type="supplementary-material"}), suggesting a nutritional deficit. Confident that we were indeed tracking feeding-related behavior, we proceeded to quantify the number of feeding events over three days and nights in our genetic variants. We observed a trend towards an increased number of feeding events in flies with *upd2* down-regulated in the FB, compared with genetic controls ([Fig 4E](#pbio.3000548.g004){ref-type="fig"}). When we again partitioned our analysis between day and night, we observed that *upd2* knockdown significantly increased the number of feeding events during the night ([Fig 4F](#pbio.3000548.g004){ref-type="fig"}). Importantly, these results match closely with our Café assay results for these same genetic variants ([Fig 2A](#pbio.3000548.g002){ref-type="fig"}), suggesting that increased feeding-related behavior in the circular arenas indicates increased food consumption. Additionally, our combined assays indicate that the feeding phenotype is unlikely to be an artifact of different assay conditions (e.g., the liquid food of the Café assay) or due to group housing in Café chambers. Thus, regardless of the assay employed, removing a cytokine signal from the FB reliably increases nighttime feeding, and therefore hyperphagia. The observed sleep reduction and sleep fragmentation resulting from *upd2* down-regulation aligns with the behavior of starved animals more generally \[[@pbio.3000548.ref029]\]. Moreover, the hyperphagia of *upd2* mutants suggests that a signal communicating that food has been consumed is not being integrated or processed, even if flies are well fed.

Neural correlates of starvation in the ellipsoid body R4-neurons {#sec006}
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If *upd2* mutants are failing to process a feeding-related signal, then neural evidence of this "chronically starved" state might be evident in brain activity. Several neurons in the *Drosophila* brain reflect nutritional effects, including lateral horn leucokinin (LHLK) neurons and neuropeptide F (Npf) neurons (for a review, see \[[@pbio.3000548.ref021],[@pbio.3000548.ref062]\]). Interestingly, certain neurons in the ellipsoid body (EB) in the central brain (which is involved in sleep as well as visual behaviors \[[@pbio.3000548.ref044], [@pbio.3000548.ref063]\]) appear to be responsive to starvation cues: a previous study found that acute starvation increased the activity levels of R4 neurons in the EB \[[@pbio.3000548.ref064]\]. This suggested we could utilize these neurons to determine if *upd2* knockdown animals were indeed chronically starved. We used a calcium-dependent nuclear import of LexA (CaLexA) reporter \[[@pbio.3000548.ref065]\] to visualize activity levels in the brains of *upd2* mutants, and compared these to animals that were actually starved. We expressed the reporter construct in the R4 neurons (using *R38H02-GAL4*; [S8A Fig](#pbio.3000548.s008){ref-type="supplementary-material"}), which labels the same neurons as in the aforementioned study \[[@pbio.3000548.ref064]\]. We tracked both sleep and feeding behavior in individual animals in the open-field arena (as in [Fig 4](#pbio.3000548.g004){ref-type="fig"}) for two days, after which we dissected and imaged their brains ([Fig 5A](#pbio.3000548.g005){ref-type="fig"}). In control animals, we found that activity in the R4 neurons was negatively correlated with the number of feeding events ([Fig 5B and 5C](#pbio.3000548.g005){ref-type="fig"}), but not with sleep duration ([S8B Fig](#pbio.3000548.s008){ref-type="supplementary-material"}). This suggests that the R4 neurons are indicative of nutritional status in our paradigm, but not of sleep differences among individuals.

![*upd2* mutants show increased CaLexA expression in EB R4 neurons.\
(A) Flies were housed in open-field arenas for 24 hours (starting at ZT0) for tracking sleep and feeding-related behavior. They were then collected for dissection and brains were imaged. (B) Feeding count number in open-field arenas significantly correlated with the measured CaLexA intensity of R4 neurons. (C) Sample images representing a high (number 1, upper panel) compared with a low CalexA signal (number 2, lower panel). *n* = 11; warmer colors indicate an increase in GFP intensity. (D) Representative whole-mount brain immunostaining of fed (black) and starved wild-type (blue) (*w+; R38H02-GAL4\>CaLexA*) compared with fed (red) and starved *upd2* mutant (purple) (genotype: *upd2*;*R38H02-GAL4\>CaLexA*). Maximal intensity projections are shown in pseudo color (scale bar = 20 μm). (E) Quantification of CaLexA signals (*n* = 8--13 per condition). Control flies had ad libitum access to food from days 0 to 6. Starved group was transferred to 1% agar/starvation medium on day 5 (ZT0) until day 6 (ZT0). For correlation analysis, two-tailed *P* values for Pearson's correlation coefficient are shown. Student *t* test for normally distributed data or Mann-Whitney U rank-sum test for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. Flies in B and E were from a different antibody cohort. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. CaLexA, calcium-dependent nuclear import of LexA; EB, ellipsoid body; GFP, green fluorescent protein; *upd2*, unpaired 2.](pbio.3000548.g005){#pbio.3000548.g005}

Consistent with the above data and a previous study \[[@pbio.3000548.ref064]\], we observed a significant increase in R4 neuron activity after 24 hours of starvation ([Fig 5D](#pbio.3000548.g005){ref-type="fig"}, first versus second panel; [Fig 5E](#pbio.3000548.g005){ref-type="fig"}). We then placed the CaLexA/R38H02 reporter in an *upd2* mutant background to investigate R4 neuron activity in these flies. Interestingly, *upd2* mutants display increased R4 neuron activity, like starved flies, even though they had ad libitum food access and consumed amounts similar to fed controls ([Fig 5D](#pbio.3000548.g005){ref-type="fig"}, third panel). The average activity of R4 neurons (quantified by GFP intensity) in the mutants was comparable to the level observed in starved wild-type flies ([Fig 5E](#pbio.3000548.g005){ref-type="fig"}). Starving *upd2* mutants did not further increase R4 activity levels ([Fig 5D and 5E](#pbio.3000548.g005){ref-type="fig"}). Overall, our results support the conclusion that lack of the Upd2 signal produces a chronically starved-like state, associated with this salient neural signature in the fly's central brain. We therefore refer to *upd2* down-regulation as a chronic starvation signal from here on.

*upd2* down-regulation in the FB sharpens visual selective attention {#sec007}
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Sleep-deprived flies have been shown to have deficits in learning and memory and visual attention tasks \[[@pbio.3000548.ref045],[@pbio.3000548.ref047],[@pbio.3000548.ref066]\]. Yet starvation-induced sleep loss seems to preserve performance in some behavioral assays \[[@pbio.3000548.ref049]\]. Indeed, many behavioral studies exploit starvation as a way to increase motivation and to even improve performance \[[@pbio.3000548.ref067]--[@pbio.3000548.ref069]\]. Since most sleep-monitoring assays for *Drosophila* do not provide much insight into behavioral processes beyond locomotion, it remains unclear how starvation might preserve or improve behavioral performance in spite of less sleep.

We investigated whether down-regulating *upd2* affected visual selective attention. To study visual attention in flies, we used a modified version of Buridan's paradigm to track visual fixation behavior in freely walking animals ([Fig 6A](#pbio.3000548.g006){ref-type="fig"}) \[[@pbio.3000548.ref070],[@pbio.3000548.ref071]\]. To ensure that vision was normal in our genetic variants, we outcrossed them to *white*^*+*^ so that their eye pigmentation was wild-type (see [Materials and methods](#sec010){ref-type="sec"}). We then proceeded to test them first for simple visual behaviors, namely object fixation \[[@pbio.3000548.ref072],[@pbio.3000548.ref073]\] and optomotor responsiveness \[[@pbio.3000548.ref074],[@pbio.3000548.ref075]\]. Fixation behavior was not different from controls in *upd2* knockdown flies: animals responded normally to two opposing "target" bars by walking decisively back and forth between them (measured by their low target deviation angle, see [Materials and methods](#sec010){ref-type="sec"}) ([Fig 6A](#pbio.3000548.g006){ref-type="fig"}). Optomotor behavior was also not significantly impacted in *upd2* knockdown animals ([Fig 6B](#pbio.3000548.g006){ref-type="fig"}), suggesting these flies are able to perceive motion normally, along with being able to fixate on target objects. To investigate visual attention, we combined the two different kinds of visual stimuli (target and optomotor) in a visual competition scenario ([Fig 6C](#pbio.3000548.g006){ref-type="fig"}), which allowed us to measure how much the moving grating distracted flies from the target stimulus \[[@pbio.3000548.ref047]\]. Surprisingly, in this attention paradigm, *upd2* knockdown animals performed significantly better than controls, meaning that they were less distracted by the moving grating ([Fig 6D](#pbio.3000548.g006){ref-type="fig"}).

![Visual behavior in *upd2* deficient flies.\
(A) Fixation behavior in Buridan's paradigm, in which flies were presented with two opposing black bars flickering at 7 Hz. Fixation responses were calculated by target deviation in degrees and was nonsignificant between genetic controls (gray and black bars) and knockdown flies (orange bar) (*n* = 15). (B) Optomotor response to motion stimulus (3-Hz grating) determined by the fly's turning angle (degrees per second) for genetic controls (gray and black bars) and knockdown flies (orange bar) (*n* = 11). (C) For visual competition (attention) experiments, flies were presented with the two flickering bars (fixation targets) and the 3-Hz grating (distractor) together. The target deviation angle represents a measure of distractibility. Representative traces show the trajectories of single flies with low (right), average (middle), and high (left) target deviation angles. (D) Target deviation for knockdown flies (orange bar) was significantly lower compared with genetic controls, indicating less distractibility (*n* = 21). One-way ANOVA with Tukey correction was used for comparing different conditions. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.](pbio.3000548.g006){#pbio.3000548.g006}

*upd2* mutants sleep less than control flies (Figs [1](#pbio.3000548.g001){ref-type="fig"}--[4](#pbio.3000548.g004){ref-type="fig"}), but it is unclear if they are sleep deprived. Sleep-deprived wild-type flies sleep more deeply after deprivation \[[@pbio.3000548.ref076]\], to presumably recover lost deep sleep functions. Contrary to this, *upd2* knockdown flies sleep more lightly ([Fig 3](#pbio.3000548.g003){ref-type="fig"}). Sleep deprivation also impairs attention in flies \[[@pbio.3000548.ref047]\], contrary to our results in *upd2* knockdown animals. This made us question if the increased visual focus observed in *upd2*-deficient animals was a failure of attention, rather than improved attention. In other words, if attention is optimal in well-rested wild-type animals \[[@pbio.3000548.ref077]\], a decreased capacity to detect a distracting stimulus (e.g., a moving grating) might be viewed as defective rather than improved attention. To address this conundrum, we decided to restore normal levels of sleep to *upd2* mutants, to see if this returned their visual attention to control levels. To increase sleep in *upd2* mutants, we used a sleep-promoting drug, THIP (4,5,6,7- tetrahydroisoxazolo-\[5,4-c\]pyridine-3-ol). Previous work has shown that THIP-induced sleep restores behavioral plasticity and attention to *Drosophila* mutants \[[@pbio.3000548.ref028],[@pbio.3000548.ref078]\], so we were curious if increased sleep in *upd2* knockdown animals would return distractibility to control levels, which would argue that the increased fixation observed in these animals was a failure of attention corrected by sufficient sleep. As expected, THIP exposure increased sleep in *upd2* knockdown animals ([Fig 7A](#pbio.3000548.g007){ref-type="fig"}, compare with [Fig 2C](#pbio.3000548.g002){ref-type="fig"}). However, THIP exposure in *upd2* knockdown animals did not change their visual attention phenotype compared with similarly treated controls ([Fig 7B](#pbio.3000548.g007){ref-type="fig"}). This suggests that the increased focus (or decreased distractibility) in these flies is a direct feature of their starved-like state rather than suboptimal attention processes resulting from insufficient sleep.

![Additional sleep does not restore attention in *upd2* knockdown flies.\
(A) Total sleep duration in THIP-fed flies (0.1 mg/mL) was increased (compared with the same genotypes in [Fig 2](#pbio.3000548.g002){ref-type="fig"}). *upd2* knockdown flies still showed a significant decrease in their sleep duration compared with genetic controls (*n* = 17--19). (B) Additional sleep via THIP did not rescue the improved attention phenotype of flies with FB *upd2* knockdown (orange, *yolk-GAL4\>upd2RNAi*) compared with controls (*yolk-GAL4/+*, black, and *UAS-upd2RNAi/+*, gray) (*n* = 12--13). One-way ANOVA with Tukey correction was used for comparing different conditions. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. FB, fat body; GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence *upd2*, unpaired 2.](pbio.3000548.g007){#pbio.3000548.g007}

We showed earlier that *upd2* down-regulation in the FB decreased sleep, while down-regulation in the muscle had no effect on sleep and down-regulation in the nervous system had some effects ([Fig 2](#pbio.3000548.g002){ref-type="fig"}). To determine whether this FB specificity extended to visual attention behavior as well, we next examined whether down-regulation of Upd2 in these other tissues (i.e., muscle and neurons) also altered visual attention. We found that *upd2* knockdown in muscle and neurons had no effect on visual behaviors or on visual attention ([S9 Fig](#pbio.3000548.s009){ref-type="supplementary-material"}). This confirms the *Drosophila* FB as a relevant cytokine source for producing this suite of associated effects (decreased sleep, hyperphagia, and improved attention).

Knockdown of *dome*, the *upd2* receptor in Ilp2 neurons, recapitulates *upd2* knockdown phenotypes {#sec008}
---------------------------------------------------------------------------------------------------

We next investigated how down-regulation of *upd2* might be signaling a starvation signal to the fly brain. The *upd2* receptor, *dome*, is expressed in several subsets of neurons, including mushroom body neurons, Npf neurons, and in the pars intercerebralis (PI) region of the fly brain, where Ilp neurons are located \[[@pbio.3000548.ref035],[@pbio.3000548.ref051]\]. *Drosophila* Ilp neurons share functional similarities with mammalian insulin cells and are involved in nutrient sensing \[[@pbio.3000548.ref079],[@pbio.3000548.ref080]\]. Ilp2, one of the three Ilps expressed in the insulin-producing cells (IPCs), ([Fig 8A](#pbio.3000548.g008){ref-type="fig"}), has been previously shown to alter sleep and feeding \[[@pbio.3000548.ref081],[@pbio.3000548.ref082]\]. We therefore employed an RNAi strategy to down-regulate *dome* in Ilp2 neurons ([Fig 8B](#pbio.3000548.g008){ref-type="fig"}). As before, we investigated sleep, feeding, and visual behaviors. Remarkably, these mirrored closely all of the *upd2* knockdown phenotypes: daytime sleep in *dome* knockdown flies was decreased and more fragmented compared with genetic controls ([Fig 8C and 8D](#pbio.3000548.g008){ref-type="fig"}; [S10 Fig](#pbio.3000548.s010){ref-type="supplementary-material"}). *dome* knockdown flies also displayed significantly more feeding-related behavior at night ([Fig 8E](#pbio.3000548.g008){ref-type="fig"}) and a trend to overall more feeding in general ([S11 Fig](#pbio.3000548.s011){ref-type="supplementary-material"}). Finally, when we investigated visual attention in *dome* knockdown animals, we saw significantly improved visual attention, compared with genetic controls ([Fig 8F](#pbio.3000548.g008){ref-type="fig"}), while there were no differences in their simple visual behaviors ([S12 Fig](#pbio.3000548.s012){ref-type="supplementary-material"}). These results suggest that the brain integrates the peripheral Upd2 signal secreted from the FB via the Dome receptor in Ilp2 neurons, among perhaps other neurons, to simultaneously decrease sleep while sharpening visual attention.

![Knockdown of *dome*, the *upd2* receptor, in Ilp2- expressing neurons, recapitulates *upd2* knockdown phenotypes.\
(A) *Ilp2-GAL4* neurons are located in the PI region of the brain. Whole-mount brain image, maximum projection, scale bar = 100 μm. (B) Schema illustrating the integration of *upd2* signal via *dome* receptor in Ilp2 neurons. (C) The 24-hour sleep profile of flies with knockdown of *dome* receptor in Ilp2 neurons (blue) compared with genetic controls (*Ilp2-GAL4/+*, black; *UAS-domeRNAi/+*, light gray). Sleep was measured over 3 days in open-field arenas. (D) Daytime sleep was significantly reduced in knockdown flies, whereas nighttime sleep was not affected. (E) The number of feeding events showed a significant increase at nighttime. (F) Target deviation angle of flies with *dome* knockdown was significantly lower compared with genetic controls, indicating less distractibility (*n* = 13--15). For sleep analysis, 3--5-day-old adult females virgins were used, *n* = 15--17 per genotype. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; one-way ANOVA, error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. *dome*, *domeless*; GAL4, galactose-responsive transcription factor; GFP, green fluorescent protein; Ilp2, insulin-like peptide 2; PI, pars intercerebralis; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.](pbio.3000548.g008){#pbio.3000548.g008}

Discussion {#sec009}
==========

Sleep has been found to be necessary for maintaining cognitive properties such as memory, attention, and decision-making in several animals, including flies and humans \[[@pbio.3000548.ref017],[@pbio.3000548.ref047],[@pbio.3000548.ref078],[@pbio.3000548.ref083]--[@pbio.3000548.ref086]\]. This is because sleep most likely accomplishes a variety of important conserved functions, for any animal brain \[[@pbio.3000548.ref060],[@pbio.3000548.ref077]\]. It is therefore remarkable that sleep can under some circumstances be suppressed, as in the case of starvation. This suggests downstream pathways that can override some of the deleterious effects of sleep deprivation, especially effects relating to cognitive performance. That such mechanisms should exist seems adaptive: starving animals need to find food quickly, rather than sleep.

One important category of decisions animals make on a daily basis is their food choices and how to source them. In humans, insufficient sleep has been found to alter desire for food and increase the preference for high caloric foods \[[@pbio.3000548.ref087]\]. Additionally, nutrition strongly alters sleep amount and sleep quality \[[@pbio.3000548.ref088]\]. Poor food choices are the most common cause of metabolic disorders such as type 2 diabetes and obesity \[[@pbio.3000548.ref089],[@pbio.3000548.ref090]\], and these disorders are also associated with sleep disorders \[[@pbio.3000548.ref091],[@pbio.3000548.ref092]\]. Yet, how sleep and feeding decisions influence each other is not well understood, and hard to disentangle in human patients. Manipulating satiation cues in animal models provides a way to establish some level of causality that is hard to achieve by starvation per se. By manipulating a genetic starvation signal in *Drosophila*, we were able to investigate effects on sleep, feeding, and attention without the confound of actual starvation, which obviously can affect behavioral performance for a variety of spurious reasons.

A key roadblock toward addressing these questions in the *Drosophila* model has been a lack of paradigms in which sleep quality and feeding behavior can be quantified in the same animals. Studying both behaviors simultaneously has been difficult given the small size of flies and their miniscule food intake. So far, the only platform which allowed this was the Activity Recording Capillary (ARC) Feeder \[[@pbio.3000548.ref052],[@pbio.3000548.ref093]\]. In our current study, we provide an alternative approach to measuring both behaviors by using a solid food source in open-field arenas amenable to visual tracking for sleep experiments. Our combined feeding and sleep paradigm confirmed a role for *upd2* in regulating feeding and sleep behavior in *Drosophila*. In future work, similar combined platforms (e.g., FlyPad \[[@pbio.3000548.ref094]\]) could be employed to provide a more precise assessment of feeding alongside sleep monitoring. This is important, as there is clearly substantial individual variability in feeding and sleep behavior ([Fig 5B](#pbio.3000548.g005){ref-type="fig"}, [S7 Fig](#pbio.3000548.s007){ref-type="supplementary-material"}). Our results nevertheless showed a close correspondence between feeding related behavior and food ingestion. However, feeding-related behavior and actual food consumption may not always be entirely correlated. Increased feeding-related behavior in well-fed flies may be more suggestive of a missing satiety cue than increased consumption per se, because of a likely ceiling effect on how much food flies can consume.

Satiety requires signals from peripheral tissues to terminate food intake. The FBs are the main energy storage site in flies and fulfill functions similar to mammalian adipose tissue and liver \[[@pbio.3000548.ref054]\]. In *Drosophila*, *upd2* is secreted from the FBs upon feeding \[[@pbio.3000548.ref037]\]. Similarly, the mammalian ortholog leptin is secreted by adipose tissue and is received as a satiety signal by the brain to inhibit appetite \[[@pbio.3000548.ref038],[@pbio.3000548.ref095]\]. Leptin levels in the blood change upon food intake \[[@pbio.3000548.ref096]\] and are shown to fluctuate in a circadian manner \[[@pbio.3000548.ref097]\]. Interestingly, a number of studies have found that leptin levels in rodents are significantly decreased in response to sleep deprivation \[[@pbio.3000548.ref098]--[@pbio.3000548.ref101]\]. On the other hand, leptin-deficient mice (Lepob/ob) have been found to have altered sleep architecture, with increased non-rapid eye movement (NREM) sleep and shorter sleep bouts \[[@pbio.3000548.ref102]\]. These studies suggest a homeostatic relationship between leptin levels and sleep amount or quality. Our study suggests that Upd2 in flies plays a similar role to leptin in the simultaneous regulation of feeding and sleep.

It is, however, unclear if *upd2* mutants need more sleep. It seems likely that key sleep functions are not being satisfied in *upd2* mutants, such as functions that might be associated with deeper sleep, for example. Nevertheless, these animals have improved visual attention, and imposing more sleep does not change their sharpened attention phenotype. One possibility is that *upd2* knockdown promotes a different kind of sleep that optimizes attention specifically. Alternatively, the satiety cue provided by *upd2* might be directly modulating attention-like behavior, irrespective of parallel effects on sleep. A direct effect of *upd2* on improving attention might ensure optimal cognitive performance in the face of suppressed sleep functions. In other words, if sleep is going to be sacrificed in order to promote foraging and feeding, then this behavior should be optimized rather than degraded. Although we did not examine foraging behavior in our study, our visual attention experiments probe a fundamental cognitive process (distractibility) that could affect many different kinds of behaviors, including foraging. Interestingly, we found that simple visual behaviors remained unaffected in *upd2* or *dome* knockdown animals. Similarly, sleep deprivation does not seem to affect visual fixation and optomotor behavior in *Drosophila* \[[@pbio.3000548.ref047]\]. One interpretation of our results is that the decreased level of distractibility in the knockdown animals reflects improved attention, which might be an adaptation for finding appropriate food resources under suboptimal nourishment conditions. While we have not completely excluded the possibility that this could be a form of impaired attention (to be less distractible can be maladaptive, as in autism), such a sharpened focus on innately attractive objects (for a walking fly, a dark bar is attractive \[[@pbio.3000548.ref026]\]) could be seen as beneficial when animals are foraging for food.

Along with attention and sleep, another behavior that is significantly altered in *upd2* mutants as well as knockdown animals is feeding itself, with nighttime hyperphagia being a recurrent observation using two different assays. Does decreased sleep quality alter feeding (and thus *upd2* levels), or do altered feeding patterns affect sleep quality? It remains difficult to disambiguate causality in this regard in humans, and even in animal models. In our *Drosophila* experiments, we found an increase in R4 neuronal activity in *upd2* mutants, which we propose reflects their nutritional status, as these neurons have been shown to respond under starvation regimes \[[@pbio.3000548.ref103]\]. This suggests that a persistent hunger signal, reflected in R4 neuronal activity, may underlie the decreased sleep phenotype. Considering the similarities between leptin/upd2 regulation, future studies temporally manipulating this hunger cue at different points in the signaling pathway should be able to disambiguate causal links between sleep need and nutritional status. It will also be of interest in future research to determine if there is a long-term cost to sacrificing sleep by down-regulating satiety cues to the brain.

Our findings suggest that IIp2 cells are a key portal in the fly brain for integrating hunger signals and translating these into appropriate behavioral programs. How signaling from IIp2 cells in the fly brain leads to improved visual attention remains unknown. In fly larvae, Ilp2 is released within the brain and acts on a subset of neurons (e.g., Hugin neurons \[[@pbio.3000548.ref104]\]) via insulin receptors. So it is possible that, in adults, insulin-expressing cells such as Ilp2 may target the EB indirectly via insulin receptors in the EB, and that this would regulate selective attention by tuning circuits in the central complex that affect visual behavior more broadly \[[@pbio.3000548.ref105],[@pbio.3000548.ref106]\]. Furthermore, signals downstream of the insulin receptors have been shown to regulate gene expression, such as for other receptors \[[@pbio.3000548.ref107],[@pbio.3000548.ref108]\], so insulin signaling from the Ilp2 (or other Ilp) cells might have far-reaching effects on neuronal functions. Alternatively, insulin-expressing cells might be communicating more directly to arousal-regulatory circuits in the central complex, by way of gap junctions, for example \[[@pbio.3000548.ref109]\], to directly modulate behavioral responsiveness levels. Future experiments testing either of those possibilities should reveal the downstream mechanisms involved.

Materials and methods {#sec010}
=====================

Fly stocks and maintenance {#sec011}
--------------------------

Flies were raised on standard agar-yeast-based food at 25°C, 50%--60% humidity with 12-hour light: 12-hour dark cycle. Starvation experiments were performed on 1% agar food for 24 hours. *upd2*^*Δ3--62*^ was kindly provided by J. Hombria \[[@pbio.3000548.ref033]\]. The following stocks were from Bloomington Drosophila Stock Center (BDSC), *yw* (\#1495), *yolk-GAL4* (\#58814), *UAS-upd2RNAi* (\#33988, HMS00901) \[[@pbio.3000548.ref110]\], *UAS-domeRNAi* (\#32860), *Ilp2*-Gal4 (\#37516), *24B-GAL4* (\#1757), *R57C10-GAL4* (\#39171), *R38H02-GAL4* (\#47352) \[[@pbio.3000548.ref111]\], *UAS-CaLexa* (\#66542) \[[@pbio.3000548.ref065]\]. Flies were crossed into a *w+* (*Canton-S*) background (lab stock, originally obtained from BDSC) for all knockdown experiments.

Sleep and sleep intensity measurements {#sec012}
--------------------------------------

DART software was used for sleep tracking and analysis \[[@pbio.3000548.ref053]\]. Three-to-five-day-old virgin females were placed into 65-mm glass tubes (Trikinetics, Waltham, MA). A Logitech webcam (c9000 or c920) was used for sleep recordings with 5 frames per second. Sleep parameters were calculated according to traditional fly sleep criteria (Shaw and colleagues, 2000; Hendricks and colleagues, 2000), where sleep is defined as inactive durations for 5 minutes or more (a "sleep bout"). These were binned for every hour ("sleep minutes/hour"). For sleep intensity measurements, a vibration stimulus (a train of five 200-ms pulses set at 3.0 G) using motors (Precision Microdrives, 312--101) was presented every hour to measure behavioral responsiveness. Details of the software and calculations are described in Faville and colleagues, 2015. Behavioral responsiveness was registered if a fly moved 3 mm or more within 60 seconds of the vibration stimulus, with any movement within that time frame identified as an awakening. Responsiveness data were binned into 10-minute prior immobility epochs, depending on when flies had last shown any movement since the vibration stimulus, from 0 to 60 minutes.

Open-field behavioral analysis {#sec013}
------------------------------

The platform was custom made with white acrylic sheet. Each platform consisted of 36 individual chambers 36 mm in diameter and 2.5 mm in height. The center of the chamber had a hole with a 3-mm depth and 5-mm width, where food was placed. The platform was covered with a transparent acrylic sheet.

Each food area was pre-filled with a layer of 1% agar to maintain the moisture of the food. Once solidified, it was covered with a layer of regular fly food.

Flies were transferred without anesthesia by using a mouth aspirator and acclimatized for minimum of 12 hours prior to experiment. Fly sleep tracking and analyses were performed using DART \[[@pbio.3000548.ref053]\] with custom-made MATLAB (Mathworks Natick, MA) scripts. Kinematic calculations were performed as previously described \[[@pbio.3000548.ref112]\]. For feeding analysis, the food area was detected using the Matlab function "imfindcircles" (object polarity was set to Dark). A fly was considered as feeding if it fulfilled four criteria: (1) distance from the feeding region (0 mm from food pit), (2) speed ≤1 mm/second, (3) time spent feeding \>30 seconds, and (4) flies were not sleeping (see above). The number of feeding events represent the total number of events throughout the recording.

Café assay {#sec014}
----------

The assay was slightly modified from the previously published versions (Ja and colleagues, 2007). Virgin female flies (6--8 days old) were used. Every testing chamber had 1% agar on the bottom, to eliminate the possibility of desiccation. Food (5% w/v sucrose, Sigma Aldrich) was presented in 5-μL micropipettes (VWR, Westchester, PA) and the level of the meniscus was measured over time. For each condition, 6--8 chambers were set, with 4--5 flies in each chamber. The experiments for different conditions were performed on the same day starting at ZT0-1, in an incubator with 25°C and 50%--55% relative humidity. At least 5 empty chambers without flies were used to control for the effects of evaporation.

Visual paradigm {#sec015}
---------------

A modified version of Buridan's paradigm was used to assay for visual attention \[[@pbio.3000548.ref047]\]. Visual cues were presented on light-emitting diode (LED) panels. Each LED panel contained 1,024 individual LED units (32 rows by 32 columns) and was controlled via an LED Studio software (Shenzen Sinorad, Medical Electronics, Shenzen, China). Visual stimuli were created in Vision Egg software \[[@pbio.3000548.ref113]\], written in Python programming language (L. Kirszenblat and Y. Zhou).

Three different visual cues were tested.

1.  A moving grating (3 Hz) for testing the optomotor response behavior.

2.  Two opposing flickering bars (7 Hz) for testing fixation behavior.

3.  Competition stimulus (figure-ground), with both grating and opposing flickering bars for testing selective visual attention.

Fixation and optomotor experiments lasted 1 minute. Figure-ground experiments lasted 3 minutes, during which the grating (clockwise or counterclockwise) was switched after 1.5 minutes.

For each test, female flies were collected as virgins and kept in vials in groups of 15--20 per vial. On day 2, their wings were cut under CO~2~ anesthesia and they were placed into fresh vials. They were given 2 days to recover from the effects of CO~2~ anesthesia. Tests were performed on a round platform (R = 86 mm) surrounded by a water-filled moat to prevent escape. The visual stimuli were alternated between each experiment from being presented on the horizontal or the vertical axes. Optomotor experiments were alternated between clockwise and counterclockwise gratings (1.5 minutes each). LED panels formed a hexagon, surrounding the platform (29-cm diameter, 16-cm height). The dark bar was 9 degrees in width and 45 degrees in height from the center of the arena. A camera (SONY Hi Resolution Colour Video Camera CCD-IRIS SSC-374) placed above the arena was used for tracking the movement of the fly on the platform at 30 frames per second. The open-source tracking software was used to record the position of the fly (Colomb and colleagues, 2012)

Visual responses were analyzed by using CeTran (3.4) software (Colomb and colleagues, 2012) and custom-made scripts in R programming language (L. Kirszenblat and Y. Zhou). Target deviation was calculated as the smallest angle between the fly's trajectory and either of the vertical stripes (Colomb and colleagues, 2012). Optomotor index was calculated as the angular velocity (turning angle/second) in the direction of the moving grating.

Pharmacology {#sec016}
------------

THIP, also known as gaboxadol, was dissolved in standard food at 0.1 mg/mL for two days. For sleep experiments, flies were transferred to tubes containing THIP-laced regular food. For visual behavior experiments, flies were transferred to regular food 1 hour prior to testing, as described previously \[[@pbio.3000548.ref078]\].

Immunohistochemistry and confocal Imaging {#sec017}
-----------------------------------------

Flies were collected under CO~2~ anesthesia and transferred to a drop of 1× PBS for dissection. After dissection, brains were transferred to a mini PCR-tube with 200 μL of 1× PBS. All of the following steps were performed on a rotator with 27 rpm at room temperature. Brains were fixed with 4% paraformaldehyde diluted in PBS-T (1× PBS, 0.2 Triton-X 100) for 20--30 minutes, followed by 3 washes in PBS-T. They were then blocked with 10% goat serum (Sigma Aldrich, St. Louis, MO) for 1 hour, followed by overnight primary antibody incubation. On the second day, primary antibody was removed and brains were washed 3× with PBS-T. Then the secondary antibody was added and the tube was covered with aluminum foil for overnight incubation. On day 3, secondary antibody was removed and brains were washed with PBS-T. Primary antibodies were rabbit anti-GFP 1:1,000 (Invitrogen), mouse anti-nc82 1:10 (DSHB). Secondary antibodies were anti-rabbit 488, 1:250 (Invitrogen), anti-mouse 647, 1:250 (Invitrogen). Brains were then transferred to microscope slides and mounted on a drop of Vectashield (Vector Laboratories, Burlingame, CA) for imaging. Images were acquired on a spinning-disk confocal system (Marianas; 3I) consisting of an Axio Observer Z1 (Carl Zeiss) equipped with a CSU-W1 spinning-disk head (Yokogawa Corporation of America), ORCA-Flash4.0 v2 sCMOS camera (Hamamatsu Photonics), 20× 0.8 NA PlanApo, and 100× 1.4 NA PlanApo objectives were used and image acquisition was performed using SlideBook 6.0 (3I).

For CaLexA experiments, the same acquisition settings were used between different conditions. Fiji (ImageJ) was used for image processing. GFP intensity measurements were done using the Fiji intensity measurement plug-in.

Statistical analyses {#sec018}
--------------------

Statistical analyses were performed using Prism 7.0a (GraphPad Software). Normality tests were performed using Shapiro-Wilk normality tests. For normally distributed data, two-tailed, unpaired Student *t* test or one-way ANOVA followed by Tukey correction was performed. Unless otherwise stated, all data sets represent mean ± SEM.

Supporting information {#sec019}
======================

###### Nighttime hyperphagia of *upd2* mutants is not dependent on light entrainment.

\(A\) Total food intake for feeding experiments under constant darkness (DD) was not significantly different between control and *upd2* mutants. (B) *upd2* mutants (red) had decreased nighttime food intake compared to controls (black); daytime food intake was similar to controls (*n* = 25--35 flies with 5 flies per Café chamber). \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, Student *t* test, error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. Café, capillary feeding; *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### *upd2* mutants are more active but have comparable walking speed to controls.

\(A\) Average speed of *upd2* mutant flies (red) was not significantly different from controls (black). (B) Mutant flies had increased wake duration compared to controls. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; flies in this figure are from the same data set as in [Fig 1](#pbio.3000548.g001){ref-type="fig"}. Student *t* test for normally distributed data or Mann-Whitney U rank-sum test for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Starvation does not change sleep phenotype of *upd2* mutants.

\(A\) Flies were kept on regular food from days 0--3. On day 3 at ZT12, they were placed into tubes with either regular food or starvation media for sleep tracking. Recording was started at nighttime and followed for 24 hours. (B) Both fed (red) and starved (pink) *upd2* mutants slept significantly less during both night and (C) day compared to fed (black) and starved (blue) controls. *n* = 14--17, Student *t* test, \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Total food intake of flies with *upd2* knockdown.

\(A\) FB knockdown of *upd2* significantly increases food intake compared with UAS-*upd2*RNAi/+. (B,C) Muscle and pan-neuronal knockdown of *upd2* shows similar food intake compared with both genetic controls. These data sets are the same as in [Fig 2A, 2D and 2G](#pbio.3000548.g002){ref-type="fig"}. One-way ANOVA with Tukey correction was used for comparing different conditions. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. FB, fat body; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Sleep fragmentation in flies with FB-specific *upd2* knockdown.

\(A\) Bout number plotted against average bout duration (minutes) showed that *upd2* knockdown flies had fragmented day sleep. (B) Nighttime pattern was similar to controls (black, *yolk-GAL4/+*; gray, *UAS-upd2RNAi/+*; orange, *yolk-GAL4\>upd2RNAi*). *n* = 24--28 per genotype; data set plotted here is the same as in [Fig 2B and 2C](#pbio.3000548.g002){ref-type="fig"}. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. FB, fat body; GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Sleep fragmentation in open-field arena for flies with FB-specific *upd2* knockdown.

\(A\) Bout number plotted against average bout duration (minutes) showed a fragmentation pattern for daytime in open-field arena for *upd2* knockdown flies. (B) Nighttime pattern was similar to controls. Sleep was tracked for 3 days (*n* = 15--17 per genotype) (black, *yolk-GAL4/+*; gray, *UAS-upd2RNAi/+*; orange, *yolk-GAL4\>upd2RNAi*). Data set plotted here is the same as in [Fig 4](#pbio.3000548.g004){ref-type="fig"}. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. FB, fat body; GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Feeding-related behavior in open-field arena under different conditions.

\(A\) Number of feeding events with access to the food cup (red) compared with "no food" condition (black), where the food cup was covered with parafilm to prevent access. Exemplary heatmaps for flies under different food conditions (right panel). (B) Flies starved for 48 hours (blue bar) displayed a significant increase in feeding counts compared with control flies that had been fed (black) (*n* = 13--15). (C) Representative images from the video recordings. The bottom image is showing a fly feeding. (D) Visual annotation of the number of food visits displayed a significant increase in starved flies. (E) The average duration of food visit per fly was not significantly different between control and starved flies. (F) Flies on diluted food (red) (20% of regular food calories) displayed no change in their feeding counts compared with flies on regular food (black) but (G) slept less during both day and night (*n* = 10--13, Student *t* test for normally distributed data or Mann-Whitney U rank-sum test for nonparametric data was used to compare data sets. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}.

(TIF)

###### 

Click here for additional data file.

###### R4 neuron activity does not correlate with sleep duration.

\(A\) Expression of *R38H02-GAL4* in the brain using *UAS-mCD8*::*GFP* (green). Neuropil is stained with bruchpilot (nc82, magenta). Scale bar, 100 μm. (B) CaLexA intensity of individual flies plotted against their total sleep duration (over 24 hours). Flies were housed in open-field arenas. Two-tailed *P* values for Pearson's correlation coefficient are shown. Analyses in this figure is from the same data set as in [Fig 5](#pbio.3000548.g005){ref-type="fig"}. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. CaLexA, calcium-dependent nuclear import of LexA; GAL4, galactose-responsive transcription factor; GFP, green fluorescent protein; UAS, upstream activation sequence

(TIF)

###### 

Click here for additional data file.

###### Knockdown of *upd2* in muscles or neurons has no effect on visual behaviors.

\(A\) We did not observe any differences in simple visual behaviors (fixation \[*n* = 15--20\], optomotor \[*n* = 6--8\] or in visual attention \[*n* = 13--16\] with muscle-specific *upd2* knockdown (*24B-GAL4\>upd2RNAi*, maroon) compared with controls (*24B-GAL4/+*, black and *UAS-upd2RNAi/+*, gray). (B) Pan-neuronal knockdown of *upd2* (*R57C10-GAL4\>upd2RNAi*, purple) also had no impact on visual behaviors (optomotor, *n* = 12--16), fixation, *n* = 8--16), and visual attention (*n* = 14--16) compared with genetic controls (*R57C10-GAL4/+*, black and *UAS-upd2RNAi*, gray). One-way ANOVA with Tukey correction was used for comparing different conditions. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. GAL4, galactose-responsive transcription factor; RNAi, RNA interference; UAS, upstream activation sequence; *upd2*, unpaired 2.

(TIF)

###### 

Click here for additional data file.

###### Sleep fragmentation in open-field arena for flies with *dome* knockdown in *Ilp2* expressing neurons.

\(A\) Bout number plotted against average bout duration (minutes) showed a fragmentation pattern for daytime. (B) There was no obvious fragmentation pattern for nighttime. Flies in this figure are from the same data set as in [Fig 8D and 8E](#pbio.3000548.g008){ref-type="fig"}. Sleep was tracked for 3 days (*n* = 15--17 per genotype). (*Ilp2-*GAL4*/+*, black; UAS-*dome*RNAi*/+*, light gray; blue *Ilp2*-GAL4\>*dome*RNAi. *n* = 15--17 per genotype. Sleep was recorded over 3 days. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. *dome*, *domeless*; GAL4, galactose-responsive transcription factor; Ilp2, insulin-like peptide 2; RNAi, RNA interference; UAS, upstream activation sequence.

(TIF)

###### 

Click here for additional data file.

###### *dome* knockdown shows a trend to increased feeding.

\(A\) Number of feeding events was not significantly different between control flies and *dome* knockdown flies. Flies in this figure are from the same data set as in [Fig 8D and 8E](#pbio.3000548.g008){ref-type="fig"}. (B) Total food intake over 24 hours in Café chamber of *dome* knockdown flies was significantly increased compared to one of the genetic controls (*n* = 25, with 5 flies per chamber). One-way ANOVA with Tukey correction was used for comparing different conditions. *\*P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. Café, capillary feeding; *dome*, *domeless*.

(TIF)

###### 

Click here for additional data file.

###### *dome* knockdown does not alter simple visual behaviors.

\(A\) Fixation and (B) optomotor behavior of *Ilp2*-GAL4\>*dome*RNAi (blue) were not significantly different from *Ilp2*-GAL4/+, black, and UAS-*dome*RNAi/+, gray. *n* = 6--8 per experiment; one-way ANOVA with Tukey correction was used for comparing different conditions. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001, \*\*\*\**P* \< 0.0001; error bars show SEM. The data underlying this figure can be found in [S1 Data](#pbio.3000548.s013){ref-type="supplementary-material"}. *dome*, *domeless*; GAL4, galactose-responsive transcription factor; Ilp2, insulin-like peptide 2; RNAi, RNA interference; UAS, upstream activation sequence.

(TIF)

###### 

Click here for additional data file.

###### Excel spreadsheet with data listed for all main and supplementary figures.

(XLSX)

###### 

Click here for additional data file.

We would like to thank the Goetz lab (Queensland Brain Institute) for antibodies. Imaging was performed at the Queensland Brain Institute\'s Advanced Microscopy Facility using Yokogawa spinning disk confocal. We thank Burczyk/Faville/Kottler (BFK) for adjustments made to the DART software for tracking feeding behavior.

ARC

:   Activity Recording Capillary

BDSC

:   Bloomington Drosophila Stock Center

Café

:   capillary feeding

CaLexA

:   calcium-dependent nuclear import of LexA

DART

:   *Drosophila* arousal tracking

dome

:   *domeless*

EB

:   ellipsoid body

FB

:   fat body

Ilp2

:   insulin-like peptide 2

IPC

:   insulin-producing cell

LED

:   light-emitting diode

LHLK

:   lateral horn leucokinin

Npf

:   neuropeptide F

NREM

:   non-rapid eye movement

PI

:   pars intercerebralis

RNAi

:   RNA interference

upd2

:   unpaired 2

10.1371/journal.pbio.3000548.r001

Decision Letter 0

Alvarez-Garcia

Ines

Senior Editor

© 2020 Ines Alvarez-Garcia

2020

Ines Alvarez-Garcia

This is an open access article distributed under the terms of the

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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

11 Oct 2019

Dear Dr van Swinderen,

Thank you for submitting your manuscript entitled \"­­Downregulation of a satiety signal from peripheral fat bodies improves visual attention while reducing sleep need in Drosophila.\" for consideration as a Research Article by PLOS Biology.

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10.1371/journal.pbio.3000548.r002

Decision Letter 1

Alvarez-Garcia

Ines

Senior Editor

© 2020 Ines Alvarez-Garcia

2020

Ines Alvarez-Garcia

This is an open access article distributed under the terms of the

Creative Commons Attribution License

, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

12 Nov 2019

Dear Dr van Swinderen,

Thank you very much for submitting your manuscript \"­­Downregulation of a satiety signal from peripheral fat bodies improves visual attention while reducing sleep need in Drosophila\" for consideration as a Research Article at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by four independent reviewers.

The reviews of your manuscript are appended below. As you will see, the reviewers find the work novel and potentially interesting, however they also raise several concerns that need to be addressed. They mention a few issues with the experimental design and interpretation, the fact that you are measuring food proximity, not feeding behaviour, and that more sophisticate analyses are needed to support the conclusions.

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Reviewers\' comments

Rev. 1

Ertekin et al report that fat body-specific knockdown of upd2, which was previously shown to be downregulated during starvation and increased with high-fat and high-sugar diets, increases feeding, decreases sleep, and enhances visual attention. Using a new tool for simultaneously assessing sleep and feeding-related behavior in individual flies in combination with a neuronal activity reporter, the authors show that activity of R4 neurons of the ellipsoid body are elevated in food-deprived and in upd2 mutants. Considering the prominent effect of sleep on cognitive function and the widespread practice of starving flies prior to behavioral tests, their finding that a manipulation that mimics starvation and suppresses sleep can improve performance on a cognitive task is novel, exciting, and may profoundly impact the way researchers design experiments and interpret their data. However, some of the conclusions in this manuscript are based on untested assumptions or only indirectly supported by the data. I believe that additional critical experiments and thoughtful rephrasing can greatly improve the cohesiveness and logical flow of this manuscript.

Major comments:

1\) Some of the text used throughout the manuscript should be revised to more accurately reflect the findings. a) "Sleep need": Data presented show that upd2 downregulation decreases sleep, but whether it also decreases sleep need per se has not been tested or shown. The authors should be conservative when referring to the reduced sleep phenotype of upd2 mutants and KDs. Alternatively, the authors could add additional experiments to support the idea, e.g., a thermogenetic approach could be used to show that flies do not sleep rebound after the downregulation has been lifted. b) Synonymously referring to upd2 as a "satiety signal" or downregulation of upd2 as "starved-like state": Most of the manuscript does not directly compare the effect of upd2 manipulation with that of food deprivation (except in one instance, for R4 neuronal activity). I suggest avoid generalizing upd2 downregulation as "a lack of satiety cue" or "starvation-like state," except in the Discussion.

2\) Since the fat body and upd2 are central to the main findings of the manuscript, the driver and the RNAi should be validated. Fat body drivers are notoriously non-specific, and many publications use two or more fat body drivers to confirm a fat body-specific effect. Alternatively, or in addition, the authors could cite or show expression data for the driver. Regarding the RNAi, it's common practice to use two independent RNAi constructs, and there are several other RNAi lines with no predicted off-target effects that are available for upd2, including another TRiP short hairpin. Finally, to more rigorously support the genetic interpretations, the authors could test whether fat body-specific expression of upd2 can rescue the feeding and sleep phenotypes in upd2 mutants.

3\) Regarding the feeding estimates from the open-field platform, I think the authors are actually measuring a food-dwelling or food-proximity behavior, and the authors should be careful about calling it "feeding" or "feeding activity." It certainly isn't "food intake." Other available methods of feeding estimation (e.g., FLIC, flyPAD) have undergone extensive efforts to validate that the estimates correlate with actual food consumption. Figure S3 is not evidence for the accuracy of feeding estimates, since it only shows that the measurements can distinguish behaviors in the presence and absence of food. Whether the assay can resolve different levels of food intake or feeding-related behaviors---and thus can be used for quantitative comparisons---needs to be tested. Some examples of additional experiments that may be useful include: a) Do the feeding estimates increase with prolonged starvation; b) Can the assay resolve a compensatory feeding response to food dilution; and c) Correlate their estimation with an actual feeding measurement (e.g., see the flyPAD paper, Figure 3 of Itskov et al. (2014) Nat. Comm.)

4\) Figures 6 and S6: THIP's sleep-inducing effect is validated in upd2 mutants but its effect on visual attention is tested in fat body upd2 knockdowns. The pharmacological validation and the phenotype measurements should be performed with the same genetic manipulation. Did upd2 KD flies also sleep more with THIP? Alternatively, did upd2 mutants also perform better than the controls in the visual attention task, with and without THIP? If the authors wish to support the idea that it is the starved state that enhances performance in the visual attention test, they need to show that food deprivation also improves visual attention in genetic controls. Without this validation, it is difficult to determine whether it is the "starvation-like state" or something entirely different that enhances visual attention of the upd2 KD flies.

5\) R4 neurons as a neural correlate of starvation: This experiment doesn't integrate well with the rest of the manuscript. The increased activity of R4 in the upd2 mutants is not connected to phenotypes other than starvation, including decreased sleep and enhanced visual attention. Also, because the relationship between starvation and increased R4 activity is not further studied, the experiment only raises more questions. I think excluding this section entirely would improve the cohesiveness of the manuscript. Alternatively, some suggestions for making this finding more substantiated and relevant include:

a\) Rule out a contribution from sleep suppression on R4 activity. Both starved flies and upd2 mutants sleep less than controls. Although the authors show that the R4 activity is not significantly correlated with the range of sleep durations in freely-sleeping, well-rested flies, an "abnormal" level of sleep may still affect R4 activity. The authors should measure R4 activity in sleep-deprived controls and compare it to fed or starved controls and the upd2 mutants. For a more thorough dissociation experiment, the authors could additionally compare sleep-deprived/starved controls with sleep-deprived/fed or rested/starved controls to test whether the effects of sleep- and food-deprivations on R4 activity are additive.

b\) To support the idea that R4 activity level correlates with nutrition state, the neuronal activity should be correlated with actual food intake instead of extended time spent on food, especially since the "feeding estimates" in the open-field arena has not been validated.

c\) It is unclear why the feeding parameter being correlated with R4 CaLexA intensity is % time spent feeding rather than the number of feeding events, when the latter is the metric the authors use everywhere else when analyzing feeding behavior in the open-field arena (Figures 4, 7, S3, S8). Does the number of feeding events also correlate with R4 activity?

d\) The relationship between feeding and R4 activity can be tested easily and more definitively using optogenetic stimulation of R4 neurons, similarly to Figure S5 in which the authors show that optogenetic activation of R4 decreases sleep. If R4 activity reflects starved state, does artificially activating R4 neurons increase food intake?

e\) If the R4 neurons are THE neural correlates of starvation, and starvation is the cause of enhanced visual attention, then does R4 activation enhance visual attention in a similar way?

Minor comments:

1\) Were virgin female flies used for all the experiments? It's specified in some sections of the Methods but not in all.

2\) Lines 132-133 (referring to Figure 1B), the authors write that upd2 mutants show "no significant change in total food consumption," but from the data points shown it looks like the experiment was underpowered. The authors should not make any inference about the lack of "significant change" in this case.

3\) Lines 138-139, the phrase "this suggests that upd2 mutants are feeding when they normally should be achieving most of their deeper sleep" implies that the flies are sleeping less during the period of hyperphagia because they are eating instead of sleeping. However, the sleep loss occurs mostly during the daytime instead of nighttime, and the latter is when the mutants overfeed. The statement should be rephrased or clarified.

4\) Figure S1 does not "exclude the possibility of a circadian influence (Line 137)." The fact that the feeding pattern (nighttime hyperphagia) persists in constant dark actually suggests that the phenotype has a circadian component, per convention, as I understand it, in the field of chronobiology.

5\) Does Figure 4D show a control fly, or a upd2 KD fly? Also, since heat plots are being used to help justify the feeding estimates, heat plots of both control and experimental flies should be shown for qualitative, visual comparison.

6\) It has not been shown that upd2 acts through domeless in the Ilp2 neurons to modulate sleep, feeding, and visual attention phenotypes, as genetic interaction studies are missing. I advise the authors to either tone down the language or perform additional experiments to validate the genetic interaction. An ideal experiment would be to show that expressing upd2 in fat body rescues the behavioral phenotypes in upd2 mutants but not when dome is downregulated in Ilp2 neurons. Alternatively, the authors could show that dome RNAi has no additional effect on the behavioral phenotypes in the upd2 mutant background, although this study would not be as definitive.

Rev. 2

In this manuscript, Ertekin et al. uncover and investigate the link between Upd2 and sleep. The work rotates around the hypothesis that upd2 delivers a systemic satiety signal that, when missing, changes the feeding and sleeping behaviour of the flies.

The manuscript has several novel findings, with the main one being the characterisation of an unusual sleep phenotype of the Upd2 mutant and the altered feeding pattern, which shows a preference for nighttime feeding rather than during the day. Some of the findings are particularly intriguing: namely, the demonstration of increased activity in the R4 neurons in the brain of fed Upd2 mutant flies, which resembles that of a starved control fly, rather than that of a fed fly and the finding that upon knockdown of domeless (the Upd2 receptor) in Insulin-like peptide-2 neurons in the brain, the sleep and feeding phenotypes also resemble those shown with the Upd2 mutant.

However, I am afraid the main message of the paper is unfocused and the data are not convincing enough to support the main claim - if anything, they clearly reject it. Overall, the sensation is that there is strong disconnect between the line they authors chose to push and the actual results.

I find there are three main issues:

1\) I am certainly not convinced that upd2 delivers a satiety signal. Simply put: a fly that is always hungry should eat more. upd2 mutant flies, instead, eat less and are smaller in size (however this latter is likely to be an not-discussed developmental effect).

Clearly there is a sleep phenotype during the day in udp2 mutants, and there is a different relationship with foraging but none of the results shown is actually compatible with lack of satiety signal - if anything, the opposite is true.

2\) most of the putative \"feeding analysis\" in the paper rely on positional tracking. This is clearly the non-optimal tool for this story. Yes, flies seem to spend more time by the food but that does not mean they eat more (in fact, CAFE assay clearly shows they don\'t). Taken together, the results make me think that the udp2 phenotype is actually \*not\* a starvation phenotype: perhaps it is linked to food foraging for other reasons? flies use food to regulate their social interaction or to lay eggs. If the authors are interested in finding a mechanistically link, I suggest they look in that direction. If they want to continue on the starvation hypothesis I am afraid they would have to provide more convincing evidence. Perhaps using the flypad from the Ribeiro laboratory in Lisbon may shed some light.

3\) the RNAi phenotype in the fat bodies does not recapitulate nor phenocopies the mutant phenotype, neither in terms of sleep nor in terms of foraging. This adds a level of confusion that leaves the reader disoriented, especially because the authors claim otherwise.

4\) I don\'t know what to make of the increase in attention phenotype shown in figure 6. Perhaps is interesting but, again, it is based on too many assumptions. One important assumptions the authors make is that because upd2 flies sleep less than control, they may be sleep deprived (line 421). I would argue the opposite is true: they sleep less because they need less sleep. When we are comparing two different genotypes, the fact that one sleep less than the other says nothing about sleep deprivation state. So in conclusion, ok that udp2 KD have a slightly better attentive performance but how does this relate to sleep at all?

Additional comments on figures are as follows.

Figure 1

• upd2 mutant flies are smaller (how smaller? Please do not make quantitative claims without quantifying). A comparison should be made between the Upd2 mutant line with control flies which have been matched for size to rule out any effect that may result from a smaller and slimmer fly just showing a different feeding pattern or sleep pattern.

• An additional control of wild-type flies which have been starved could serve as point of reference for the claims.

Figure 2

• The controls used, particularly those in Figure 2A, B and C seem to show significant differences between each other - please include full statistics.

Figure 3

• I am personally uneasy about dissecting sleep parameters such as sleep bout lengths because we have no idea whatsoever of what they mean - if anything. I understand the field for some reasons like those, so it\'s fine to have these measures. However, I do not think one can talk about \"sleep quality\" as a substitute for longer bouts. This is a tautology that we should not promote in the field. The authors do this multiple times in the manuscript. In fact, their argument is that udp2 flies have worst sleep quality but at the same time they show they perform better in cognitive tests.

• In figure 1K-L, the authors show that udp2 mutant flies have shorter bouts. If, like the mutant line, the knockdown line shows bout lengths similar to that of the mutant, measuring arousal past 31 minutes during the day would lead to measurements being taken from a non representative sample of bouts, as no individuals from the mutant line had bout lengths longer than 20 minutes (similarly with measurements in the night time). Incidentally, the sample size used for these experiments is also not stated in the manuscript.

This figure would benefit firstly from the same analysis done with the mutant line where measures of bout duration and number of sleep bouts are taken first before measuring arousal.

Figure 4

• The assay used here for feeding is not state-of-the-art and is confounded by too many issues. See major point \#2

Figure 5

• This is interesting. Figure 5D would benefit from adding a starved version of the Upd2 mutant in addition to the fed version. Also, quantitative palettes should be used so that the highest level is never reached and GFP intensity appears to be almost saturated in this image - it would be interesting to see if the starved Upd2 mutant could give an even greater intensity.

Supplementary Figure 6

• This figure would benefit from quantification of food intake with the drug. If the drug also changes feeding behaviour or increases how much food the flies eat, they may show greater sleep from a postprandial response.

• If using Gaboxadol changes the amount of sleep but does not change attention, this could be further support for the phenotype being one of increased foraging rather than a starvation phenotype.

Rev. 3: Alex Keene - please note this reviewer has waived anonymity

This is a very creative and innovative manuscript that describes a relationship between sleep and attention. A number of conceptual aspects are highly novel and will broadly influence the field including the identification of upd2 as a molecular link between peripheral energy stores and central brain regulation of sleep, and the finding that increased arousal in upd2 mutants may promote attention, despite a reduction in total sleep. The manuscript is well written and technically sound. I do believe there are a number of addition experiments that are needed to support or clarify the conclusions in particular see points 2,3, 4, 6, 7.

1\. While there is evidence that upd2 functions as a homolog of leptin (Rajan et al, 2012, Rajan et al, 2017), my understanding is this is still relatively controversial and it may be safer to describe upd2 as a secreted cytokine, then discuss its leptin-like role in the discussion section.

2\. The feeding data in 1B are non-significant, but the absolute values are different by nearly 50%, suggesting the need for increased statistical power. Also, the legend states N=40, but there are only 5 data points. Please clarify.

3\. Line 183. 'any effects on sleep were inconsistent compared to controls.' It is unclear what this means. The best control is the RNAi line alone, which provides a phenotype, raising the possibility that upd2 functions in the brain. It may be worth testing with additional GAL4 or fat body drivers to clarify these results.

4\. The authors bin arousal threshold in 1-30 or 30-60 minute bins, but I believe their earlier work has shown that the switch from light to dark sleep occurs early. The conclusion that sleep intensity is reduced in upd2 mutant flies would be improved by greater temporal resolution.

5\. Based on the text, I do not understand the contributions of Figure 4. First, other systems have been devised to simultaneously measure sleep and feeding e.g. ARC assay. Second, the system cannot distinguish whether the animal is eating, only whether it is near the food which can be detected using the DART. If the purpose is to confirm results in a different arena, then this is valuable and should be stated. Otherwise, please clarify how this improves previous figures.

6\. In figure 5 it would be useful to show CaLexA in the fed and starved state for both the mutant and wild-type. In addition, this would be useful for the R4 correlations in 5B.

7\. Why was the attention assay only performed in RNAi knockdown flies. It is incongruous to have the R4 experiment exclusively in mutants and the attention work in knockdown experiments. The easiest way to rectify would be to perform the attention experiments in mutants. I also do not understand S6. The sleep measurements with THIP are in mutants, but the attention assay is in the RNAi knockdown.

Minor comments

1\. Line 54: 'the phrase 'maintain well-tune cognitive processes' is vague.

2\. The first paragraph lacks citations. Even though the concepts are generally well-accepted it would be helpful to point readers to relevant literature.

3\. It's not clear why the day and night are labeled by yellow and blue. Especially since others have used this to denote fed-starved. May be better to use white and black boxes to maintain consistency with the circadian literature.

4\. Please clarify line 183. If they are awake/foraging, why not feed?

5\. In methods, please reference the w+ line used for outcrossing.

Rev. 4

In this manuscript, the authors present an interesting set of experiments that seek to understand the relationships among sleep, physiology, and cognitive function. While we find the questions highly relevant and the data intriguing, there are several conceptual concerns that limit our enthusiasm for the manuscript in its present form. Some involve what we believe to be over interpretation of the data, which, in our opinion, obfuscate the main thesis of the paper and logical thread of the work as well as confuses direct observation with interpretation, thereby potentially limiting its overall impact. Other concerns center on what might be considered insufficient depth of mechanistic understanding of key components of their model. Significant textual revisions are recommended, as are a small number of additional experiments.

The title of this manuscript implies discovery of a link between the expression of a satiety signal (i.e., upd2) and sleep need and visual attention. These reviewers agree that this is a well-supported and interesting finding. However, in our opinion, this message is diluted throughout the manuscript by the authors persistent presentation of conjecture as established fact (e.g., by equating upd loss with starvation), by conflating key ideas (e.g., sleep need is not the same as sleep loss), and by over-interpretation of behavioral data (e.g., positional preference does not imply feeding). These positions are unnecessary in the Introduction and Results, and they actively hinder the reader's ability to carefully interpret the experiments. Such conjecture of the data should be confined to the Discussion.

Assertion of a starvation-like state: The authors repeatedly state that loss of upd signaling emanating from the fat body is responsible for a "starved-like" state; a state the authors believe mimics the starvation-induced sleep deprivation phenotype widely reported by others. Maybe, maybe not. True, some phenotypes of upd loss are similar to those in starved animals (e.g., decreased sleep duration and quality). But others are not (e.g., upd LOF flies don't eat more, starved flies do). The timing of when upd mutants feed is altered, but this may indicate changes in sleep-wake behavior and/or in baseline metabolic state. We agree that it is intriguing to consider that these animals are "genetically starved," but we are not convinced this is the case. We propose two possible ways to address this. First, the authors could simply move the discussion of a possible starved-like state and its implications to the Discussion and maintain a focus on describing the results precisely (e.g., that upd loss is causing the phenotypes, not starvation). Whether the flies are starved does not impact the fact the sleep and attention are affected by upd. Alternatively, the authors might provide more convincing evidence of perceived starvation such as:

\(1\) Determining whether starving upd mutants or yolk-GAL4 \> upd-RNAi flies alters sleep, feeding, and attention phenotypes: if loss of upd achieves a starved-like state, then further starvation would not significantly alter these phenotypes

\(2\) Asking whether conditional loss of upd function using tub-GAL80ts or tub-GeneSwitch affects baseline sleep, feeding, and attention measurements relative to baseline prior to the manipulation. Furthermore, this strategy would also help clarify whether the phenotypes reported in the paper are caused by the current state upd signaling or instead cause by loss of upd signaling throughout development.

Position preference as an "accurate estimate" of feeding: Similar to the claim of genetic-starvation, the authors use positional preference to infer "feeding events." These two may indeed be correlated, although is many instances they are not. Either way, the authors should simply state these data as "positional preference" or "time spent near the food" in the results to avoid the indication that this assay explicitly measures feeding. The lack of flies coming to the food cup when it is covered with parafilm does not provide evidence for or against what they do when they are there, and the observation that they spend similar amounts of time there when it is filled with agar seems to us to argue against the notion that this is an accurate measure of feeding. Either way, its accuracy has not been established.

Lack of connection between DILP2 and R4 Neuron Activity: Given the observations presented in Figures 5, 7, and associated supplemental figures, DILP2 neurons must somehow communicate with R4 neurons to regulate their excitability and, in turn, form the basis of nutritional state control over sleep behavior. The manuscript would be substantially strengthened if the authors provided data illuminating the relationship between these neurons, which can be done either by (1) neuroanatomical characterization of possible synaptic connections made between DILP2 and R4 neurons or (2) determining whether R4 neurons respond to artificial activation/inhibition of DILP2 neuron activity using the CaLexA approach.

Phenotype in mated female and/or male flies: It is worth noting that only virgin female flies were tested throughout the manuscript. However, it is widely understood that sleep/feeding behavior is not only sexually dimorphic, but is also strongly modulated by the reproductive status of female flies. The authors should strongly consider investigating the upd mutant phenotypes in mated females and male flies to better establish the relevance and generality of the observations.

Requirement of amino acid for feeding/sleep phenotype: This reviewer noticed that most of the feeding measurement were performed using 5% sucrose food. However, sucrose-only food does not support normal lifespan or physiology and there is increasing evidence supporting the importance of specific amino acids influencing feeding-dependent effects on sleep-wake regulation and homeostasis. While it is, perhaps, extreme to ask that these experiments be repeated under standard nutritional conditions, this limitation should be clearly stated and discussed in the Discussion section.

Differences between mutant and tissue-specific RNAi knockdown observation: The authors generally conclude that upd signaling from the fat body is responsible for its effects on feeding and sleep (lines 200-201; Fig 2). However, liquid feeding amount (e.g., daytime feeding) and sleep measurements (e.g., modest effects during nighttime; not significant across all controls) appear to be different between the mutant and fat-body knockdown strategies (Fig 1C vs. Fig 2A). The authors should comment on this discrepancy. As a side note, data showing that the upd2-RNAi is effective should be presented, either by determining whether ubiquitous knockdown of upd2 phenocopies the mutant phenotypes and/or by measuring the extent of upd transcript reduction caused by fat-body specific RNAi knockdown.

Sleep fragmentation measurements using tissue-specific RNAi approach: The authors should quantify and present sleep fragmentation parameters, similar to what is presented for the upd mutant in Fig 1H-L, for the tissue-specific knockdown data collected in Fig 2, 4A-C, and 7C-D. The expectation is that night-time sleep is much more fragmented when upd is knocked-down in fat body cells (i.e., increased sleep bout number and decreased average bout duration).

Additional Comments:

The Fig 1 legend states that "(B) Food intake was measured with Café assay, with 5 flies/chamber over 20 hours (n=40 per genotype)". However, the number of data points do not match this description (only 5 data points appear to be plotted). The only possible explanation is if each data point represents a unique experiment. If that is indeed the case, that should be explicitly stated.

In the food intake figure presented in Figure S1, the term "ZT" along the x-axis should be changed to "CT" since the experiment is being performed under constant darkness conditions.

Both the interpretation and presentation of Figure 7 should appear before Figure 5 for the sake of continuity. According to the authors' proposed model, upd signaling from the fat body is received by DILP2 neurons, which in turn somehow sends direct/indirect nutrient status information to R4 ring neurons to regulate sleep. The way it is currently presented in the manuscript, it seems like the R4 ring neurons come before the DILP2 neurons.

Additional detail should be provided in the Methods section regarding measures of sleep intensity, specifically the calculation/algorithm that was used to determine whether a fly responded to a stimulus.

10.1371/journal.pbio.3000548.r003

Author response to Decision Letter 1

25 Feb 2020

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10.1371/journal.pbio.3000548.r004

Decision Letter 2

Alvarez-Garcia

Ines

Senior Editor

© 2020 Ines Alvarez-Garcia

2020

Ines Alvarez-Garcia

This is an open access article distributed under the terms of the

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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

11 May 2020

Dear Bruno,

Thank you very much for submitting a revised version of your manuscript \"Downregulation of a cytokine secreted from peripheral fat bodies improves visual attention while reducing sleep in Drosophila\" for consideration as a Research Article at PLOS Biology. Please accept my apologies again for the delay in sending you our decision. This revised version of your manuscript has been evaluated by the PLOS Biology editors, the Academic Editor and three of the original reviewers.

As you will see, two of the reviewers feel that the manuscript is greatly improved and only have a few minor suggestions to clarify some points. Reviewer 2, however remains unconvinced about the feeding/starvation aspect of the manuscript. After discussing these comments with the academic editor, we do feel that while it is fine to propose in the discussion that the upd2 mutants represent a starve state, you should refocus and temper your message, acknowledging all the caveats associated with this hypothesis and not taking it as a given making that the starting point. In addition, you need to address Reviewer 2's Point 3, which we consider important.

In light of the reviews (attached below), we are pleased to offer you the opportunity to address the remaining points from the reviewers in a revised version that we anticipate should not take you very long. We will then assess your revised manuscript and your response to the reviewers\' comments and we may consult the reviewers again.

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Reviewers' comments

Rev. 1:

The revised manuscript is greatly improved. Removing the opto experiments and the changes to the text make the paper much more readable. The authors have addressed the critical concerns adequately. I have only two minor suggestions:

1\) Page 17, line 459: I don\'t think this comparison should be made across experiments. If you really want to make this claim, I think the lines need to be tested at the same time, both with and without THIP.

2\) Page 18, line 484-5: The subheading overstates the findings. I think the subheading should be revised to be more conservative, similar to the figure legend title.

Rev. 2:

Main issues:

Point 1) I am certainly not convinced that upd2 delivers a satiety signal. Simply put: a fly that is always hungry should eat more. upd2 mutant flies, instead, eat less and are smaller in size (however this latter is likely to be an not-discussed developmental effect). Clearly there is a sleep phenotype during the day in udp2 mutants, and there is a different relationship with foraging but none of the results shown is actually compatible with lack of satiety signal - if anything, the opposite is true.

AU answer: Increased activity of the R4 neurons is compatible with a starvation signal. This aligns with previously published work (Park et al., 2016). However, we concede that before this result, the Reviewer is correct in questioning our terminology, so we have changed our language in the text accordingly and reserved the question of satiety signals for the discussion. The Reviewer suggests that upd2 mutants should be eating more. First, flies probably eat as much as they can, so there may be a ceiling effect. What is more interesting here is if they display more feeding-related behaviours. This is exactly what we find, when we move beyond the Café assay and look at feeding-related behaviour in knockdown animals in our open field arena. In upd2 and domeless knockdown animals, the number of feeding events 10 increases during the day and the night (see Fig. 4F and Fig. 7E). It is however only significant against both genetic controls for nighttime feeding. The data in Fig. 4F quite convincingly show a generalized increase in feeding-related behaviour, even if one daytime control is not significant. We do not know why the Café assay did not show a similar increase, but again this may be due to a ceiling effect, or may have to do with the food quality (sugar water versus standard fly food), or the finer-grained behavioural analysis available for individual flies in our open field feeding assay (see discussion below regarding this assay). The important conclusion is that the manipulated flies are displaying more feeding behaviour. There is only one experiment where this does not hold, and that is daytime Café for the upd2 mutants (Figure 1B), but in total darkness food consumption in the mutant trends to more (Fig. S1). Everywhere else, upd2 manipulations shows equivalent or more feeding behaviour

Reviewer's answer: True you show that one circuit namely R2 neurons which respond to starvation have increased activity. There are many many more circuits involved. I refer you to Krishna Melnattur, Paul Shaw 2019 review on the subject. What about LHLK neurons? PAMs? IPCs/DILPs, NPF? I would expect a much more comprehensive testing of your hypothesis. As it stands it is a big (yet in my opinion unnecessary) part of the paper which is not adequately addressed. (see general comments at the end for clarification)

Point 2) most of the putative \"feeding analysis\" in the paper rely on positional tracking. This is clearly the non-optimal tool for this story. Yes, flies seem to spend more time by the food but that does not mean they eat more (in fact, CAFE assay clearly shows they don\'t). Taken together, the results make me think that the udp2 phenotype is actually \*not\* a starvation phenotype: perhaps it is linked to food foraging for other reasons? flies use food to regulate their social interaction or to lay eggs. If the authors are interested in finding a mechanistically link, I suggest they look in that direction. If they want to continue on the starvation hypothesis I am afraid they would have to provide more convincing evidence. Perhaps using the flypad from the Ribeiro laboratory in Lisbon may shed some light.

AU answer: The Reviewer is correct in suspecting that feeding-related behaviours are affected. This is exactly what our new assay is meant to detect: feeding events are not only about dwelling near the food cup, there are a number of other movement criteria that have to be satisfied to count as a feeding event (detailed in the Methods). We have now provided starvation data to further validate our assay, showing a doubling of the number of feeding events in starved flies (Fig. S7B). While it is true that we do not know how much food is actually ingested, we would counter that does not matter because it is the feeding behaviour (visits to the food cup) that is of interest to this study and aligned with our altered attention phenotypes. Indeed, that is the direction our study takes (visual attention behaviour), rather than the metabolism angle. We nevertheless feel confident that our feeding-related metric correctly estimates food consumption. The Reviewer might want to compare the behavioural estimates in Figure 4F with food consumption in Figure 2A (for upd2 knockdowns), or Figure 7E with Figure S11 (for domeless knockdowns). The graphs seem convincingly similar to us.

Reviewer's answer: I have a problem with the feeding aspect of the paper in general. Please see other comments that address this concern

Point 3) the RNAi phenotype in the fat bodies does not recapitulate nor phenocopies the mutant phenotype, neither in terms of sleep nor in terms of foraging. This adds a level of confusion that leaves the reader disoriented, especially because the authors claim otherwise.

AU answer: We are puzzled where the Reviewer is confused. Figure 2C (knockdown sleep data) does phenocopy the mutant sleep data (Figure 1G): day-time sleep is severely reduced and night time sleep only lightly. That effect sizes are not as large in the knockdown is probably attributed to penetrance. But the effect is the same. By foraging, the Reviewer is probably referring to the Café results? Here it is true that there is some discrepancy, with less daytime feeding. But total food consumption is similar to controls, also in DD trials (Fig. S1). There is no discrepancy at all between the upd2 and domeless knockdown results, for any phenotype.

Reviewer's answer: There is a problem with replicating feeding results in this paper. In figure 1a Upd2 mutants have nighttime hyperphagia but no overall effect over 24hrs. In Figure 2a there is an overall difference between one control and KD but not the other in total intake. In figure 4E KD is different from one control group but not the other and in 4F KD are hyperphagic at both day and night. I am struggling to draw a conclusion here. Also from what I can tell/ it is not clear if you are using different diets in CAFÉ assay/open field arena and in DART assay. Are these comparable- please clarify?

Point 4) I don\'t know what to make of the increase in attention phenotype shown in figure 6. Perhaps is interesting but, again, it is based on too many assumptions. One important assumptions the authors make is that because upd2 flies sleep less than control, they may be sleep deprived (line 421). I would argue the opposite is true: they sleep less because they need less sleep. When we are comparing two different genotypes, the fact that one sleep less than the other says nothing about sleep deprivation state. So in conclusion, ok that udp2 KD have a slightly better attentive performance but how does this relate to sleep at all?

AU answer: The Reviewer is correct to doubt that the upd2 mutants are sleep deprived. This is also what we thought, which is exactly why we tested this assumption by providing them with more sleep (via the sleep drug THIP), to see if this 'corrected' their hyper focussed attention phenotype. It did not. This is an important result (now Fig. 7). If their attention phenotype was a maladjustment due to insufficient sleep, then providing mutants with more sleep using this method might have corrected it, as shown previously (Kirszenblat et al, 2018). Rather, upd2 mutants seem to need less sleep, and one consequence is to be hyper focussed. This is in marked contrast to sleep-deprived wild-type flies, which are less focussed (more distractible), which we reported in Kirszenblat et al, 2018. That upd2 mutants do the opposite while also sleeping less is precisely what makes our study interesting. We have tried to clarify this striking result a bit better in the discussion (lines 577-599).

Reviewer's answer: Ok- I understand that people like to use this drug but I doubt it recapitulates natural sleep. It may be more akin to anaesthesia. I don't think this experiment adequately addresses the issue of sleep deprivation but accept this is a tool used in the field.

Additional comments on figures:

Figure 1

• upd2 mutant flies are smaller (how smaller? Please do not make quantitative claims without quantifying). A comparison should be made between the Upd2 mutant line with control flies which have been matched for size to rule out any effect that may result from a smaller and slimmer fly just showing a different feeding pattern or sleep pattern.

• An additional control of wild-type flies which have been starved could serve as point of reference for the claims.

AU answer: Physical difference in the mutants have been described previously (Rajan & Perrimon, 2012) and we see the same. We did not feel the need to replicate these measurements, as we trust these are the same mutants.

Reviewer's answer: Ok.

Figure 2

• The controls used, particularly those in Figure 2A, B and C seem to show significant differences between each other - please include full statistics.

AU answer: We have indicated the significant effects, if there were any. The full statistics can be found in our deposited files. The specific statistics refered to are in the metadata provided for Figure 2.

Reviewer's answer: Ok

Figure 3:

• I am personally uneasy about dissecting sleep parameters such as sleep bout lengths because we have no idea whatsoever of what they mean - if anything. I understand the field for some reasons like those, so it\'s fine to have these measures. However, I do not think one can talk about \"sleep quality\" as a substitute for longer bouts. This is a tautology that we should not promote in the field. The authors do this multiple times in the manuscript. In fact, their argument is that udp2 flies have worst sleep quality but at the same time they show they perform better in cognitive tests

AU response: The Reviewer may not have understood our sleep intensity measures in Figure 3, which follow from multiple publications (that we reference) where these are more thoroughly explained (e.g., Faville et al, 2015). Briefly, these are not simply sleep duration bouts. We measure sleep intensity by probing for behavioural responsiveness to mechanical stimuli, and this can happen any time between 0 and 60min. How responsive they are (how deeply asleep they are) depends to some extent on how long they have been asleep, especially at night (see Fig. 3E). The binned data in Fig. 3B is just to show that flies were probed for all of the different sleep durations. There are comparatively fewer probing events for 30-60min during the day because fewer flies ever sleep that long during the day, on average. Our measures stop at 60min because our probing events are hourly; how long flies have been asleep before the hourly probe is stochastic. We now provide some additional Methods (lines 667-672). We hope we have clarified better our sleep intensity assay, so that the reader can appreciate why allude to sleep quality. There is no question that upd2 mutants and knockdown animals have more fragmented sleep (Fig. 1J-L) and are easier to wake up (Fig. 3D,E). Following Reviewer \#4's questions (below), we now provide the sleep consolidation / fragmentation data for the upd2 and domeless knockdown strains as well (Figures S5 and S10). It all quite consistently shows a difference in sleep quality in these animals. That this should be matched by 'improved' attention is again precisely what makes this study interesting. We are however cautious in not interpreting this as 'better' performance, and provide quite a bit of discussion on what this 'improved' attention phenotype might mean ethologically (lines 577-599).

Reviewer's answer: Line 243: Authors say that flies can compensate for sleep loss by exhibiting deeper sleep during shorter sleep bouts yet use a vibration stimulus to probe "sleep depth". Sleep depth does not necessarily equate to increased quality. For example in mammals sleep is characterised by different stages (REM, non REM 1,2,3,4) which are necessary for different aspects of cognitive performance. REM sleep is though to be important for dissociation between memory and emotional salience for instance, but it is much easier to wake someone in REM sleep compared to non rem stages. Please provide some discussion/caveats to your conclusions here.

• In figure 1K-L, the authors show that udp2 mutant flies have shorter bouts. If, like the mutant line, the knockdown line shows bout lengths similar to that of the mutant, measuring arousal past 31 minutes during the day would lead to measurements being taken from a non representative sample of bouts, as no individuals from the mutant line had bout lengths longer than 20 minutes (similarly with measurements in the night time). Incidentally, the sample size used for these experiments is also not stated in the manuscript. This figure would benefit firstly from the same analysis done with the mutant line where measures of bout duration and number of sleep bouts are taken first before measuring arousal.

AU answer: Sleep in upd2 mutants was more severely affected compared to the knockdown strain. This is not surprising as the knockdown experiments were performed to circumvent developmental effects (Yolk-Gal4 expression is only in adults). As can be seen in Fig. 3B, knockdown flies are sleeping past 31 minutes, even during the day. There are fewer instances though, as expected, but enough to say something about sleep intensity differences for most time bins. The dataset in Fig. 3 are from the same flies as in Fig. 2, which we have noted in figure legend. We have now added the sample sizes under this figure as well.

Reviewer's answer: ok

General comments from the reviewer:

In general there are some interesting aspects to this paper but the feeding/starvation aspect of it is confusing and weak. For me a simpler and stronger story could be compiled by looking at the effect of upd2 and domeless mutants/KD on sleep, depth and fragmentation. The apparent lack of compensation for sleep loss yet maintenance/improvement in attention in domeless and upd2 mutants/KD animals is the most interesting part. You can discuss feeding dysregulation/starvation in discussion but I don't think you have adequately demonstrated that you are mimicking a starved state or feeding dysregulation in these animals. As I said previously you are not using state of the art means to measure intake (such as ARC or flypad). In general the feeding phenotypes are weak, upd2 mutants are different sizes and as far as I can tell you do not normalise food intake by body size.

I would encourage a rework of manuscript composed of figure 1F-L, Figure 2 C,D,G,H,K,L. Figure 3, Figure 5 (including comparison between control and Upd2 fed conditions but not starved.), Figure 6 and Figure 8. It still works well without feeding side of it.

Other comments

Images in Figure 5D are oversaturated. While the effect is clear from the representative images it makes me nervous about the validity of the imaging technique as a whole.

Where are stats/Ns in 3B? Are all bouts observed in longer bins contributed by one fly? Or many? Ns/bin not reported on graph/ legend, only the number of flies in the experiment in figure legend. "(B) The number of animals in each sleep duration bin for both day and night were similar in both genetic controls (black and grey) and in knock-down flies (orange)." How can this be if in figure 1 you show strong differences in bout number and length? Please clarify. Incidentally if the genotypes are not receiving the same number of stimuli (stimulus/fly/genotype), it is unlikely that they are receiving the "same" stimulus as you may have a problem with habituation. You will need to acknowledge this if this is the case.

Line 325: why was 30s chosen as criteria for feeding? What is average feeding bout length? Are you not missing shorter feeding bouts? This is why a method like flypad would be far superior in this study.

Rev. 3: Alex Keene

The revised version of this manuscript has addressed all my initial concerns through the inclusion of additional data and changes within text. I have two minor suggestions below that could be addressed at the authors discretion.

1\. I prefer the sentence on line 83 to be deleted or modified. Behavioral performance is used in a non-specific way, and one could envision many behaviors (such as foraging) that are better when the animal is not well-fed.

2\. Clarity could be added to paragraph on insulin signaling (line 616). Which neurons were found to be Ilp2 targets in larvae? Does the suggestion they communicate \'indirectly\' mean non-synaptically or through intermediary neurons? It is also worth noting that there are many other sources of insulin, other than Ilp2 neurons.

10.1371/journal.pbio.3000548.r005

Author response to Decision Letter 2

20 May 2020

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10.1371/journal.pbio.3000548.r006

Decision Letter 3

Alvarez-Garcia

Ines

Senior Editor

© 2020 Ines Alvarez-Garcia

2020

Ines Alvarez-Garcia

This is an open access article distributed under the terms of the

Creative Commons Attribution License

, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

9 Jun 2020

Dear Dr van Swinderen,

Thank you for submitting your revised Research Article entitled \"Downregulation of a cytokine secreted from peripheral fat bodies improves visual attention while reducing sleep in Drosophila\" for publication in PLOS Biology. I have now checked the revision and obtained advice from the Academic Editor.

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To submit your revision, please go to <https://www.editorialmanager.com/pbiology/> and log in as an Author. Click the link labelled \'Submissions Needing Revision\' to find your submission record. Your revised submission must include a cover letter, a Response to Reviewers file that provides a detailed response to the reviewers\' comments (if applicable), and a track-changes file indicating any changes that you have made to the manuscript.

Please do not hesitate to contact me should you have any questions.

Sincerely,

Ines

\--

Ines Alvarez-Garcia, PhD

Senior Editor

PLOS Biology

Carlyle House, Carlyle Road

Cambridge, CB4 3DN

+44 1223--442810

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

DATA POLICY:

Many thanks for providing a data file containing all the data underlying the graphs displayed in the figures. I have checked the file and I would like you to please fulfil the following requests regarding the data you provided:

\- Label clearly the data in Fig. 2 that belongs to each panel and add the data for Fig. 2J, K, L

\- Add missing data from Fig. 4B

\- Relabel the data in Fig. 8 (currently labelled as Fig. 7) and add missing data from Fig. 8C

\- Add all the values of the graphs in Fig. S2A, B -- currently only the mean is shown

\- Add missing data for Fig. S7D, E, F, G -- there is one panel wrongly labelled as S7C, so please add the right label and the data for the missing sections.

In addition, please also ensure that the figure legends in your manuscript include information on WHERE THE UNDERLYING DATA CAN BE FOUND, and ensure your supplemental data file/s has a legend.

Please ensure that your Data Statement in the submission system accurately describes where your data can be found.

10.1371/journal.pbio.3000548.r007

Author response to Decision Letter 3

30 Jun 2020

###### 

Submitted filename: Response.docx

###### 

Click here for additional data file.

10.1371/journal.pbio.3000548.r008

Decision Letter 4

Alvarez-Garcia

Ines

Senior Editor

© 2020 Ines Alvarez-Garcia

2020

Ines Alvarez-Garcia

This is an open access article distributed under the terms of the

Creative Commons Attribution License

, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

13 Jul 2020

Dear Dr van Swinderen,

On behalf of my colleagues and the Academic Editor, Amita Sehgal, I am pleased to inform you that we will be delighted to publish your Research Article in PLOS Biology.

The files will now enter our production system. You will receive a copyedited version of the manuscript, along with your figures for a final review. You will be given two business days to review and approve the copyedit. Then, within a week, you will receive a PDF proof of your typeset article. You will have two days to review the PDF and make any final corrections. If there is a chance that you\'ll be unavailable during the copy editing/proof review period, please provide us with contact details of one of the other authors whom you nominate to handle these stages on your behalf. This will ensure that any requested corrections reach the production department in time for publication.

Early Version

The version of your manuscript submitted at the copyedit stage will be posted online ahead of the final proof version, unless you have already opted out of the process. The date of the early version will be your article\'s publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with <biologypress@plos.org>. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit <http://www.plos.org/about/media-inquiries/embargo-policy/>.

Thank you again for submitting your manuscript to PLOS Biology and for your support of Open Access publishing. Please do not hesitate to contact me if I can provide any assistance during the production process.

Kind regards,

Pamela Berkman

Publishing Editor

PLOS Biology

on behalf of

Ines Alvarez-Garcia,

Senior Editor

PLOS Biology

[^1]: The authors have declared that no competing interests exist.
