Drosophila glia increases sleep and enhances sleep need, manifest as resistance to sleep deprivation. Surface glia comprising the fly equivalent of the blood-brain barrier (BBB) mediate the effect of dynamin on sleep. Blocking dynamin in the surface glia causes ultrastructural changes, albeit without compromising the integrity of the barrier. Supporting a role for endocytic trafficking in sleep, a screen of Rab GTPases identifies sleep-modulating effects of the recycling endosome Rab11 in surface glia. We also find that endocytosis is increased in BBB glia during sleep and reflects sleep need. We propose that endocytic trafficking through the BBB represents a function of sleep.Glia are important modulators of neural activity, yet few studies link glia to sleep regulation. We find that blocking activity of the endocytosis protein, dynamin, in adult  Sleep is a conserved behavioral state of fundamental significance. Nevertheless, the nature of its relevance to brain function is still a matter of active research and debate . Most stGlial function has been linked to sleep in some contexts . Sleep iDrosophila , a temperature-sensitive dominant negative allele of dynamin, a GTPase involved in membrane scission (ts1 (UAS-1Shi) by a pan-glial driver (Repo-GAL4) were raised at permissive temperature (18°C) until adulthood and subjected to mechanical sleep deprivation while being monitored for sleep/activity levels. Concurrently with the start of mechanical deprivation at lights off (Zeitgeber Time (ZT) 12), 1Shi was induced by raising the temperature to a restrictive 30°C. Mechanical deprivation was applied for 12 hr, concluding at the beginning of the following day (ZT 0), when the temperature was returned to permissive 18°C.To address the role of glia in homeostatic sleep in scission . Flies e1 flies exhibited a surprisingly large amount of sleep above controls throughout the deprivation period, precluding any analysis of subsequent rebound. Experimental flies slept over 300 min in the course of 12 hr of mechanical deprivation, significantly greater than both parental controls (1Shi insertion (UAS-120xShi)  .1Shi is an allele for a temperature-sensitive dynamin whose vesicle-forming membrane scission function declines with heightened temperature to act as a dominant negative. However, quite surprisingly, we found that increased sleep evident during mechanical deprivation was not dependent on a shift from permissive to restrictive temperature, as 1Repo > Shi flies deprived at 18°C exhibited greater sleep as compared to controls, commensurate to experimental flies at 30°C – with greater sleep occurring during the daytime at the expense of nighttime sleep  with K44AShi, a constitutive dominant negative allele of dynamin. While K44AShi is also deficient for GTP hydrolysis, it is a distinct mutation from 1Shi(G273D)  . ExpressDrosophila glia include the ensheathing/wrapping glia  glia of the blood brain barrier, by moody-GAL4, produced a significant increase in total .As an additional glial sub-type GAL4 driver, we observed that bundance . The nucial glia , marking1Shi, even at permissive temperature, with Rab9-G4 was lethal, we observed prominently enhanced sleep when 1Shi was conditionally expressed in adulthood using the TARGET system  on Repo > 1Shi flies raised at permissive temperature. When viewed as a horizontal section through the fly brain, the BBB glia form the two most superficial cell layers which continuously envelope the entire circumference of the brain. The thicker and seemingly less electron-dense PG layer faces the luminal side, and the underlying SPG, considered to be the tight barrier by virtue of septate junctions, appear as a predominantly thinner layer with periodic expanded involutions and protrusions. Examination of the superficial layers reveals a dense and distinct cytoplasmic make-up in Repo > 1Shi brains. Most prominently, amassed and repeated ring-like structures, resembling the microtubule bundles previously reported , and in the initial screen all available lines were crossed to a pan-glial driver, with hits defined as Rabs that showed opposing effects on sleep of all available CA as compared to all DN lines. In order to avoid potential lethality from overexpression of dominant negative constructs, we used a newly created hormone-inducible pan-glial driver, Repo-GeneSwitch (RepoGS). While lethality was not seen for any Rab using this driver, with or without RU486 feeding, we observed that expression was leaky  and are known to regulate diverse vesicle movement within the cell, including exo- and endocytotic pathways. We asked which trafficking pathways are important in glia for sleep regulation by screening an existing collection of UAS-Rab lines , which ias leaky , that isas leaky . Unfortuas leaky . In the as leaky , albeit Repo-GAL4, including the full complement of UAS and GAL4 controls. For Rab3, Rab5 and Rab9, we did not observe sleep increases by this driver  while expression of Rab11 CA in either surface glial population was sufficient to produce increases in total sleep  with or without RU, the Rabs of interest were confirmed with s driver , and whis driver , we consall glia  and was ethality , with noal sleep . Unlike  in glia , which ced cells . TogetheConsidering that inhibition of endocytic trafficking in the fly hemolymph-brain barrier increases sleep, could rates of endocytosis at the barrier depend on sleep-wake state? Recent work in mice suggests that sleep serves to promote clearance of interstitial fluid, and consequently harmful metabolites which may have accumulated as a result of sustained neuronal activity during wake, through the glymphatic system . WhetherIn order to evaluate effects of sleep-wake state on endocytosis, we collected flies from an early night time point (ZT14 - 2 hr after lights off) and an early day time point (ZT2 - 2 hr after lights on) . At ZT14Given that a difference between ZT14 and ZT2 may be indicative of a circadian effect, independent of sleep-wake state, we evaluated flies at an identical time-point (ZT2), but following sleep deprivation (SD). Flies in the SD condition were mechanically sleep deprived for 12 hr beginning at ZT12 and ending at ZT0, and allowed 2 hr to recover, before also being dissected at ZT2. Hence, circadian time is equivalent between SD and non-SD animals, but flies recovering from SD should have greater sleep pressure. We find that flies taken from a state of recovery sleep following deprivation show substantially higher rates of endocytosed AF-dextran than their un-deprived controls at ZT2  – a leveBrains collected at 6 hr intervals over the course of the 24 hr day showed a rhythmic pattern, which was not significant as a circadian cycle by JTK, but indicated lowest and highest levels of endocytosis at ZT2 and ZT14, respectively . To deteTogether these findings demonstrate that endocytosis at the hemolymph-brain barrier occurs preferentially during sleep and reflects sleep need. It is normally high during the early night when sleep predominates but can be increased in the early day if flies undergo recovery sleep following deprivation the prior night or if they are induced to sleep by drug treatment.Glial cells are an important constituent of blood or hemolymph-brain barriers, a conserved feature of complex organisms that protects the central nervous system from unregulated exchange with humoral fluids. In this study, we show that manipulations of endocytosis and vesicular trafficking in the surface glia, particularly the subperineurial glia of the fly BBB, are sufficient to elevate baseline sleep amounts, and that at the BBB, endocytosis itself is influenced by sleep-wake state. Our findings expand the scope of glial involvement in sleep and suggest that trafficking through hemolymph- or blood-brain barriers is an underexplored determinant of sleep-wake behavior.1Shibire has been a popular method to probe circuit-level influence on various fly behavior, with the prevailing interpretation being that exposure to non-permissive temperature abrogates synaptic transmission via an eventual depletion of the synaptic vesicle pool -- a consequence of inhibited endocytosis. Ultrastructural work in neurons has revealed, however, that expression of 1Shibire at the lower limit of permissive temperature markedly alters electrophysiological properties and intracellular structures  or neurons  is known to be fundamental to endocytosis and vesicle formation. Nevertheless, the stages of vesicular trafficking are intimately linked, making it difficult to exclusively manipulate, even at the level of molecular machinery, the processes of endocytosis and exocytosis    and Rab1er (GAT) . Consideer (GAT) , it is pmponents , Rab11 CMdr65, show greater total sleep, but effects have not been directly mapped to the barrier  and UAS-CD8::GFP and UAS-nGFP. For control genotypes, GAL4 and UAS lines were crossed to iso31.; ;UAS-20xShi.ts1 (referred to as UAS1-20xShi) was shared by Gerald Rubin. UAS-Shi.WT and UAS-Shi.K44A were shared by Konrad Zinsmaier. NP2222-GAL4, NP6293-GAL4, Alrm-GAL4, MZ0709-GAL4, MZ0097-GAL4 were shared by Marc Freeman. Moody-GAL4 and 9–137 GAL4 was shared by Roland Bainton. UAS-Rab CA and DN lines were acquired from the Bloomington Drosophila Stock Center. Rab9-GAL4 (#51587) was also acquired from Bloomington.Stocks present in the lab collection include—; ;Repo-GeneSwitch 2301 line a 4.2 kb genomic fragment  was amplified by PCR and cloned into a PUAST-AttB-Sfi-GeneSwitch vector. Transgenesis for this construct was performed by BestGene at landing site AttP154 (97D2).To generate the; ;Flies were raised in bottles on standard food at either room, or non-permissive temperature (18–19°C) for Shibire and TARGET system  experimeDrosophila Activity Monitoring (DAM) System  and processed using PySolo in the case of GeneSwitch experiments. Locomotor data was collected using the ng PySolo. Data ar°C). In the case of the initial temperature shift experiments with 1Shibire, flies were kept at 18°C, and shifted to 30°C for the duration of the night, before being returned to 18°C at lights-ON, ZT0. For behavioral experiments, adult flies of at least 6-days post-eclosion were used, except in the case of tubGal80.ts experiments, where younger flies were loaded to grant additional time to compensate for the loss of strength due to incomplete de-repression of tubGal80.ts.Sleep deprivation was performed by mechanical disruption using a vortexer triggered to shake randomly for 2 s out of every 20, for the span of 12 hr beginning at lights-OFF, ZT12. Flies were kept in incubators under 12 hr light: 12 hr dark (LD12:12) cycles and constant temperature . Confocal imaging was performed on a Leica TCS SP5.Repo > UAS-120xShi flies approximately two weeks post-eclosion were dissected in a cold room in PBS and fixed with 2.5% glutaraldehyde, 2.0% paraformaldehyde in 0.1M sodium cacodylate buffer, pH7.4, overnight at 4°C. Samples were post-fixed in 2.0% osmium tetroxide for 1 hour at room temperature, and rinsed in DH2O prior to en bloc staining with 2% uranyl acetate. After dehydration through a graded ethanol series, the tissue was infiltrated and embedded in EMbed-812 . Flies were not strictly circadian entrained or taken at an exact time of day, but dissections were performed together in the afternoon. Reported images represent sections from single flies from each genotype, although the experimental was compared to both parental controls. The described microtubule structures were not seen in the controls. Embedding, staining and sectioning was performed by the Electron Microscopy Resource Laboratory at Penn. Thin sections were stained with uranyl acetate and lead citrate and examined with a JEOL 1010 electron microscope fitted with a Hamamatsu digital camera and AMT Advantage image capture software.Heads from female Integrity of the barrier was assessed as previously described . Female The brains of female flies ranging from 10 to 20 days post-eclosion were dissected in cold PBS, and collected to 20 brains per vial. Excess PBS was pipetted off after spinning the samples down by micro-centrifuge. The samples were frozen on dry ice and submitted to the Children’s Hospital of Pennsylvania Metabolomic Core for HPLC analysis.2, 8.2 mM MgCl2, 4 mM NaHCO3, 1 mM NaH2PO4-H2O, 5 mM trehalose, 10 mM sucrose, 5 mM HEPES; pH 7.5), and 20 brains were collected per condition. To each sample, 60 μL of Collagenase IV (25 mg/mL) and 10 μL of DNase I (1 mg/mL) were added. Following shaking at 37°C for 15 min, brains were broken up by 3 rounds of gently pipetting. Using a 70-μm cell strainer, the brain samples were then transferred to FACS tubes, to which 3 mL of AHL was added and spun down for 5 min (2500 RPM). After removing the supernatant, the pellet was resuspended in AHL and brought to a volume of 200 μL, at which point the sample was split into two new FACS tubes of 100 μL each. One tube received an additional 50 μL of vehicle solution while 50 μL of dynasore hydrate solution  was added to the other. The samples were mixed well and incubated at room temperature for 10 min. Following incubation, 50 μL of Alexa fluor 647-10kd dextran  was added to each tube, mixed, and incubated at room temperature for 30 min. 4 mL of FACS buffer (0.5% BSA w/v + 0.1% w/v sodium azide in PBS) were added to each tube, spun down and the supernatant removed. Samples were immediately analyzed using a FACS Canto II (BD Biosciences).Flies expressing membrane bound GFP in the surface glia  were dissected in ice-cold AHL  was added to standard 5% sucrose in 2% agar food in locomotor tubes at 0.1 mg/mL. For the ZT14 timepoint, flies were flipped to Gaboxadol food at lights ON (ZT0) while for the ZT2 timepoint they were flipped onto drug just before lights OFF (ZT12). In both cases, they remained on this food for ~14 hr until dissection.6-cm plates were coated with poly-L-lysine (0.01%) for greater than 10 min, removed, and let dry. Brains were dissected in AHL, placed on the coated plates, and incubated with 50 μL droplet of 500 μL/mL 3kD FITC-conjugated dextran and either imaged continuously for 10 mins or dye was washed off and brains were imaged in AHL for 5 min. Confocol imaging was performed with 20x submergible water objective (with the lens touching the dye droplet) at excitation/emission wavelengths of 488/520. For a permeable dye, brains were dissected in AHL and incubated with 50 μL droplet of 125 μL/mL Rhodamine B and imaged continuously for 10 min. Imaging was performed with 20x submergible water objective at excitation/emission wavelengths of 543/555.Graphs and statistical tests were completed using Excel and GraphPad Prism. FACS analysis was performed in FlowJo. For measures of sleep, Controls  were compared to Experimental (GAL4 > UAS) animals by one-way ANOVA with Holm-Sidak post-hoc correction. For comparison of endocytosis between ZT2 and ZT14 or ZT2 and ZT2 SD or between each time point control and inhibitor, Students’ T test was performed. Additional details regarding tests and significance values are provided in the figure legends. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by a Senior Editor, a Reviewing Editor, and three reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: Paul Taghert (Reviewer #3).Thank you for submitting your work entitled "Endocytosis at the blood-brain barrier as a function for sleep" for consideration by eLife. However, we do feel the work could be made acceptable but only after the essential changes noted below are handled experimentally. Should you choose to submit a new version of this work, we will endeavor to have it reviewed by the same Board member and referees.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Summary:Drosophila. It uses a full range of genetic cellular and imaging methods to consider the hypothesis that endocytosis/exocytosis in BBB glia changes in ways that supports the role of these cells in helping resolve levels of metabolites that increase in relation to sleep-need. Further it provides evidence for the involvement of glial Rab11 in the cellular mechanism. Experimentally analyzing glial function in sleep, the work touches an aspect of brain physiology that has just begun to be explored but that has been proposed as one of the crucial functions of sleep. Thus, this work is a potentially significant contribution of interest to a general audience.This paper explores the functional contributions of the blood brain barrier (BBB) to sleep regulation in Though work is clearly described, thorough in most aspects and also several caveats are clearly acknowledged, there are issues that need to be clarified and conclusions that need to be strengthened in order to better establish the key conclusion of the paper that inhibition of endocytosis in glia increases sleep.Essential revisions:1) The authors nicely describe various difficulties experienced with genetic tools to perturb endocytosis in targeted glia . In addition, they report similar effects on sleep following expression of constitutively active and dominant negative forms of Rab11 as well as perturbation of Rabs that may not participate in endocytosis . Thus, it is important to better establish sleep phenotypes arise from changes in endocytosis rather than non-specific glial malfunction. Some suggestions of additional experiments to support the hypothesised role of endocytosis are proposed below.1a) To better characterise sleep-associated endocytosis, it would be useful to measure rates of endocytosis at 4-6 time points to define a daily cycle, not just two time points.1b) Given the possibility that even adult-specific expression of mutant dynamin or Rab RNAi can have long-lasting changes in glial physiology or alterations in BBB permeability, it would be useful to test if these flies revert to normal sleep behaviour if allowed to recover for an appropriate period of time.1c) To show that a different amount of sleep deprivation (more or less) produces a proportionate change in endocytosis .1d) To show bidirectionality of regulation and to control for potential contribution of stress rather than sleep drive, the authors should test whether endocytosis following more sleep is reduced .2) Different Gal4 lines are used to target subsets of glia. The specificity of these lines needs to be clearly documented. The logic for using one or other for different experiments needs to be clarified. And at least one case, an additional Gal4 may need to be used.2a) The description of Gal4 expression lines  should be expanded Critically, to demonstrate that each one the Gal4s used is indeed expressed preferentially in glia, the figure should include counter stains with glia or neuron-specific antibodies . Glial-specific expression would be also more convincing if the authors could test how well the expression pattern of these Gal4s  is blocked by Repo>Gal80. .2b) The tubGal80.ts experiments utilize Rab9-Gal4. To more convincingly implicate the glial cells presumed to be targeted, these experiments should also be repeated with Moody-Gal4 .3) Figure 1 shows that of Shi-ts expression in glia induces resistance to mechanical stimulation during sleep. Can a control experiment be done to rule out the possibility of a sensory defect that makes these flies unresponsive to mechanical stimulation? E.g. To test the responsiveness of awake Repo>Shi flies to mechanical stimulation? On this note, are the PG and SPG drivers expressed outside the brain in sensory structures?Other concerns and suggestions:4) Is it necessary to dissociate cells before adding Alexa647-dextran? Why not measure before dissociation as most cellular processes including endocytosis could be more robust in an intact brain. If it is simply to allow access to Alexa647-dextran, then could an alternative smaller endocytic tracer be tried?5) Is resistance to mechanical stimulation following SD specific only to Shi, or also seen following endocytic Rab manipulation?6) The shi-ts expression results suggest that sleep during SD happens at both temperatures, but nighttime sleep seems to elevated sooner in both males and females at 18 degrees, than in controls. Is this impression correct? and if so, is there an explanation? This difference (if reproducible) involves latencies and time of day differences. The fly sleep literature appears to point to an increasing fragmentation of cellular mechanisms between those that affect daytime versus nighttime sleep. Is there any basis to think or expect that the cell biological mechanisms (exo- and endocytosis) should be geared to daytime versus nighttime?7) Figure 2C suggests that the effects of Shi-ts on sleep are not due to merely overexpression of Dynamin since the expression of Shi-wt does not have a significant effect. However, for Shi-wt, only total sleep is shown, whereas the main effect for Shi-ts at 18C is on day sleep. To exclude the possibility that a Shi-wt effect on sleep is masked when only total sleep is shown the figure should be revised to include data showing the effect of Shi-wt on daytime sleep.8) Indeed, it would useful to consistently show data for total sleep and daytime sleep for all genotypes in the paper (these data must be available).9) In Figure 3A, to exclude possible effects due to expression of Shi in neurons (in addition to expression in the BBB glia), the authors could show that these phenotypes remain when the expression in all glia is blocked using Repo>Gal80.10) It would be valuable to test, with existing tools, to what extent surface glia are responsible for the phenotypes observed with the pan-glial driver, Repo. Does Shi expression in surface glia also cause resistance to mechanical sleep deprivation? Is the phenotype of either NP6293 or moody >Shi weaker than that of Repo>Shi and does combining NP6293 and moody Gal4s generate a phenotype comparable to that of Repo-Gal4? Why is the 9-137-Gal4 driver (SPG & PG) that is later used in Figure 5 (subsection “Endocytosis occurs during sleep and is influenced by prior wakefulness”) not used here?11) Figure 3 localizing effect to surface glia: In Figure 3A – which UAS-shi was used? The effect does not appear to match that of repo-Gal4. The difference is no more than 100 min; whereas. in Figure 2A, the difference is >200 min. Is this lack of quantitative matching significant? Maybe it reflects the strength of the Gal4s but alternatively it could mean that other cells contribute. This should be discussed.12) In the same figure – the lack of effect of astrocyte expression seems to be correlated with an unusually high value for the UAS control. Is there a reason to be concerned here?13) Regarding EM images in Figure 4A – how abundant and frequent are these ultra-structural changes and how do they correlate with sleep phenotypes?The observations themselves  are only described to the level of positioning of two arrows in one of the micrographs. There should be some definition of the phenotypes and indication of prevalence, severity or quantification . Were the suspected MTs coated or bridged as originally described in the paper cited? There no details provided in the methods regarding gender, time of day, age, or "n" value.In addition, it would be useful to know if these are also seen following induction of wild-type dynamin, because WT-dynamin expression does not cause sleep phenotypes and there is the potential to examine how relevant these ultrastructural defects might be to the sleep effects observed. (Perhaps the cited EM paper has addressed this issue in neurons?) Are there any changes in membranes or vesicular structures that could potentially be connected to a block in endocytosis? What is the predicted EM phenotype associated with Rab11 CA expression?14) Is the overall structural integrity of BBB, as shown in Figure 4B, also preserved when Rab11CA is expressed in glia?15) In Figure 4C, the significance is determined based on a difference between DN and CA allele of each Rab protein. Is this the best criterion? Wouldn't it be better to compare each manipulation to the parental controls? In any event, it should be made clear why only Rab11 and not others is deemed to have crossed a significance threshold.16) The overall logic that explains the link between the manipulations, endocytosis and sleep is confusing. Glial expression of Rab11CA increases sleep, similarly to Shi-ts. Do the authors propose that Rab11CA inhibits endocytosis, similarly to Shi-ts?17) On similar lines, the data shown in Figure 4C seem to contradict the data in Figure 4D. In Figure 4D, Repo>Rab11CA causes increased sleep, compared to parental controls. Figure 4C, shows that using the RepoGS driver, Rab11 DN induces even more sleep than Rab11CA. Thus, both of these opposing manipulations cause increased sleep. Could it be that both manipulations simply make glial cells sick, and therefore, in both cases, sleep is increased due to malfunction of glia, and not due to specific changes in endocytosis? The possibility for a non-specific effect is supported by similar data in Figure 4—figure supplement 1C, where expression of either DN or CA versions of Rab27 and Rab30 produces exactly the same phenotype of increased sleep, unless the authors believe that these also work by affecting endocytosis. These issues should be explained and addressed in a revised manuscript.18) In Figure 4—figure supplement 1B – are the effects of Rab3, Rab9 and Rab11 that were significant in Figure 4A (without RU486) still significant in the Figure 4—figure supplement 1B (with RU486)? The significance stars in Figure 4—figure supplement 1B are shown only for Rab1, Rab5, Rab27 and Rab30.19) The apparent contradiction between Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5 should be more clearly explained. Figure 5 shows that SD (which increases sleep pressure) causes increased endocytosis, but, Figure 1, Figure 2, Figure 3 and Figure 4, show that inhibiting endocytosis actually increases sleep, suggesting that endocytosis in BBB promotes wakefulness. An explanation that connects the two observations should be included.20) Are the changes in endocytosis that correlate with sleep state specific only to surface glia? Are there changes in endocytosis in other populations of glial cells, as function of sleep?Reviewer #1:Drosophila model of the blood-brain barrier. The work is very interesting in principle as it touches an aspect of brain physiology that has been hardly explored so far but that has been proposed as one of the crucial functions of sleep.In this manuscript, Artiushin et al., aim to explore the role of glia endocytosis in sleep regulation focussing on a The manuscript is well written in general and the experiments are well reported. The main weakness of the work is that in almost all experiments the genetic tools adopted did not behave as expected. The temperature sensitive form of shibire,  does not appear to provide any specific insight, possibly due to a temperature insensitive activation. The repoGENESwitch tool  does not seem to be properly responsive when it comes to controlling activation.All in all, the fact that the genetic tools employs are not behaving appropriately is a major weakness for the study as it is basically impossible to understand the exact specificity of the effect in terms of timing. For instance, it is impossible to exclude that this a developmental effect.One possibility to reinforce the findings could be to adopt different genetic tools with greater reported specificity, such as the ones described in https://pubs.acs.org/doi/10.1021/acssynbio.7b00302Presentation of data could be improved.For instance:# There is no reason to limit labelling of panels within figures. Please use more letters, ideally one per panel. It facilitates discussion.# Avoid using barplots. Barplots are uninformative and should almost always be discouraged. Use plots that are more informative, giving more insights on the distribution (such as boxplot or violin plots)# The experiment in Figure 3—figure supplement 2 needs some kind of co-staining confirmation# Please quantify the observations collected using electron microscopy?Reviewer #2:Most of the manuscript  describes how manipulating glia affects sleep. Overall, the behavioral phenotypes are strong. However, there are multiple issues with specificity of the methods, with data representation and with the interpretation of some of the results. Another question is – is it really so surprising and interesting that manipulating large numbers of glial cells has an effect on brain function, and sleep in particular?The second part  is more interesting and promising, as it provides evidence for correlation between sleep/wake states and the rate of endocytosis in BBB glia. However, this part needs further development. Also, the link between this part and the rest of the manuscript seems a bit artificial, as they do not really support or complement each other well.Specific comments:1) The authors show in Figure 1 that expression of the temperature sensitive allele of dynamin, Shi-ts in glia induces resistance to mechanical sleep deprivation. The authors show that the basal wake activity of these flies is not affected. However, another interpretation of the data should be considered. These flies may be unresponsive to mechanical stimulation. The authors should test the responsiveness of awake Repo>Shi flies to mechanical stimulation. The authors should also check if Repo>Shi provides resistance to sleep deprivation induced by alternative methods (thermo-genetic). Finally, the authors test the effects on SD only with Shi, but do not do it with the rest of manipulations, presented later in the paper, such as expressing mutant Rab proteins. Are the effects on resistance to SD specific only to Shi, but not to other manipulations of endocytosis?2) The authors observe strong effects for the temperature sensitive Shi allele even at the permissive temperature of 18C. This raises the question whether Shi is a good tool in this specific system. The main concern is that the observed phenotypes are mostly due to non-specific effects that make the glial cells sick. The authors should, at least, test if knocking down the endogenous Shi using RNAi produces a phenotype similar to expressing dominant negative Shi.3) The authors claim in Figure 2C, that the effects of Shi-ts on sleep are not due to merely overexpression of Dynamin since the expression of Shi-wt does not have a significant effect. However, for Shi-wt, only total sleep is shown, whereas the main effect for Shi-ts at 18C is on day sleep. Could it be that the effect of Shi-wt on sleep is masked when only total sleep is shown? The authors should demonstrate the effect of Shi-wt on day sleep.4) In Figure 3A, to exclude possible effects due to expression of Shi in neurons (in addition to expression in the BBB glia), the authors should show that these phenotypes remain when the expression in all glia is blocked, using Repo>Gal80.5) To what extent are surface glia responsible for the phenotypes observed with the pan-glial driver, Repo? For instance, does Shi expression in surface glia also cause resistance to mechanical sleep deprivation? Is the phenotype of either NP6293 or moody >Shi weaker than that of Repo>Shi? Will combining both NP6293 and moody Gal4s (SPG & PG) generate a phenotype comparable to that of Repo-Gal4? Why don't the authors use here the 9-137-Gal4 driver (SPG & PG) that is later used in Figure 5 (subsection “Endocytosis occurs during sleep and is influenced by prior wakefulness”)?6) The images in Figure 3—figure supplement 2 should be of better quality. The outline of the brain should be drawn. The authors also should demonstrate that each one the Gal4s used in this Figure is indeed expressed preferentially in glia by co-staining with glia or neuron-specific antibodies . The glia-specific expression would be also more convincing if the authors could show the expression pattern of these Gal4s  when the expression of the UAS-nGFP reporter is blocked in glia, using Repo>Gal80.7) The EM images in Figure 4A – how abundant and frequent are these ultra-structural changes? Are these changes also observed upon the expression of Rab11CA? Also, could the authors detect any accumulation of vesicles that cannot be secreted due to expression of mutant Shi?8) Is the overall structural integrity of BBB, as shown in Figure 4B, also preserved when Rab11CA is expressed in glia?9) In Figure 4C, the significance is determined based on a difference between DN and CA allele of each Rab protein. Is this the best criterion? Wouldn't it be better to compare each manipulation to the parental controls? Also, what would be the effect of overexpressing WT versions of these Rab proteins?10) The overall logic that explains the link between the manipulations, endocytosis and sleep is confusing. Glial expression of Rab11CA increases sleep, similarly to Shi-ts. Do the authors propose that Rab11CA inhibits endocytosis, similarly to Shi-ts?Do they suggest that expression of constitutively active Rab11 is equivalent to the inhibition of vesicular trafficking? If so, what would be the effect of Rab11 RNAi? Would it produce an opposite phenotype (less sleep)? Repo>Rab11 DN could be useful, but it was lethal. The authors should try using tub-gal80ts, to achieve inducible expression of Rab11 DN. Also, does Repo>Rab11CA cause resistance to mechanical sleep deprivation, just like Shi-ts?11) One of the major claims of this paper is that inhibition of endocytosis in glia  increases sleep. It is important to show that an opposite manipulation of increasing endocytosis actually reduces sleep. The data shown in Figure 4C is confusing and seems to contradict the data in Figure 4D. In Figure 4D, Repo>Rab11CA causes increased sleep, compared to parental controls. The interpretation is that this happens due to inhibition of endocytosis. However, in Figure 4C, using RepoGS driver, Rab11 DN induces even more sleep than Rab11CA. It seems that both of these opposing manipulations cause increased sleep, which is counter-intuitive. Could it be that both manipulations simply make glial cells sick, and therefore, in both cases, sleep is increased due to malfunction of glia, and not due to specific changes in endocytosis? The possibility for a non-specific effect is supported by similar data in Figure 4—figure supplement 1C, where expression of either DN or CA versions of Rab27 and Rab30 produces exactly the same phenotype of increased sleep.12) In Figure 4—figure supplement 1B – are the effects of Rab3, Rab9 and Rab11 that were significant in Figure 4A (without RU486) still significant in the Figure 4—figure supplement 1B (with RU486)? The significance stars in Figure 4—figure supplement 1B are shown only for Rab1, Rab5, Rab27 and Rab30.13) How do the authors explain the phenotypes caused by the expression of other Rabs, besides Rab11? Do they think that they all work by affecting endocytosis?14) In Figure 5, the authors should demonstrate that their genetic manipulations (Shi-ts and Rab11CA) actually can inhibit endocytosis in the surface glia using the 10kd dextran absorption assay. Also, could it be that the changes they see after SD are in fact caused by physical damage to the BBB? They may test if the effects of SD on endocytosis are resolved once the flies are allowed few hours of recovery after SD. They can also test how thermo-genetic SD affects their assay. They should also do EM on BBB after SD.15) There is an apparent contradiction between Figure 1, Figure 2, Figure 3Figure 4 and Figure 5. According to Figure 5, SD causes increased endocytosis. SD of course increases sleep pressure. But, in the Figure 1, Figure 2, Figure 3 and Figure 4Figures, the authors claim that inhibiting endocytosis actually increases sleep, suggesting that endocytosis in BBB promotes wakefulness. A possible solution to this paradox is that during normal sleep, there is an increase in endocytosis. This is important to support the function of sleep because increased endocytosis eventually helps to reduce sleep pressure. This is why inhibition of endocytosis promotes sleep – because in the absence of proper endocytosis, sleep quality is compromised, and sleep is then less effective in reducing the sleep pressure.16) Are the changes in endocytosis that correlate with sleep state specific only to surface glia? The authors should have additional control in Figure 5 – test if there are any changes in endocytosis in other populations of glial cells, as function of sleep.Reviewer #3:Drosophila. It is a careful study that uses a full range of genetic cellular and imaging methods to consider the hypothesis that endocytosis/exocytosis in BBB glia changes in ways that supports the role of these cells in helping resolve levels of metabolites that increase in relation to sleep-need. Further it makes a strong case for the involvement of Rab11 in the cellular mechanism. The emerging involvement of glial compartments in the fundamentals of brain physiology (here sleep regulation) makes this work a significant contribution of interest to a general audience. The work is clearly described, thorough in most aspects of the analysis and discussed in a scholarly fashion. I have a few questions and concerns that may help improve the paper.This paper explores the functional contributions of the blood brain barrier (BBB) to sleep regulation in the model system Shibere mis-expression and effects on sleep.The paper wisely pays careful attention to the effects (and lack of effects) of temperature with a classic ts allele of dynamin. In addition, I also commend the use of different shi isoforms to help focus on the biologically  important results. I had one question about this dataset that involved, not amount of sleep, but its temporal patterning.The results suggest that sleep during SD happens at both temperatures but I notice that nighttime sleep is elevated sooner in both males and females at 18 degrees, than in controls. Is this impression correct? and if so, is there an explanation? This difference (if reproducible) involves latencies and time of day differences. In my reading of the fly sleep literature, there is an increasing fragmentation of cellular mechanisms between those that affect daytime versus nighttime sleep. Is there any basis to think or expect that the cell biological mechanisms (exo- and endocytosis) should be geared to daytime versus nighttime?Dynamin and MTsThe ultrastructural work is an effective supporting body of evidence to evaluate the effects of UAS-shi at permissive temperatures. However, the results – amassed ring-like structures resembling microtubules, and a thinner PG layer – were not well-described, beyond the positioning of two arrows in one of the micrographs. There was no clear definition of the phenotypes nor any indication of prevalence, severity or quantification . Were the suspected MTs coated or bridged as originally described in the paper cited? There no details provided in the methods regarding gender, time of day, age, or "n" value. On a tangential (but relevant) note, was ultrastructure observed at only a single phase point? This issue is clearly beyond the scope of the current study.Cell SpecificityFigure 3 localizing effect to surface glia;3A – which UAS-shi was used?- Male or female flies?- The effect does not appear to match that of repo-Gal4. Difference is no more than 100 m; whereas. In Figure 2A, the difference is >200 min. Is this lack of quantitative matching significant? Maybe it reflects the strength of the Gal4s but alternatively it could mean that other cells contribute. I did not find any discussion of this point.Rab involvementThe screens were careful, unbiased, quantitative and very large in scope. I commend the authors for determining that the Gs-Gal4 line was leaky but was somewhat confused by the overall definitions of hits. There was a set of hits with no RU486  and a different one with RU486 . Yet at the end, only Rab11 is judged to have experimental support – I was not clear why (1) only Rab11 and not others crossed threshold. Also (2) whether Rab11 is a positive or negative regulator of sleep.“As preliminary screening compared only experimental flies (RepoGS>UAS-Rab) with or without RU, the Rabs of interest were confirmed with Repo-GAL4, including the full complement of UAS and GAL4 controls.”1) But only Rab 11 is shown, not Rab 3 or Rab9 – follow-up on Rabs 3 and 9 were neither shown nor mentioned,“Expression of recycling-endosome associated Rab11 CA resulted in a robust sleep increase when driven in all glia  and was significant for both CA lines.”2) Paradoxically, Rab11 activity appears to increase sleep in the follow-up but in the preliminary large scale screen the DN isoforms of Rab11 produced significantly more sleep than did the CA isoforms – by my reading that means Rab11 in glia is a negative regulator. So, I'm not sure how to interpret the combined dataset.Endocytosis1) Why dissociate cells before adding Alexa647-dextran? Why not measure before dissociation? The results are fortunately very clear, but I would think cellular processes like endocytosis would be more robust in an intact brain.2) Also it would be a stronger case if a different amount of SD produces a proportionate change in endocytosis .Also, would it would strengthen the case to measure endocytosis following more sleep ; This would require orthogonal genetics to drive sleep-inducing FB neurons, so I suspect the genetics would be too complicated for a two-month period of additional study. [Editors’ note: the author responses to the first round of peer review follow.]This paper explores the functional contributions of the blood brain barrier (BBB) to sleep regulation in Drosophila. It uses a full range of genetic cellular and imaging methods to consider the hypothesis that endocytosis/exocytosis in BBB glia changes in ways that supports the role of these cells in helping resolve levels of metabolites that increase in relation to sleep-need. Further it provides evidence for the involvement of glial Rab11 in the cellular mechanism. Experimentally analyzing glial function in sleep, the work touches an aspect of brain physiology that has just begun to be explored but that has been proposed as one of the crucial functions of sleep. Thus, this work is a potentially significant contribution of interest to a general audience.Though work is clearly described, thorough in most aspects and also several caveats are clearly acknowledged, there are issues that need to be clarified and conclusions that need to be strengthened in order to better establish the key conclusion of the paper that inhibition of endocytosis in glia increases sleep.Essential revisions:1) The authors nicely describe various difficulties experienced with genetic tools to perturb endocytosis in targeted glia . In addition, they report similar effects on sleep following expression of constitutively active and dominant negative forms of Rab11 as well as perturbation of Rabs that may not participate in endocytosis . Thus, it is important to better establish sleep phenotypes arise from changes in endocytosis rather than non-specific glial malfunction. Some suggestions of additional experiments to support the hypothesised role of endocytosis are proposed below.1a) To better characterise sleep-associated endocytosis, it would be useful to measure rates of endocytosis at 4-6 time points to define a daily cycle, not just two time points.We have expanded the nighttime time points and included these findings in . We also show time points around the clock .1b) Given the possibility that even adult-specific expression of mutant dynamin or Rab RNAi can have long-lasting changes in glial physiology or alterations in BBB permeability, it would be useful to test if these flies revert to normal sleep behaviour if allowed to recover for an appropriate period of time.To examine whether adult-induced expression of Shi produces irreversible alterations in sleep, which could signify permanent changes in function of the barrier, we expressed Shi in glia under the control of tubGal80.ts. In these experiments, temperature-dependent expression of tubGal80.ts suppresses Shi expression during development but allows expression in adults. We find that sleep is increased upon adult expression of Shibire and returns to levels which are not significantly different from controls when expression is blocked (in each case with a temperature shift) .1c) To show that a different amount of sleep deprivation (more or less) produces a proportionate change in endocytosis .Although this is a good point and we would certainly like to resolve this question, there are a number of scientific and technical difficulties that prevent us from doing so. First, more or less deprivation does not necessarily correlate with a linearly proportionate response, whether in behavioral rebound or cellular measures . Second, due to the degree of variability in the endocytosis assay, we do not have the resolution to detect such a difference. If we expand our time points after a period of deprivation, we still may not be positioned to detect a difference. We have examined later night time points  and found that while endocytosis is high during early night sleep and low by the end of the night/early day, it is difficult to determine a difference in the time points in between, meaning that endocytosis may be largely complete within the early hours of sleep or we simply do not have the resolution with this technique.1d) To show bidirectionality of regulation and to control for potential contribution of stress rather than sleep drive, the authors should test whether endocytosis following more sleep is reduced .To examine potential bidirectionality and assess the contribution of stress, we have elected to use Gaboxadol to induce sleep rather than introducing the added complications of orthogonal genetics and the requisite temperature-induction. We found that enhancing sleep throughout the day did not decrease endocytosis at ZT14, but endocytosis was increased at ZT2 if flies were treated with Gaboxadol prior to this point . We suggest that Gaboxadol increases sleep pressure and so promotes endocytosis at all times, thereby accounting for lack of a decrease at ZT14. Importantly, these data indicate that it is not the stress of sleep deprivation which is responsible for elevating endocytosis, as two methods of inducing sleep at a time when wake predominates both increase endocytosis.2) Different Gal4 lines are used to target subsets of glia. The specificity of these lines needs to be clearly documented. The logic for using one or other for different experiments needs to be clarified. And at least one case, an additional Gal4 may need to be used.In addition to the discussion of sub-points below, we have added a few remarks throughout the text clarifying why the given Gal4 lines were used for particular experiments.2a) The description of Gal4 expression lines  should be expanded Critically, to demonstrate that each one the Gal4s used is indeed expressed preferentially in glia, the figure should include counter stains with glia or neuron-specific antibodies . Glial-specific expression would be also more convincing if the authors could test how well the expression pattern of these Gal4s  is blocked by Repo>Gal80. .The Gal4 lines used in this study, other than Rab9-G4, have all been previously described and used for glial expression. We have emphasized this point and ensured that appropriate references for all drivers are included in the text.repo-Gal80, which by comparison makes clear what expression is neuronal and what glial . This is particularly evident for the drivers relevant for behavioral differences, such as Rab9-Gal4, as the surface glia are easily distinguishable from all other cell types. As requested by the reviewer, we provide an outline of the brain in Figure 3—figure supplement 2A.The Gal4 lines we used  have limited expression, if any, outside the BBB; given that, it would be difficult to co-localize expression with a pan-neuronal or pan-glial stain. In lieu of a counter-stain, we have provided Gal4>GFP expression in the presence and absence of 2b) The tubGal80.ts experiments utilize Rab9-Gal4. To more convincingly implicate the glial cells presumed to be targeted, these experiments should also be repeated with Moody-Gal4 .The SPG glial cells are implicated by two drivers, Rab9-Gal4 and Moody-Gal4. To confirm the adult-specific effect of Shi, we now provide the tubGal80.ts experiment with a second driver, Repo-Gal4 . The data confirm that conditional adult expression is sufficient for the glial sleep phenotype.3) Figure 1 shows that of Shi-ts expression in glia induces resistance to mechanical stimulation during sleep. Can a control experiment be done to rule out the possibility of a sensory defect that makes these flies unresponsive to mechanical stimulation? E.g. To test the responsiveness of awake Repo>Shi flies to mechanical stimulation? On this note, are the PG and SPG drivers expressed outside the brain in sensory structures?To rule out the possibility that a sensory defect in Repo>Shi flies makes them unresponsive to the mechanical stimulation of sleep deprivation, we stimulated the experimental and control flies at ZT11 – 12, a time when flies are predominantly awake, as suggested. We find no difference in the resultant beam crossings induced by stimulation between Repo>Shi flies and controls , suggesting that resistance to sleep deprivation cannot be accounted for by impaired sensitivity to the stimulus. Also, we are not aware of sensory structure expression of PG and SPG.Other concerns and suggestions:4) Is it necessary to dissociate cells before adding Alexa647-dextran? Why not measure before dissociation as most cellular processes including endocytosis could be more robust in an intact brain. If it is simply to allow access to Alexa647-dextran, then could an alternative smaller endocytic tracer be tried?While we agree that a minimally perturbed sample might better reflect cellular processes occurring in vivo, in our hands we have found that dissociation is necessary to allow access of AF647-Dextran to both sides of the barrier. Even with a smaller (3KD) dextran we did not observe any appreciable penetration into an intact brain .5) Is resistance to mechanical stimulation following SD specific only to Shi, or also seen following endocytic Rab manipulation?We have added this experiment . Resistance to mechanical stimulation appears to be specific to Shi, with surface glial being sufficient. It is plausible that Shi provides a more general perturbation of trafficking than Rab11, hence capturing both baseline and resistance phenotypes. These data also demonstrate that these effects can be separated as Rab11 only affects baseline sleep.6) The shi-ts expression results suggest that sleep during SD happens at both temperatures, but nighttime sleep seems to elevated sooner in both males and females at 18 degrees, than in controls. Is this impression correct? and if so, is there an explanation? This difference (if reproducible) involves latencies and time of day differences. The fly sleep literature appears to point to an increasing fragmentation of cellular mechanisms between those that affect daytime versus nighttime sleep. Is there any basis to think or expect that the cell biological mechanisms (exo- and endocytosis) should be geared to daytime versus nighttime?We are not sure that this difference in latencies that the reviewer mentions is a consistent finding in our data. The difference in latency between 18 and 30 degrees could also be a consequence of the shift in sleep patterns related to temperature, as touched on elsewhere in the text.With respect to time-of-day differences, it is true that some publications demonstrate effects predominantly on daytime or nighttime sleep when given neuronal populations are manipulated, but in our view, there is not yet a clear delineation between circuits and mechanisms which govern daytime vs. nighttime sleep. Likewise, there is little evidence to suggest that daytime sleep and nighttime sleep serve different functions in the fly.7) Figure 2C suggests that the effects of Shi-ts on sleep are not due to merely overexpression of Dynamin since the expression of Shi-wt does not have a significant effect. However, for Shi-wt, only total sleep is shown, whereas the main effect for Shi-ts at 18C is on day sleep. To exclude the possibility that a Shi-wt effect on sleep is masked when only total sleep is shown the figure should be revised to include data showing the effect of Shi-wt on daytime sleep.The figure has been updated to include daytime sleep. The result is still the same – Shi-wt is not significantly different from both controls, in terms of total or day sleep.8) Indeed, it would useful to consistently show data for total sleep and daytime sleep for all genotypes in the paper (these data must be available).Daytime and total sleep figures have now been included, either in the main text or supplements in order to not crowd the figures.9) In Figure 3A, to exclude possible effects due to expression of Shi in neurons (in addition to expression in the BBB glia), the authors could show that these phenotypes remain when the expression in all glia is blocked using Repo>Gal80.repo-Gal80  shows little or, in the case of moody-G4, no expression in neurons, therefore making it quite unlikely that the phenotype is due to non-glial expression.We showed that the increased sleep phenotype is present with Repo-G4  as well as Rab9-G4 (BBB expression), but not when glial expression of Rab9-Gal4 is blocked. Comparing expression of surface glial drivers  in the presence and absence of 10) It would be valuable to test, with existing tools, to what extent surface glia are responsible for the phenotypes observed with the pan-glial driver, Repo. Does Shi expression in surface glia also cause resistance to mechanical sleep deprivation? Is the phenotype of either NP6293 or moody >Shi weaker than that of Repo>Shi and does combining NP6293 and moody Gal4s generate a phenotype comparable to that of Repo-Gal4? Why is the 9-137-Gal4 driver (SPG & PG) that is later used in Figure 5 (subsection “Endocytosis occurs during sleep and is influenced by prior wakefulness”) not used here?We conducted additional experiments to address this question and find that the resistance to sleep deprivation phenotype indeed tracks to surface glia, with NP6293 G4 being sufficient .In the screen of glial sub-type drivers, we chose to use the individual surface glial drivers instead of 9-137 G4 in order to establish the effects of each population in isolation, just as the other glial drivers were chosen for specificity to individual glial sub-types. Nevertheless, Shi expression with 9-137 G4 also produces an increase in sleep, albeit smaller than would be expected if combining the effects of the surface glial lines individually . This may be a consequence of differences in driver strength, which are not readily assessable or comparable. Given that NP6293 G4 and moody G4 are on the same chromosome, the effort required to perform these experiments may not be worth the potential result because there still may be a discrepancy in driver strength between the surface glial drivers and repo.11) Figure 3 localizing effect to surface glia: In Figure 3A – which UAS-shi was used? The effect does not appear to match that of repo-Gal4. The difference is no more than 100 min; whereas. in Figure 2A, the difference is >200 min. Is this lack of quantitative matching significant? Maybe it reflects the strength of the Gal4s but alternatively it could mean that other cells contribute. This should be discussed.A line has been added regarding this point. As the reviewers point out, it may very well be a difference in driver strength, and/or that repo-G4 encompasses both surface glial populations while the lines in Figure 3A do not.12) In the same figure – the lack of effect of astrocyte expression seems to be correlated with an unusually high value for the UAS control. Is there a reason to be concerned here?We do not believe so as the UAS control does not achieve ceiling levels of sleep, so there is still room for an increase. Also, as sleep varies some from experiment to experiment, we are careful to always compare to controls within the same experiment. Importantly, a different astrocyte driver also does not increase sleep significantly above the controls. As these data used the other Shibire line, rather than the 20xShibire used in much of the manuscript and in the rest of Figure 3A, we did not include them in the manuscript, but provide them for the reviewer .13) Regarding EM images in Figure 4A – how abundant and frequent are these ultra-structural changes and how do they correlate with sleep phenotypes?The observations themselves  are only described to the level of positioning of two arrows in one of the micrographs. There should be some definition of the phenotypes and indication of prevalence, severity or quantification . Were the suspected MTs coated or bridged as originally described in the paper cited? There no details provided in the methods regarding gender, time of day, age, or "n" value.The details requested regarding the animals used have now been provided in the methods and/or figure caption. Briefly, our intention is to highlight the presence of unusual MT and intracellular structure in glial cells expressing Shi at 18 degrees. This simply demonstrates that by EM, expression of Shi at permissive temperature is not without consequence in glia, as has also been shown for neurons in the paper cited. While perhaps we could quantify something like the number of MTs over a length of surface glial area, it is not clear that this or any other quantification will be a useful measurement, because the phenotype is binary – the structures are present in Repo>Shi tissue, and not found in controls.In addition, it would be useful to know if these are also seen following induction of wild-type dynamin, because WT-dynamin expression does not cause sleep phenotypes and there is the potential to examine how relevant these ultrastructural defects might be to the sleep effects observed. (Perhaps the cited EM paper has addressed this issue in neurons?) Are there any changes in membranes or vesicular structures that could potentially be connected to a block in endocytosis? What is the predicted EM phenotype associated with Rab11CA expression?Our hope was to identify membrane or vesicular structures in the control surface glial images to have a target for comparison with the experimental images. Unfortunately, perhaps due to resolution or preparation, we were unable to pinpoint obvious endocytic or vesicular trafficking events in the control images, thereby making it hard to compare to Shi flies and to say, beyond the gross changes described, where the alteration connected to blocking endocytosis is expressed.Determining whether WT-dynamin causes ultrastructural defects could be interesting, but even if so, we could not exclude a contribution of such defects to the sleep phenotype produced by Shibire as these defects may be necessary but not sufficient.14) Is the overall structural integrity of BBB, as shown in Figure 4B, also preserved when Rab11CA is expressed in glia?Yes, the barrier does not appear permissive to dextran when Rab11CA is expressed .15) In Figure 4C, the significance is determined based on a difference between DN and CA allele of each Rab protein. Is this the best criterion? Wouldn't it be better to compare each manipulation to the parental controls? In any event, it should be made clear why only Rab11 and not others is deemed to have crossed a significance threshold.We agree that the ideal comparison would be between each manipulation and its respective parental controls. But since our initial purpose was to screen as many Rabs as possible (which included multiple CA and DN lines per Rab in most cases), we chose to compare CA and DN effects of the experimental animals only. As noted below, we expected that CA and DN would have opposing effects, although this is not always the case. We followed up Rabs that were promising in the initial screen by using Repo-G4 and including all of the parental controls. Rab11 was a positive in the DN/CA screen and, in subsequent assays, Rab11 CA consistently increased sleep relative to controls, regardless of whether it was expressed with Repo or BBB drivers. This is now clarified in the text.16) The overall logic that explains the link between the manipulations, endocytosis and sleep is confusing. Glial expression of Rab11CA increases sleep, similarly to Shi-ts. Do the authors propose that Rab11CA inhibits endocytosis, similarly to Shi-ts?Rab11 can affect endocytosis, as knockdown of Rab11 has been shown to inhibit endocytosis and transcytosis, for example, of LDL . Assuming that CA and DN have opposing effects, Rab11CA would not be expected to block endocytosis; however, DN and CA versions can sometimes go in the same direction, with WT or CA mutants blocking the pathway at a specific point due to limiting amounts of binding partners. In addition, for the Rab collection we used, the Rab CA and DNs are called so based on the best prediction that the sites mutated will be GTPase-defective and GTP-binding defective, rather than functional confirmation of their CA and DN activity; in fact, Rab3 is mislocalized with CA and DN mutations .In the case of Rab11, in particular, both the DN and CA mutations we used alter trafficking in one direction in certain contexts .Therefore, it is possible that Rab11 CA inhibits endocytosis. Alternatively, if the mechanism underlying the sleep phenotype involves, for example, a certain receptor, it is also possible that Rab11 CA and Shi lead to the same behavioral phenotype by keeping the receptor on the plasma membrane – Rab11CA by promoting its recycling, and Shi by inhibiting its endocytosis.17) On similar lines, the data shown in Figure 4C seem to contradict the data in Figure 4D. In Figure 4D, Repo>Rab11CA causes increased sleep, compared to parental controls. Figure 4C, shows that using the RepoGS driver, Rab11 DN induces even more sleep than Rab11CA. Thus, both of these opposing manipulations cause increased sleep. Could it be that both manipulations simply make glial cells sick, and therefore, in both cases, sleep is increased due to malfunction of glia, and not due to specific changes in endocytosis? The possibility for a non-specific effect is supported by similar data in Figure 4—figure supplement 1C, where expression of either DN or CA versions of Rab27 and Rab30 produces exactly the same phenotype of increased sleep, unless the authors believe that these also work by affecting endocytosis. These issues should be explained and addressed in a revised manuscript.We have driven Rab27 and Rab30 with the surface glial drivers, but have not found consistent phenotypes when limiting expression to these glial populations. Thus, Rab11 is specific in terms of increasing sleep through this population, as sleep-promoting effects of other Rabs likely map to other glia. The similarity of increased sleep may therefore suggest sleep functions of Rabs in other glial populations.Concerning the discrepancy between RepoG4 and RepoGS sleep effects of Rab11CA and DN, it is difficult to compare as Rab11 DN is lethal with constitutively expressed Gal4s, while CA is not. As we have stated above, it is possible that CA and DN mutations do not really have opposing effects at the cellular level. In this case one might argue that CA is simply a weaker blocking mutation than DN. Nevertheless, Repo>Rab11CA flies do not appear to be sick and show intact general integrity of the BBB .18) In Figure 4—figure supplement 1B- are the effects of Rab3, Rab9 and Rab11 that were significant in Figure 4A (without RU486) still significant in the Figure 4—figure supplement 1B (with RU486)? The significance stars in Figure 4—figure supplement 1B are shown only for Rab1, Rab5, Rab27 and Rab30.Our standard for selection, as further clarified in the manuscript, is that all available DNs be significantly different from all available CAs. By this measure Rab3, 9 and 11 are different in the no-RU condition, and 1, 5, 27 and 30 are such in the RU condition, hence the difference in stars. Nevertheless, if we look at individual comparisons for the aforementioned Rabs in the RU+ condition, we find that the lone DN line of Rab3 is significantly different from one (of two) CA lines, a single DN line for Rab9 is significantly different from both CAs, and a single DN line for Rab11 is significantly different from both CAs.19) The apparent contradiction between Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5 should be more clearly explained. Figure 5 shows that SD (which increases sleep pressure) causes increased endocytosis, but, Figure 1, Figure 2, Figure 3 and Figure 4, show that inhibiting endocytosis actually increases sleep, suggesting that endocytosis in BBB promotes wakefulness. An explanation that connects the two observations should be included.To address this point we have built upon a relevant line in the Discussion section. In short, we propose that endocytosis in the BBB is a function of sleep and serves a homeostatic need. As shown in Figure 5, endocytosis is enhanced during times of baseline sleep, or during rebound sleep induced by sleep deprivation. If endocytosis during sleep fulfills a homeostatic role, it would follow that endocytosis would decline as sleep continues , just as sleep pressure does. In the experiments where we inhibit endocytosis at the surface glia, we believe we have created a situation where the potential homeostatic function of endocytosis cannot be accomplished, hence these flies may not be able to dissipate sleep pressure, and therefore have perpetually increased sleep.20) Are the changes in endocytosis that correlate with sleep state specific only to surface glia? Are there changes in endocytosis in other populations of glial cells, as function of sleep?Our interest in endocytosis in the surface glia stems from our findings that Shi expression in these populations produces changes in sleep behavior. This does not exclude the possibility of sleep-dependent changes in endocytosis in other glial populations . For example, phagocytic engulfment may be altered in astrocytes by sleep deprivation .


Physical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and large proportions of older children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles are associated with other covariates and how they change over time.Valid accelerometer data were collected for 1132 children aged 6 and 1121 at age 9, with 565 children with data at both ages. We calculated the proportions of total wear time spent in sedentary, light and MVPA activity on both weekdays and weekends. Latent profile (class) analysis was applied separately to the two age groups to identify activity profiles. We then used latent transition analysis to explore transitions between profiles at the two time points.We identified five profiles of activity at age 6 and six profiles at age 9. Although profiles were not directly equivalent, five classes captured similar patterns at both ages and ranged from very active to inactive. At both ages, active profiles, where the majority achieved the recommended MVPA guidelines, were more likely to be active at weekends than on weekdays. There was substantial movement between classes, with strongest patterns of movement to classes with no change or a decrease in MVPA. Transition between classes was associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities.This paper is the first to apply latent profile analysis to the physical activity of UK children as they move through primary school. Profiles were identified at ages 6 and 9, reflecting different weekday and weekend patterns of physical activity and sedentary time. There was substantial movement between profiles between ages 6 and 9, mostly to no change or less active profiles. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted.The online version of this article (10.1186/s12966-018-0735-8) contains supplementary material, which is available to authorized users. Physical activity is associated with improved physical and mental wellbeing among children and young people . There iThe relationship between physical activity and sedentary behaviour is complex . While sCluster analysis  and lateThe aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles may change over time and how children move between them. In addition, we aim to explore how these profiles and transitions are associated with factors such as sex, BMI, deprivation and activities.Data are from the B-PROACT1V study, a longitudinal study that aimed to examine the physical activity and sedentary behaviours of primary school children aged 5–11 years, and their parents , 27. TheChildren wore a waist-worn ActiGraph wGT3X-BT accelerometer for 5 days, including two weekend days. Accelerometer data were processed using Kinesoft  and analysis was restricted to those children who provided at least 2 days of valid weekday data and one valid weekend day to provide a compromise between a typical day and maximising the sample size. A valid day was defined as at least 500 min of data, after excluding intervals of ≥60 min of zero counts allowing up to 2 min of interruptions . Valid dhttp://data.gov.uk/dataset/index-of-multiple-deprivation), were assigned to each child based on their reported home postcode. Higher IMD scores indicate a greater level of deprivation.Child height and weight were measured, and body mass index (BMI) was calculated and converted to an age- and sex-specific standard deviation score based on UK reference curves , 31. IndTo understand the contribution of specific domains of physical activity and sedentary behaviour to different physical activity profiles, further details of screen-viewing and activity were obtained. In both years, parents were asked about the number of hours their child typically spent in various screen-viewing activities on weekdays and at weekends  and these were combined to give a total number of average hours spent in screen-viewing on weekdays and weekends. Children completed a short questionnaire, in which they were asked about the frequency (coded from 0 = ‘Never’ to 3 = ‘5 days per week’) with which they engaged in different forms of activity outside school hours: sport or exercise club at school, sport or exercise club elsewhere, playing outdoors in their neighbourhood, and playing outdoors at home . These wLatent class analysis is a latent variable model that can be used to identify underlying homogenous subgroups in a population, based on one or more observed variables which may be categorical or continuous; in the latter case the analysis is often called latent profile analysis  or finitLatent transition analysis  is a lonWe fitted cross-sectional latent profile analysis models for age 6 and 9 separately, using as class variables the proportions of time spent in MVPA and sedentary time for weekdays and weekends . We assumed that the weekday and weekend sedentary proportion variances were the same within a class (and likewise for the MVPA variances) but allowed sedentary variances to differ from the MVPA variances, and both to differ across classes. Latent profile models usually assume conditional independence which is theoretically violated here as the proportions are correlated. To account for this, we allowed a residual covariance between weekday sedentary and MVPA proportions and the same for weekend proportions. We explored a few alternative specifications to assess the sensitivity of the final models to these assumptions, especially concerning the variance.p-value rejecting the k-1 class model in favour of the k class model. These criteria have been shown to perform well at identifying an appropriate number of classes [There is no single commonly-accepted criterion to determine the number of classes , so we u classes , 38. We  classes  and the Once the number of classes was chosen based on the steps outlined above, we applied descriptive labels to each class based on the estimated profile of sedentary/light/MVPA proportions within each class. Finally, to aid interpretation, we also estimated the expected proportion in each class meeting physical activity guidelines  based on the estimated parameters in each class. Once the classes had been identified we examined whether there were differences between them in terms of sex, standardised BMI z-score, IMD, number of hours of screen viewing and participation in out-of-school activities (for age 9 only) with a Wald test using the BCH method, which includes the classification error and is robust to assumption violations .A latent transition model –43 was uA total of 1690 children with valid accelerometer data at at least one time point were included. The model for age 6 was based on 1087 weekday accelerometer measurements and 980 weekend measurements, and the model for age 9 was based on 1059 weekday and 942 weekend measurements. Missing covariate information  in a sensitivity analysis and found a 5-class model fitted best, with class profiles very similar to the remaining 5 classes from the full data. This suggested that the main influence of the outliers was the formation of the extra class. As this class was too small for further analysis and caused identification and interpretation problems in further analyses, we have reported the 5-class model without outliers in the remainder of the paper (n = 1128). The full 6-class model that included the outliers is described in Additional file Additional file aper n = 128. The The weekday and weekend profiles for each class are shown in Fig. Highly active (9%): High and very high levels of MVPA, especially at weekends, combined with low sedentary proportion. Nearly all meet the recommended level of MVPA of 60+ mins/day (92% on weekdays and 100% on weekends).Active/light (29%): Higher MVPA than average and average to low sedentary proportion; a large proportion of non-sedentary time is light activity. Most meet the recommended MVPA (77% on weekdays and 87% on weekends).Active/sed (19%): Higher MVPA than average (similar to Active/light) but above average sedentary proportion. Most meet the recommended MVPA (77% and 86%).Inactive/light (15%): Low MVPA combined with average sedentary proportion; a large proportion of non-sedentary time is light activity. A minority met the recommended MVPA (33% on weekdays and 19% on weekends)Inactive/sed (28%): Low MVPA (similar to Inactive/Light) but combined with high sedentary proportion. A minority met the recommended MVPA (33% on weekdays and 20% on weekends).To aid interpretation we have organised the classes approximately in order from most active (highest MVPA) to least active (MVPA). The five classes were identified as follows:Highly active classes which contained children who are more active and less sedentary at the weekend than during the week. The two Active classes were similar in terms of MVPA but differed in time spent in sedentary and light activity, and likewise for the two Inactive classes. In the Highly active and Active classes, the majority met the recommendation of 60+ mins of MVPA, with more achieving the guidelines at weekends than on weekdays. Only a minority of those in the Inactive classes achieved the recommended MVPA levels, with this proportion higher on weekdays than weekends.Most classes captured similar patterns of physical activity and sedentary behaviour on weekdays and weekends, apart from the Highly active classes were predominantly boys (88%), while the Inactive/sed class comprised 60% girls  than other classes. BMI z-score at ages 6 or 9 and average hours of screen viewing were similar across the classes : Very similar to the Age 6 Highly active class. High and very high levels of MVPA, especially at weekends, combined with low sedentary time. Nearly all meet the recommended MVPA (90% on weekdays and 100% on weekends)Active/light (6%): Similar to the Age 6 Active/light class, but with slightly lower sedentary proportions. Higher MVPA than average and average to low sedentary – more so at weekends. A large proportion of non-sedentary time is spent in light activity rather than MVPA. Most meet the recommended MVPA (68% on weekdays and 78% on weekends)Active/sed (11%): Very similar to the Active/sed class at age 6. Higher MVPA than average (similar to Active/light), but above average sedentary times – more so at weekends. Most meet the recommended MVPA (72% on weekdays and 91% on weekends)Average (33%): No corresponding class at age 6. Average levels of sedentary and MVPA; slightly more active and less sedentary at weekends. The majority met the recommended MVPA (59% on weekdays and 73% on weekends).Inactive/light (22%): Similar pattern to the Age 6 Inactive/light class, but MVPA is much lower. Very low MVPA combined with average sedentary; a large proportion of non-sedentary time is spent in light activity. A minority meet the recommended MVPA (11% on weekdays and 11% on weekends)Inactive/sed (21%): Similar to the Age 6 Inactive/sed class. Low/ very low MVPA (similar to Inactive/light) but combined with very high sedentary proportions. A minority meet the recommended MVPA (42% on weekdays and 14% on weekends)When labelling the Age 9 classes, we used some of the Age 6 labels to reflect similar profiles. However, these classes are not directly equivalent as nearly all classes are more sedentary at age 9 than at age 6. Instead we have defined ‘similar’ in terms of patterns of high and low activity and sedentary proportions, when compared to the overall year average levels. This means, for example, that the Inactive/sed exhibiting lower sedentary proportions at weekends. The majority of children in the Highly Active, Active/light, Active/sed and Average classes met the recommended levels of MVPA, especially at weekends. Only a minority of those in the two Inactive classes met the recommended MVPA levels, with this proportion higher on weekdays than weekends.In contrast to the classes at age 6, we saw slightly stronger differences in weekday/weekend patterns of physical activity and sedentary time within the classes, with all classes except Highly active class were boys (93%) whereas most in the Inactive/light class were girls  and unstructured (playing outdoors) activities , but this did not fit the data well. This is supported by the cross-sectional findings, which found a different number of latent classes at each age and differences in the class definitions, in particular with higher sedentary proportions at age 9. The results presented here are based on a non-invariance model (classes differ), with latent class profiles fixed to be the same as those found in the cross-sectional analyses.Active/light class, all Age 9 classes were more sedentary than their Age 6 counterparts, and so children who move to a more active class may still become more sedentary. The most common patterns of movement were to classes with either no change or a decrease in MVPA (84%), and children in active classes at age 6 were more likely to remain in active classes at age 9 (45%) than children in inactive classes at age 6 were to move to active classes at age 9 (12%). Children who move from Active to Inactive classes tended to keep the same light/sedentary behaviour, while children who moved away from Highly active tended to move to the light rather sedentary classes.Figure Active/sed class at age 6 had a 43% probability of staying in the similar Age 9 class and 30% probability of moving to Inactive/sed. For children with a BMI z-score of 1  these tHighly active class between ages 6 and 9 is strongly associated with higher activity participation. A child in the Highly active class at age 6 with a participation score at the average of 6 has a 32% probability of remaining in the Highly active class, increasing by approximately 8 percentage points for every extra activity session per week (one unit increase in participation score). A higher participation score is also associated with an increase in the probability of moving into the Highly active class at age 9 from other classes. Activity participation was also associated with moving from Inactive classes to Active classes, although the numbers of children transitioning between these two was small. Patterns were similar for associations with structured and unstructured activity (not shown), but with high levels of unstructured activity and low levels of structured activity associated with a move to the Active/light class, and high levels of structured activity associated with movement to the Active/sed class.The probability of remaining in the Active/sed and Inactive/sed classes at age 6 where more hours spent screen viewing increased the probability of transitioning to the Inactive/sed class at age 9. These probabilities are 31% for Active/sed and 47% for Inactive/sed for an average screen viewing of 3.8 h at weekends, increasing to 41% and 58% for an extra 2 h. In the Highly active class, the opposite pattern was seen, with very high screen-viewing associated with remaining in the Highly active class.Transition probabilities were associated with the average number of hours spent screen viewing at weekends but not on weekdays. The strongest patterns were for the The data presented in this paper have shown that five different classes described the physical activity and sedentary behaviour of pupils at age 6 and six classes at age 9. The profiles range from very active to inactive with differentiation between subgroups in terms of sedentary and light behaviour. Five classes were found to be similar between the 2 years, in terms of general patterns, with the addition of a substantial extra class of ‘average’ children at age 9 who fell between the more extreme active and inactive classes seen at age 6. This suggests that the classes are relatively stable, although the proportions in each class change, with the active classes decreasing in prevalence. It is important to note that consistent with a previous study using similar methods applied to physical activity data from several countries , all claAll Age 9 classes were more sedentary than their Age 6 counterparts. There was substantial movement between classes between age 6 and 9 with less than a third of pupils remaining in the similar class across the two time points. Across this time, the strongest patterns of movement were to profiles with either no change or a decrease in MVPA, and even where children moved to a more active class they typically become more sedentary. This highlights a need to understand the factors that are associated with change in activity profile from age 6 to 9 and how to mitigate movement to less active profiles.Highly active class is predominantly boys at both ages, with the proportion of boys increasing from 88 to 93%. Likewise, at the other end of the scale the Inactive classes have a higher proportion of girls. Gender is also strongly associated with movement between classes with girls more likely to move to less active classes and boys more likely to move to more active classes. Unfortunately, sample sizes were too small to produce separate profiles for boys and girls, especially to look at change over time or associations with covariates. However, the findings do suggest that girls may particularly be at risk of moving to less active profiles.The most robust finding from previous studies is an association between girls and low-activity clusters  and thisHighly active class. This indicates that both structured and unstructured activities may contribute to children’s overall physical activity levels and maintained engagement in such activities may mitigate some of the age-related decline in activity. Conversely, screen-viewing was associated with becoming more sedentary, rather than becoming less active. For example, in the Highly active class, more screen-viewing was associated with remaining Highly active, suggesting that for this class, screen-viewing occurs in addition to, rather than instead of, being active. This is consistent with a male ‘techno-active’ group commonly identified in cluster analyses [Participants’ body mass-index (z-score), screen-viewing behaviour and the extent to which they engaged in activities were associated with movement between classes. These characteristics were not strongly associated with class membership at age 6 but showed much stronger links with classes at age 9. Movement to more active classes was associated with a lower BMI z-score at age 6 and higher activity participation, with the latter strongly associated with movement into the analyses . Collectanalyses  and thatanalyses . The finActive/light and Active/sed classes at age 6 have similar MVPA but have distinct patterns of movement. Most of the Active/light class move to the new Average class at age 9; their proportion of non-sedentary time spend in MVPA remains the same but as sedentary time increases, the total MVPA decreases. Meanwhile, children in the Active/sed class are most likely to either remain in Active/sed  or move to Inactive/sed (more sedentary time and less MPVA). In both cases, those who move to Inactive classes tend to keep the same light/sedentary behaviour. This suggests that sedentary patterns reflect underlying behaviour preferences which are independent of how much activity children engage in. This finding suggests that early physical activity behaviours may contribute to physical activity throughout childhood and developing strategies to engage children in physical activity early and then keep them active are likely to be very important. Research that examines this possibility is therefore essential for the advancement of the field.A key finding from this study was that children who are active are more likely to remain active than inactive children to become active. For example, children in the Active/light and Active/sed classes may be due to changes in the amount of structured versus unstructured activity, with parents reporting a decrease in free play between age 6 and 9 [Active/light and Active/sed is that the former class engage in predominantly unstructured activity while the latter in structured activity. A gradual decline in the amount of unstructured activity, when not replaced by structured activities results in a decline in activity levels . Conversely, those in the Active/sed class may either continue their club activity and remain in Active/sed, or experience a sudden drop in MVPA when they stop the activity and they transition to the Inactive/sed class. In addition, we note that both Active/sed and Inactive/sed are more likely at age 9 to meet the recommended 60 min of MVPA than their light counterparts, despite having similar MVPA and engaging in more sedentary time. This suggests that unstructured activities may be more variable in the intensity of activity (perhaps more often dipping into light rather than moderate activity), whereas structured activity may enable children to more consistently meet MVPA guidelines. This highlights the potential important role of structured activities, such as after-school activity clubs, in promoting and maintaining physical activity in childhood, and if unstructured activity continues to decline may indicate that those in the Average and Active/light classes are at risk of becoming more inactive in the future.The differences in movement from the  6 and 9 . While aThe major strength of this study is the use of objective physical activity and sedentary behaviour data to examine how physical activity profiles change from Year 1 (age 6) to Year 4 (age 9) of primary school. By applying latent profile analysis to a contextually rich dataset we have provided new information which provides some insights into the change in profiles and the potentially important role of weekend physical activity. The study does however have several limitations that need to be considered. Although the data are from a relatively large cohort, the sample is from a single UK city and some of the transitions are based on a relatively small number of cases. As a result, we are unable to conduct separate analyses for boys and girls, despite the strong associations between gender and latent classes. The class grouping was relatively consistent between the two time points, which suggests a degree of robustness, but the latent class approach is data driven which makes comparisons between studies more difficult. Finally, it is also important to recognise that the move from Year 1 to Year 4 is a key period of change in children’s lives when their motor skills develop, they get increased licence to be physically active and there are numerous other social changes, such as going to bed later and increased homework, which can all impact on physical activity and sedentary time . These bFive profiles were identified at ages 6 and six profiles at age 9, reflecting different patterns of physical activity and sedentary time, and differences between weekdays and weekends. There was substantial movement between profiles between ages 6 and 9, with transitions associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities. Our results highlight the importance of engaging children in physical activity early and the potential important role of structured activities, such as after-school activity clubs, in promoting and maintaining physical activity in childhood. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted.Additional file 1:Table S1. Characteristics of sample – observed and missing data. Table S2. Model Fit for models with 2–10 classes. Table S3. Class membership proportions and percentage time in sedentary, light and MVPA respectively. Table. Age 9: Model-based estimates of additional covariate means and test for differences across classes. Table S5. Estimated transition probabilities: probability a child will move from a profile at age 6 to a profile at age 9. Figure S1. Associations between transition probabilities and BMI z-score at age 6. How transition probabilities from classes at age 6 (panels) to classes at age 9 (lines) change with BMI z-score at age 6. Figure S2. Associations between transition probabilities and activity participation. How transition probabilities from classes at age 6 (panels) to classes at age 9 (lines) change with activity participation score. A one unit increase in activity score corresponds to approximately one extra session of activity per week. Figure S3. Associations between transition probabilities and weekend screen-viewing. How transition probabilities from classes at age 6 (panels) to classes at age 9 (lines) change with hours of weekend screen-viewing. (DOCX 397 kb)