Gene Expression Changes of Murine Cortex Homeostasis in Response to Sleep Deprivation Hint Dysregulated Aging-like Transcriptional Responses.
Panagiotis GiannosKonstantinos ProkopidisScott C ForbesKamil CelochDarren G CandowJaime L TartarPublished in: Brain sciences (2022)
Sleep deprivation leads to the deterioration in the physiological functioning of the brain, cognitive decline, and many neurodegenerative diseases, all of which progress with advancing age. Sleep insufficiency and impairments in cognitive function are characterized by progressive neuronal losses in the cerebral cortex. In this study, we analyze gene expression profiles following sleep-deprived murine models and circadian matched controls to identify genes that might underlie cortical homeostasis in response to sleep deprivation. Screening of the literature resulted in three murine ( Mus musculus ) gene expression datasets (GSE6514, GSE78215, and GSE33491) that included cortical tissue biopsies from mice that are sleep deprived for 6 h ( n = 15) and from circadian controls that are left undisturbed ( n = 15). Cortical differentially expressed genes are used to construct a network of encoded proteins that are ranked based on their interactome according to 11 topological algorithms. The analysis revealed three genes-NFKBIA, EZR, and SGK1-which exhibited the highest multi-algorithmic topological significance. These genes are strong markers of increased brain inflammation, cytoskeletal aberrations, and glucocorticoid resistance, changes that imply aging-like transcriptional responses during sleep deprivation in the murine cortex. Their potential role as candidate markers of local homeostatic response to sleep loss in the murine cortex warrants further experimental validation.
Keyphrases
- gene expression
- sleep quality
- physical activity
- cognitive decline
- genome wide
- dna methylation
- genome wide identification
- mild cognitive impairment
- machine learning
- systematic review
- oxidative stress
- multiple sclerosis
- transcription factor
- depressive symptoms
- type diabetes
- white matter
- adipose tissue
- metabolic syndrome
- deep learning
- subarachnoid hemorrhage
- genome wide analysis
- heat stress