Article

What are microarrays teaching us about sleep?

University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Trends in Molecular Medicine (Impact Factor: 10.11). 02/2009; 15(2):79-87. DOI: 10.1016/j.molmed.2008.12.002
Source: PubMed

ABSTRACT Many fundamental questions about sleep remain unanswered. The presence of sleep across phyla suggests that it must serve a basic cellular and/or molecular function. Microarray studies, performed in several model systems, have identified classes of genes that are sleep-state regulated. This has led to the following concepts: first, a function of sleep is to maintain synaptic homeostasis; second, sleep is a stage of macromolecule biosynthesis; third, extending wakefulness leads to downregulation of several important metabolic pathways; and, fourth, extending wakefulness leads to endoplasmic reticulum stress. In human studies, microarrays are being applied to the identification of biomarkers for sleepiness and for the common debilitating condition of obstructive sleep apnea.

0 Followers
 · 
114 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The two-process model of sleep-wake regulation asserts a neurobiological drive for sleep that varies homeostatically (increasing as a saturating exponential during wakefulness and decreasing in a similar manner during sleep) and a circadian process that neurobiologically modulates the homeostatic drive for sleep and waking performance and alertness. Sleep deprivation increases homeostatic sleep drive and degrades waking neurobehavioral functions as reflected in fatigue, sleepiness, attention, memory, and cognitive speed. Notably, there are robust individual differences in neurobehavioral responses to sleep loss which are trait-like and phenotypic and not explained by baseline functioning or other possible predictors. This review discusses “omics” methodologies (transcriptomics, epigenomics, and metabolomics) in sleep and circadian rhythm research. Since the molecular mechanisms underlying differential vulnerability remain virtually unknown, such methodologies can be used to yield biomarkers for predicting individual differences in neurobehavioral responses to sleep loss in humans. Reliable prediction of who is more or less likely to experience neurobehavioral decrements from sleep loss would provide more targeted use of biological countermeasures and optimization of personnel in a variety of occupational settings.
    03/2015; 1(1). DOI:10.1007/s40675-014-0003-7
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: During their development, Caenorhabditis elegans larvae go through four developmental stages. At the end of each larval stage, nematodes molt. They synthesize a new cuticle and shed the old cuticle. During the molt, larvae display a sleep-like behavior that is called lethargus. We wanted to determine how gene expression changes during the C. elegans molting cycle. We performed transcriptional profiling of C. elegans by selecting larvae displaying either sleep-like behavior during the molt or wake behavior during the intermolt to identify genes that oscillate with the molting-cycle. We found that expression changed during the molt and we identified 520 genes that oscillated with the molting cycle. 138 of these genes were not previously reported to oscillate. The majority of genes that had oscillating expression levels appear to be involved in molting, indicating that the majority of transcriptional changes serve to resynthesize the cuticle. Identification of genes that control sleep-like behavior during lethargus is difficult but may be possible by looking at genes that are expressed in neurons. 22 of the oscillating genes were expressed in neurons. One of these genes, the dopamine transporter gene dat-1, was previously shown in mammals and in C. elegans to control sleep. Taken together, we provide a dataset of genes that oscillate with the molting and sleep-wake cycle, which will be useful to investigate molting and possibly also sleep-like behavior during lethargus.
    PLoS ONE 11/2014; 9(11):e113269. DOI:10.1371/journal.pone.0113269 · 3.53 Impact Factor
  • Source
    SpringerPlus 01/2014; 3:728. DOI:10.1186/2193-1801-3-728

Full-text (2 Sources)

Download
17 Downloads
Available from
Jul 9, 2014

John E. Zimmerman