Between 2013 and 2022, the share of adults in the U.S. not getting enough sleep fluctuated between roughly 33 and 37 percent. In 2022, this figure reached 36.8 percent, the highest share in the given period. This statistic displays the share of adults getting insufficient sleep in the U.S. between 2013 and 2022.

Sleep deprivation affects cognitive performance and immune function, yet its mechanisms and biomarkers remain unclear. This study explored the relationships among gene expression, brain metabolism, sleep deprivation, and sex differences. Methods Fluorodeoxyglucose-18 positron emission tomography (18F-FDG PET) measured brain metabolism in regions of interest (ROIs), and RNA analysis of blood samples assessed gene expression pre- and post-sleep deprivation. Mixed model regression and principal component analysis (PCA) identified significant genes and regional metabolic changes. Results There were 23 and 28 differentially expressed probesets for the main effects of sex and sleep deprivation, respectively, and 55 probesets for their interaction (FDR-corrected p<0.05). Functional analysis revealed enrichment in nucleoplasm- and UBL conjugation-related genes. Genes showing significant sex effects mapped to chromosomal regions Y and 19 (Benjamini-Hochberg (BH) FDR p<0.05), with 11 genes (4%) and 29 genes (10.5%) involved, respectively. Differential gene expression highlighted sex-based differences in innate and adaptive immunity. For brain metabolism, sleep deprivation resulted in significant decreases in the left insula, medial prefrontal cortex (BA32), somatosensory cortex (BA1/2), and motor premotor cortex (BA6) and increases in the right inferior longitudinal fasciculus, primary visual cortex (BA17), amygdala, cerebellum, and bilateral pons. Hemispheric asymmetry in brain metabolism was observed, with BA6 decreases correlating with increased UBL conjugation gene expression. Conclusion Sleep deprivation broadly impacts brain metabolism, gene expression, and immune function, revealing cellular stress responses and hemispheric vulnerability. These findings enhance understanding of the molecular and functional effects of sleep deprivation. Methods Sleep Deprivation Eight healthy subjects, 4 male and 4 female, were recruited from the University of California Irvine, after IRB approval. On day 1, subjects were initially assigned a 24-hour period of normal activity (e.g. walk, talk, study, watch TV, play games, use the computer, etc.). These subjects were tested on the Psychomotor Vigilance Test (PVT) and asked to rate their subjective level of sleepiness on the Stanford Sleepiness Scale (SSS) at baseline. Higher scores indicate a longer, more delayed, response time on the PVT, while higher scores on the SSS indicate greater degrees of sleepiness. The SSS scale is shown in Table 1. Each subject’s performance on the Psychomotor Vigilance Test (PVT), and subjective sleepiness ratings (SSS) were recorded both before and after sleep deprivation (Table 2). There was no significant difference in age between male and female subjects (Table 3), all of whom had no prior psychiatric history. Blood samples were collected on baseline day at 1 p.m, pre-sleep deprivation (pre-SD). Sleep deprivation activities and blood sample acquisition times are recorded in Table 4. At the end of day 1 (11 p.m), subjects were moved to an outpatient research facility for the sleep deprivation protocol. They were requested not to nap or sleep during the sleep deprivation period, and were additionally tasked with filling out forms and answering questions about their mood every two to four hours. Staff members monitored the subjects during the sleep deprivation period. Subjects were allowed to walk, talk, study, watch TV, play games or cards, read, and use the computer, but were not allowed caffeinated foods or beverages. A second blood sample was collected 18 hours after starting sleep deprivation activities (SD Day 2, 1 p.m), subjects completed the protocol and were driven home by cab. Gene Data Processing Blood samples (3 ml) were drawn from each subject, into Tempus tubes (ABI, ThermoFisher, Carlsbad, CA) 24 hours apart. The blood samples collected at baseline and 18 hours after starting sleep deprivation activities were processed with Affymetrix HG-U133 Plus 2.0 gene expression microarray chips according to the manufacturer’s instructions (Affymetrix, ThermoFisher, Carlsbad CA). Data processing was done using R 4.2 and BioConductor 3.16 [32]. The Affymetrix HG-U133 Plus 2.0 microarray ‘cel’ files were read using the affy routine with the hgu133plus2.db package. Quantile normalization was used to standardize probeset data [33]. A linear model was fitted to the expression data for each probeset using ‘lmfit’ from the limma package, to eliminate weakly expressed probesets, and the top 40,000 probesets were found using the topTables function. Type III mixed ANOVA was implemented using the lmerTest library in R, with the main effects being sex, sleep deprivation, and sleep deprivation-sex interaction. Age and RNA integrity number (RIN) were used as covariates. The top 300 probesets for each main effect from mixed ANOVA and PCA were analyzed for enrichment using the Database for Annotation, Visualization and Integrated Discovery (DAVID) [34; 35]. Principal component analysis was conducted using the pca function with normalized and scaled expression data. F18-FDG PET Scan Processing The pre-SD and post-SD F18 FDG-PET scans were obtained from each subject. Each F18-FDG PET scan was normalized in MATLAB (Mathworks, Sherborn, Massachusetts, USA) using Statistical Parametric Mapping (SPM) 5 software (Functional Imaging Laboratory, Wellcome Department of Cognitive Neurology, University College London, London, UK) to spatially transform the images to a template conforming to the space derived from standard brains from the Montreal Neurological Institute, and convert it to the space of the stereotactic atlas of Talairach and Tournoux. The images were then smoothed with a Gaussian low-pass filter of 8mm to minimize noise and improve spatial alignment. Regions of interest (ROI) analysis was done by extracting metabolic values from regions of interest using VINCI (“Volume Imaging in Neurological Research, Co-Registration and ROI included”) software. Supplementary Figure 1 shows ROI segmentation of FDG-PET scans labeled with brain regions and Brodmann areas (BA). A type III mixed two-way ANOVA was implemented using the lmerTest library in R. The model considered sex as a between-subjects factor and condition (pre-sleep deprivation vs. post-sleep deprivation) as a within-subjects factor. Principal component analysis was performed using the pca() function in the BioConductor environment [32] in R. Prior to extracting principal components, all probesets were scaled by extracting the mean value and dividing by the standard deviation for that variable in R.

Sleep is broadly conserved across the animal kingdom but can vary widely between species. It is currently unclear which selective pressures and regulatory mechanisms influence differences in sleep between species. The fruit fly Drosophilamelanogaster has become a successful model system for examining sleep regulation and function, but little is known about the sleep patterns in many related fly species. Here, we find that fly species with adaptations to extreme desert environments, including D. mojavensis, exhibit strong increases in baseline sleep compared to D. melanogaster. Long-sleeping D. mojavensis show intact homeostasis, indicating that desert flies carry an elevated drive for sleep. In addition, D. mojavensis exhibit altered abundance or distribution of several sleep/wake related neuromodulators and neuropeptides that are consistent with their reduced locomotor activity and increased sleep. Finally, we find that in a nutrient-deprived environment, the sleep patterns of individual D. mojavensis are strongly correlated with their survival time and that disrupting sleep via constant light stimulation renders D. mojavensis more sensitive to starvation. Our results demonstrate that D. mojavensis is a novel model for studying organisms with high sleep drive, and for exploring sleep strategies that provide resilience in extreme environments. Methods Behavior 4-8 day old female flies were housed individually in borosilicate glass tubes (65mm length, 5mm diameter) containing fly food coated with paraffin wax at one end and a foam plug in the other. Locomotor activity was recorded using DAM5M or DAM5H multibeam Drosophila Activity Monitors from Trikinetics Inc. (Waltham MA, USA) and sleep was analyzed in Matlab (MathWorks Inc) with the SCAMP script package90. Locomotor activity was measured as the number of movements between beams per one-minute bins. Periods of sleep were defined by at least 5 minutes with no change in position within the multibeam activity monitors. Sleep time courses display 30-min time bins. Sleep Deprivation and Arousability Sleep deprivations were performed mechanically by mounting DAM5M activity monitors onto platform vortexers (VWR 58816-115). Individual tubes were plugged with food at one end and 3D-printed PLA plastic caps at the other. Monitors were vortexed at an intensity of 2.5g for 3-second pulses every minute through the duration of the 12-hour dark period. Arousability was tested in a darkened incubator with 60 seconds of blue light (luminance 0.048 Lv) every hour for 24 hours following sleep deprivation. Food- and Water- Deprivation Assays All flies were put in DAM5H activity monitors on standard food for baseline recording. After 2-3 days, control flies were transferred to tubes containing fresh food, food-deprived flies to tubes containing a 1% agar gel, and food-and-water-deprived flies to empty tubes plugged with foam at both ends. Flies immobile for at least 24 hours were defined as dead and data subsequent to their last full day alive was removed from sleep analysis. Pharmacological Microinjections 4-8 day old female flies were loaded into behavior tubes and monitored in DAM5M Activity Monitors to obtain baseline sleep and locomotor activity under 12h light: 12h dark (25˚C). After 1-2 days of baseline in DAM5M monitors, flies housed in borosilicate tubes were placed on ice for anesthetization prior to injection using Drummond Nanoject II. For injection of exogenous neuromodulators, the anteriormost ocelli of D. mojavensis baja were injected with 18.4nl of 20mg/mL of Octopamine (Sigma-Aldrich, Catalog # O0250). For each round of injections, new OA is solubilized using Schneider’s Drosophila Medium with L-Glutamine (Genesee Scientific, Catalog # 25-515). Following each individual injection, flies are returned back into individual borosilicate tubes, and placed in respective DAM5M Activity Monitors to continue sleep and activity surveillance for >48h. Neurochemical Quantifications Sample preparation protocol Fly brain samples were stored at -80°C then treated with 99.9/1 Water/Formic Acid. An internal standard (IS) of each targeted compound was added to every sample to account for compound loss during sample processing. The samples are vortexed, homogenized for 30 sec in a bead beater using 2.0 mm zirconia beads, and centrifuged at 16.000xg for 5 min. The supernatant is transferred to new microcentrifuge test tubes and dried in a vacuum concentrator. The samples are reconstituted in 40 µl of water, vortexed, and centrifuged. The supernatant is transferred to HPLC vials and 10 µl is injected to an HPLC - triple quadrupole mass spectrometer system for analysis. Liquid Chromatography-Tandem Mass Spectrometry LC-MS A targeted LC-MS/MS assay was developed for each compound using the multiple reaction monitoring (MRM) acquisition method on a triple quadrupole mass spectrometer (6460, Agilent Technologies) coupled to an HPLC system (1290 Infinity, Agilent Technologies) with an analytical reversed phase column (GL Sciences, Phenyl 2 µm 150 x 2.1 mm UP). The HPLC method utilized a mobile phase constituted of solvent A (100/0.1, v/v, Water/Formic Acid) and solvent B (100/0.1, v/v, Acetonitrile/Formic Acid) and a gradient was used for the elution of the compounds (min/%B: 0/0, 10/0, 25/75, 27/0, 35/0). The mass spectrometer was operated in positive ion mode and fragment ions originating from each compound was monitored at specific LC retention times to ensure specificity and accurate quantification in the complex biological samples (Octopamine OA 159-136, Histamine HA 112-95, Dopamine DA 154-137, Serotonin 5HT 177-160). The standard curve was made by plotting the known concentration for each analyte of interest (CDN Isotopes) against the ratio of measured chromatographic peak areas corresponding to the analyte over that of the labeled standards. The trendline equation was then used to calculate the absolute concentrations of each compound in fly brain tissue. QUANTIFICATION AND STATISTICAL ANALYSIS Statistical Analysis Statistical tests were completed as described in the figure legends using Prism 9 (GraphPad Software, Boston MA, USA). Statistical comparisons primarily consist of one- or two-way ANOVAs followed by pairwise Holm-Sidak’s multiple comparisons test when experiments include at least three experimental groups or two-tailed Student’s T-test for experiments that include two groups; specific tests used are described in each figure legend. All data figures pool individual data points from at least two independent replicates.

According to a survey from 2024, around eight percent of college students in the United States had extremely difficulty falling asleep for seven of the last seven days. This statistic shows the percentage of college students in the U.S. who had an extremely hard time falling asleep within the past seven days as of fall 2024.

This statistic shows the share of respondents in Singapore who indicated sleeping the following amount of hours daily on average as of November 2018. During the period surveyed, ** percent of survey respondents indicated that they get an average of seven to eight hours of sleep a day. In comparison, ** percent indicated sleeping on average six hours or less daily.

In each sheet, the data are referenced to the respective sections of the figure panels (e.g., left, right). (XLSX)