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.

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.