scholarly journals Sleep deprivation suppresses aggression in Drosophila

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Matthew S Kayser ◽  
Benjamin Mainwaring ◽  
Zhifeng Yue ◽  
Amita Sehgal
Keyword(s):  
Sleep Deprivation ◽  
Sleep Disturbances ◽  
Social Behaviors ◽  
Fruit Fly ◽  
Reproductive Fitness ◽  
Sleep Loss ◽  
Aggressive Behaviors ◽  
Clear Effect ◽  
Competitive Disadvantage ◽  
Acute Sleep Deprivation

Sleep disturbances negatively impact numerous functions and have been linked to aggression and violence. However, a clear effect of sleep deprivation on aggressive behaviors remains unclear. We find that acute sleep deprivation profoundly suppresses aggressive behaviors in the fruit fly, while other social behaviors are unaffected. This suppression is recovered following post-deprivation sleep rebound, and occurs regardless of the approach to achieve sleep loss. Genetic and pharmacologic approaches suggest octopamine signaling transmits changes in aggression upon sleep deprivation, and reduced aggression places sleep-deprived flies at a competitive disadvantage for obtaining a reproductive partner. These findings demonstrate an interaction between two phylogenetically conserved behaviors, and suggest that previous sleep experiences strongly modulate aggression with consequences for reproductive fitness.

scholarly journals Sleep-dependent modulation of metabolic rate in Drosophila

2017 ◽  
Author(s):  
Bethany A. Stahl ◽  
Melissa E. Slocumb ◽  
Hersh Chaitin ◽  
Justin R. DiAngelo ◽  
Alex C. Keene
Keyword(s):  
Metabolic Rate ◽  
Sleep Disturbances ◽  
Metabolic Diseases ◽  
Fruit Fly ◽  
Sleep Loss ◽  
Negative Energy ◽  
Neural Systems ◽  
Normal Sleep ◽  
Induced Changes ◽  
The Relationship

AbstractDysregulation of sleep is associated with metabolic diseases, and metabolic rate is acutely regulated by sleep-wake behavior. In humans and rodent models, sleep loss is associated with obesity, reduced metabolic rate, and negative energy balance, yet little is known about the neural mechanisms governing interactions between sleep and metabolism. We have developed a system to simultaneously measure sleep and metabolic rate in individual Drosophila, allowing for interrogation of neural systems governing interactions between sleep and metabolic rate. Like mammals, metabolic rate in flies is reduced during sleep and increased during sleep deprivation suggesting sleep-dependent regulation of metabolic rate is conserved across phyla. The reduction of metabolic rate during sleep is not simply a consequence of inactivity because metabolic rate is reduced ∼30 minutes following the onset of sleep, raising the possibility that CO2 production provides a metric to distinguish different sleep states in the fruit fly. To examine the relationship between sleep and metabolism, we determined basal and sleep-dependent changes in metabolic rate is reduced in starved flies, suggesting that starvation inhibits normal sleep-associated effects on metabolic rate. Further, translin mutant flies that fail to suppress sleep during starvation demonstrate a lower basal metabolic rate, but this rate was further reduced in response to starvation, revealing that regulation of starvation-induced changes in metabolic rate and sleep duration are genetically distinct. Therefore, this system provides the unique ability to simultaneously measure sleep and oxidative metabolism, providing novel insight into the physiological changes associated with sleep and wakefulness in the fruit fly.Significance statementMetabolic disorders are associated with sleep disturbances, yet our understanding of the mechanisms underlying interactions between sleep and metabolism remain limited. Here, we describe a novel system to simultaneously record sleep and metabolic rate in single Drosophila. Our findings reveal that uninterrupted sleep bouts of 30 minutes or greater are associated with a reduction in metabolic rate providing a physiological readout of sleep. Use of this system, combined with existing genetic tools in Drosophila, will facilitate identification of novel sleep genes and neurons, with implications for understanding the relationship between sleep loss and metabolic disease.

scholarly journals Circadian and sleep/wake-dependent variations in tau phosphorylation are driven by temperature

SLEEP ◽  
2019 ◽  
Vol 43 (4) ◽  
Author(s):  
Isabelle Guisle ◽  
Maud Gratuze ◽  
Séréna Petry ◽  
Françoise Morin ◽  
Rémi Keraudren ◽  
...  
Keyword(s):  
Body Temperature ◽  
Sleep Deprivation ◽  
Tau Protein ◽  
Sleep Disturbances ◽  
Tau Phosphorylation ◽  
Physiological Basis ◽  
Physiological Regulation ◽  
Hyperphosphorylated Tau Protein ◽  
Phosphatase 2A ◽  
Acute Sleep Deprivation

Abstract Study Objectives Aggregates of hyperphosphorylated tau protein are a hallmark of Alzheimer’s disease (AD) and other tauopathies. Sleep disturbances are common in AD patients, and insufficient sleep may be a risk factor for AD. Recent evidence suggests that tau phosphorylation is dysregulated by sleep disturbances in mice. However, the physiological regulation of tau phosphorylation during the sleep–wake cycle is currently unknown. We thus aimed to determine whether tau phosphorylation is regulated by circadian rhythms, inherently linked to the sleep–wake cycle. Methods To answer these questions, we analyzed by Western blotting tau protein and associated kinases and phosphatases in the brains of awake, sleeping, and sleep-deprived B6 mice. We also recorded their temperature. Results We found that tau phosphorylation undergoes sleep-driven circadian variations as it is hyperphosphorylated during sleep but not during acute sleep deprivation. Moreover, we demonstrate that the mechanism behind these changes involves temperature, as tau phosphorylation was inversely correlated with circadian- and sleep deprivation-induced variations in body temperature, and prevented by housing the animals at a warmer temperature. Notably, similar changes in tau phosphorylation were reproduced in neuronal cells exposed to temperatures recorded during the sleep–wake cycle. Our results also suggest that inhibition of protein phosphatase 2A (PP2A) may explain the hyperphosphorylation of tau during sleep-induced hypothermia. Conclusion Taken together, our results demonstrate that tau phosphorylation follows a circadian rhythm driven mostly by body temperature and sleep, and provide the physiological basis for further understanding how sleep deregulation can affect tau and ultimately AD pathology.

scholarly journals The Effect of Sleep Deprivation and Subsequent Recovery Period on the Synaptic Proteome of Rat Cerebral Cortex

2022 ◽  
Author(s):  
Péter Gulyássy ◽  
Katalin Todorov-Völgyi ◽  
Vilmos Tóth ◽  
Balázs A. Györffy ◽  
Gina Puska ◽  
...  
Keyword(s):  
Synaptic Transmission ◽  
Sleep Deprivation ◽  
Sleep Disturbances ◽  
Recovery Period ◽  
Sleep Loss ◽  
Synaptic Function ◽  
Synaptic Proteins ◽  
Human Cost ◽  
Vesicle Exocytosis ◽  
Altered Proteins

AbstractSleep deprivation (SD) is commonplace in the modern way of life and has a substantial social, medical, and human cost. Sleep deprivation induces cognitive impairment such as loss of executive attention, working memory decline, poor emotion regulation, increased reaction times, and higher cognitive functions are particularly vulnerable to sleep loss. Furthermore, SD is associated with obesity, diabetes, cardiovascular diseases, cancer, and a vast majority of psychiatric and neurodegenerative disorders are accompanied by sleep disturbances. Despite the widespread scientific interest in the effect of sleep loss on synaptic function, there is a lack of investigation focusing on synaptic transmission on the proteome level. In the present study, we report the effects of SD and recovery period (RP) on the cortical synaptic proteome in rats. Synaptosomes were isolated after 8 h of SD performed by gentle handling and after 16 h of RP. The purity of synaptosome fraction was validated with western blot and electron microscopy, and the protein abundance alterations were analyzed by mass spectrometry. We observed that SD and RP have a wide impact on neurotransmitter-related proteins at both the presynaptic and postsynaptic membranes. The abundance of synaptic proteins has changed to a greater extent in consequence of SD than during RP: we identified 78 proteins with altered abundance after SD and 39 proteins after the course of RP. Levels of most of the altered proteins were upregulated during SD, while RP showed the opposite tendency, and three proteins (Gabbr1, Anks1b, and Decr1) showed abundance changes with opposite direction after SD and RP. The functional cluster analysis revealed that a majority of the altered proteins is related to signal transduction and regulation, synaptic transmission and synaptic assembly, protein and ion transport, and lipid and fatty acid metabolism, while the interaction network analysis revealed several connections between the significantly altered proteins and the molecular processes of synaptic plasticity or sleep. Our proteomic data implies suppression of SNARE-mediated synaptic vesicle exocytosis and impaired endocytic processes after sleep deprivation. Both SD and RP altered GABA neurotransmission and affected protein synthesis, several regulatory processes and signaling pathways, energy homeostatic processes, and metabolic pathways.

Sleep, health, and society

2018 ◽  
Author(s):  
Francesco P. Cappuccio ◽  
Michelle A. Miller ◽  
Steven W. Lockley ◽  
Shantha M. W. Rajaratnam
Keyword(s):  
Sleep Deprivation ◽  
Sleep Duration ◽  
Sleep Disturbances ◽  
Modern Society ◽  
Social Conditions ◽  
Sleep Health ◽  
Health And Wellbeing

Sleep disturbances are common in modern society. Since the beginning of the century, populations have shown a decline in sleep duration, owing to changes in environmental and social conditions. Industry was the first to appreciate the detrimental effects of sleep disturbances on health and wellbeing. It has taken, however, many decades to understand the implications for individuals and populations of sustained sleep deprivation.

scholarly journals Residual, differential neurobehavioral deficits linger after multiple recovery nights following chronic sleep restriction or acute total sleep deprivation

SLEEP ◽  
2020 ◽  
Author(s):  
Erika M Yamazaki ◽  
Caroline A Antler ◽  
Charlotte R Lasek ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Response Speed ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Total Sleep Deprivation ◽  
Psychomotor Vigilance Test ◽  
Recovery Sleep ◽  
Time In Bed ◽  
Neurobehavioral Deficits ◽  
Chronic Sleep

Abstract Study Objectives The amount of recovery sleep needed to fully restore well-established neurobehavioral deficits from sleep loss remains unknown, as does whether the recovery pattern differs across measures after total sleep deprivation (TSD) and chronic sleep restriction (SR). Methods In total, 83 adults received two baseline nights (10–12-hour time in bed [TIB]) followed by five 4-hour TIB SR nights or 36-hour TSD and four recovery nights (R1–R4; 12-hour TIB). Neurobehavioral tests were completed every 2 hours during wakefulness and a Maintenance of Wakefulness Test measured physiological sleepiness. Polysomnography was collected on B2, R1, and R4 nights. Results TSD and SR produced significant deficits in cognitive performance, increases in self-reported sleepiness and fatigue, decreases in vigor, and increases in physiological sleepiness. Neurobehavioral recovery from SR occurred after R1 and was maintained for all measures except Psychomotor Vigilance Test (PVT) lapses and response speed, which failed to completely recover. Neurobehavioral recovery from TSD occurred after R1 and was maintained for all cognitive and self-reported measures, except for vigor. After TSD and SR, R1 recovery sleep was longer and of higher efficiency and better quality than R4 recovery sleep. Conclusions PVT impairments from SR failed to reverse completely; by contrast, vigor did not recover after TSD; all other deficits were reversed after sleep loss. These results suggest that TSD and SR induce sustained, differential biological, physiological, and/or neural changes, which remarkably are not reversed with chronic, long-duration recovery sleep. Our findings have critical implications for the population at large and for military and health professionals.

121 Behavioral Attention Relationships Vary Between Demographic Groups Across Sleep Loss and Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A49-A50
Author(s):  
Caroline Antler ◽  
Erika Yamazaki ◽  
Tess Brieva ◽  
Courtney Casale ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Age Groups ◽  
Response Speed ◽  
Sleep Loss ◽  
Demographic Groups ◽  
Time In Bed ◽  
Median Split ◽  
The Relationship ◽  
High Bmi

Abstract Introduction The Psychomotor Vigilance Test (PVT) is a behavioral attention measure widely used to describe sleep loss deficits. Although there are reported differences in PVT performance for various demographic groups, no study has examined the relationship between measures on the 10-minute PVT (PVT10) and the 3-minute PVT (PVT3) within sex, age, and body mass index (BMI) groups throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (mean±SD ages, 33.9±8.9y) participated in a 13-night experiment [2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD)]. A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Repeated measures correlation (rmcorr) compared PVT10 and PVT3 lapses (reaction time [RT] >355ms [PVT3] and >500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and time point (e.g., 1000h-2000h) within sex groups (18 females), within age groups defined by a median split (median=32, range=21-49y), and within BMI groups defined by a median split (median=25, range=17-31). Results PVT10 and PVT3 1/RT was significantly correlated at all study days and time points excluding at baseline for the younger group and at R2 for the higher BMI group. PVT10 and PVT3 lapses showed overall lower correlations across the study relative to 1/RT. Lapses were not significantly correlated at baseline for any group, for males across recovery (R1-R4), for the high BMI group at R2-R4, for the older group at R2-R3, or for the younger group at SR5 or R3. Conclusion Differentiating participants based on age, sex, or BMI revealed important variation in the relationship between PVT10 and PVT3 measures across the study. Surprisingly, lapses were not significantly correlated at baseline for any demographic group or across recovery for males or the high BMI or older group. Thus, PVT10 and PVT3 lapses may be less comparable in certain populations when well-rested. These findings add to a growing literature suggesting demographic factors may be important factors to consider when evaluating the effects of sleep loss. Support (if any) ONR Award N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

293 Predictive utility of a brief scale to identify U.S. Army Soldiers who are genetically vulnerable and resilient to sleep loss

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A117-A117
Author(s):  
Janna Mantua ◽  
Carolyn Mickelson ◽  
Jacob Naylor ◽  
Bradley Ritland ◽  
Alexxa Bessey ◽  
...  
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Sleep Deprivation ◽  
Cognitive Performance ◽  
Response Inhibition ◽  
Sleep Loss ◽  
Cutoff Score ◽  
Cognitive Subscale ◽  
Army Soldiers ◽  
Psychomotor Vigilance

Abstract Introduction Sleep loss that is inherent to military operations can lead to cognitive errors and potential mission failure. Single Nucleotide Polymorphisms (SNPs) allele variations of several genes (COMT, ADORA2A, TNFa, CLOCK, DAT1) have been linked with inter-individual cognitive resilience to sleep loss through various mechanisms. U.S. Army Soldiers with resilience-related alleles may be better-suited to perform cognitively-arduous duties under conditions of sleep loss than those without these alleles. However, military-wide genetic screening is costly, arduous, and infeasible. This study tested whether a brief survey of subjective resilience to sleep loss (1) can demarcate soldiers with and without resilience-related alleles, and, if so, (2) can predict cognitive performance under conditions of sleep loss. Methods Six SNPs from the aforementioned genes were sequenced from 75 male U.S. Army special operations Soldiers (age 25.7±4.1). Psychomotor vigilance, response inhibition, and decision-making were tested after a night of mission-driven total sleep deprivation. The Iowa Resilience to Sleeplessness Test (iREST) Cognitive Subscale, which measures subjective cognitive resilience to sleep loss, was administered after a week of recovery sleep. A receiver operating characteristic (ROC) curve was used to determine whether the iREST Cognitive Subscale can discriminate between gene carriers, and a cutoff score was determined. Cognitive performance after sleep deprivation was compared between those below/above the cutoff score using t-tests or Mann-Whitney U tests. Results The iREST discriminated between allele variations for COMT (ROC=.65,SE=.07,p=.03), with an optimal cutoff score of 3.03 out of 5, with 90% sensitivity and 51.4% specificity. Soldiers below the cutoff score had significantly poorer for psychomotor vigilance reaction time (t=-2.39,p=.02), response inhibition errors of commission (U=155.00,W=246.00,p=.04), and decision-making reaction time (t=2.13,p=.04) than Soldiers above the cutoff score. Conclusion The iREST Cognitive Subscale can discriminate between those with and without specific vulnerability/resilience-related genotypes. If these findings are replicated, the iREST Cognitive Subscale could be used to help military leaders make decisions about proper personnel placement when sleep loss is unavoidable. This would likely result in increased safety and improved performance during military missions. Support (if any) Support for this study came from the Military Operational Medicine Research Program of the United States Army Medical Research and Development Command.

137 Recovery Dynamics in a Biomathematical Model of Fatigue

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A56-A56
Author(s):  
Mark McCauley ◽  
Peter McCauley ◽  
Hans Van Dongen
Keyword(s):  
Sleep Deprivation ◽  
Goodness Of Fit ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Commercial Aviation ◽  
Circadian Misalignment ◽  
Total Sleep Deprivation ◽  
Homeostatic Process ◽  
Recovery Sleep

Abstract Introduction In commercial aviation and other operational settings where biomathematical models of fatigue are used for fatigue risk management, accurate prediction of recovery during rest periods following duty periods with sleep loss and/or circadian misalignment is critical. The recuperative potential of recovery sleep is influenced by a variety of factors, including long-term, allostatic effects of prior sleep/wake history. For example, recovery tends to be slower after sustained sleep restriction versus acute total sleep deprivation. Capturing such dynamics has proven to be challenging. Methods Here we focus on the dynamic biomathematical model of McCauley et al. (2013). In addition to a circadian process, this model features differential equations for sleep/wake regulation including a short-term sleep homeostatic process capturing change in the order of hours/days and a long-term allostatic process capturing change in the order of days/weeks. The allostatic process modulates the dynamics of the homeostatic process by shifting its equilibrium setpoint, which addresses recently observed phenomena such as reduced vulnerability to sleep loss after banking sleep. It also differentiates the build-up and recovery rates of fatigue under conditions of chronic sleep restriction versus acute total sleep deprivation; nonetheless, it does not accurately predict the disproportionately rapid recovery seen after total sleep deprivation. To improve the model, we hypothesized that the homeostatic process may also modulate the allostatic process, with the magnitude of this effect scaling as a function of time awake. Results To test our hypothesis, we added a parameter to the model to capture modulation by the homeostatic process of the allostatic process build-up during wakefulness and dissipation during sleep. Parameter estimation using previously published laboratory datasets of fatigue showed this parameter as significantly different from zero (p<0.05) and yielding a 10%–20% improvement in goodness-of-fit for recovery without adversely affecting goodness-of-fit for pre-recovery days. Conclusion Inclusion of a modulation effect of the allostatic process by the homeostatic process improved prediction accuracy in a variety of sleep loss and circadian misalignment scenarios. In addition to operational relevance for duty/rest scheduling, this finding has implications for understanding mechanisms underlying the homeostatic and allostatic processes of sleep/wake regulation. Support (if any) Federal Express Corporation

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