DOI: 10.5061/dryad.pzgmsbcz0
title: Exploratory analysis of sleep deprivation effects on gene expression and regional brain metabolism

description: 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&lt;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&lt;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.
description: 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.
description: # Exploratory Analysis of Sleep Deprivation Effects on Gene Expression and
 Regional Brain Metabolism ## Description of the data and file structure
 This data was collected in order to study relationships between gene
 expression and regional brain metabolism in the context of sleep
 deprivation and sex.  `ROI_faw` and `applied_subset_40000` are the data
 collected from subjects, after preprocessing was performed. **Applied
 subset 40000** columns appear as: * ProbeID from applied subset 40000 are
 the probe names/identifiers from the affymetrix microarray chip,
 designated by the manufacturer. RNA microarray chips measure fluorescence
 intensity (RFU). The raw intensity RFU values for each probe (ProbeID)
 were background-corrected, normalized, and log-transformed to a log2 scale
 to populate this table. * P01- P08 denotes patient, and pre post condition
 (before or after sleep deprivation) (e.g: P01_post represents the patient
 1, post sleep deprivation) This data set is used by the `applied_subset
 and genes_degradation_residuals` function in the script funtions.r **PET
 SEGMENTATION** columns: PET SEGMENTATION is needed to run
 get_top8_component_pcaplot and get_top8_cum_var functions from
 function.R.  BrainRegion specifies name of the brain region (ROI).
 Subesequent columns identify subjects with 4 letter arbitruary acronym
 (created during data acquisition to help maintain double blinding), and a
 number (patient number, same as Applied subset 40000 and ROIfaw). Pre =
 pre sleep deprivation, SD = post sleep deprivation. Values represent
 metabolic value for specified brain region, and is unitless.  **ROIfaw**
 is in long format with columns: MetVal (arbituary units) is the normalized
 metabolic value extracted from the BrainRegion (brain region) for that
 patient (Patient_ID) under the pre or post condition (Conditon; Pre = pre
 sleep deprivation, post = post sleep deprivation). Gender for each patient
 was also recorded (F=female, M=male). **df for corr matrix** has columns
 that appear as: There are many more columns, PC denotes principal
 component, and the number denotes which principal component. \_gene or
 \_ROI denotes which principal component analysis the PC came from, as gene
 and brain imaging data were analyzed with PCA separately. Each subject’s
 PC score is a weighted combination of their original measurements (either
 gene expression levels or brain ROI values). This score reflects where the
 subject falls along a latent dimension or principal component. A higher or
 lower PC score indicates how strongly a subject expresses the underlying
 pattern defined by that principal component. Because the data was
 normalized by z-score during principal component analysis, the PC values
 are unitless.  The rest of the columns are brain areas, and the values
 represent metabolic values from that brain region of interest (ROI), for
 that subject under the condition (specified by condition). affyrnadeg
 denotes the RNA degradation slope value, used to evaluate and control for
 the sample quality. Condition (1= pre sleep deprivation, 2= post sleep
 deprivation) and Sex (1=male, 2=female) are categorical, age is recorded
 in years, SSS is the Stanford Sleepiness Score, and PVT is the psychomotor
 vigilance test, and is the subject's response delay to a stimulus,
 measured in milliseconds.  data from corr matrix is used to create the
 dataframe for: calculate_and_save_correlation &lt;- function(df,
 output_filename) This function is from the function.R script ###
 **Supplementary Tables for the associated manuscript:**
 **1_functional_clustering.xlsx** - functional clustering of gene
 expression principal component analysis and ANOVA using DAVID
 **2_Gene_anova_BH_FDR.csv** - two-way mixed ANOVA on gene expression, with
 Benjamini-Hochberg FDR **3_significant_gene_anova.xlsx** - post FDR
 significant genes from the gene ANOVA (supplementary
 table 2_Gene_anova_BH_FDR.csv) **4_ubl_conj_genes_function-mpv.xlsx** -
 the genes and functions for the UBL conjugation functional cluster, see
 first supplementary table. Also includes ANOVA for genes in this
 functional cluster **5_top_300_genes.xlsx** - the top 300 genes that have
 the strongest loading values for each gene principal component, also has
 the top 300 most significant genes from the gene ANOVA
 **6_ROI_ANOVA_FULL.xlsx** - Two Way Mixed ANOVA Brain Region Metabolism
 (all regions) **7_allROI_PCA_loadings.docx -** PCA Feature Loadings for
 Brain Regions **8_correlation_matrix_ROI_genePCA.xlsx -** correlation
 matrix with all the brain region ROI, and gene PC's
 **9_correlation_allROI_300cond.xlsx -** correlation matrix with all the
 brain region ROI, and top 300 genes from condition ANOVA 
 **10_laterality_anova_ROI.xlsx** - ANOVA for hemisphere effects and
 interaction with gender and on brain region **functions.r** is a
 collection of scripts used to generate the statistical tests for the
 associated research paper. These include 2 way mixed ANOVAs, correlation
 matrixes, and principal component analysis.  ## Code/software R libraries:
 ggplot2 car psych dplyr tidyr Hmisc lme4 afex lsr lmerTest readr dbscan
 glmnet biomaRt qvalue hgu133plus2.db AnnotationDbi purrr **Consent to
 participate statement: Informed consent was obtained from all participants
 before the start of this study**