Abstract
The power of the application of bioinformatics across multiple publicly available transcriptomic data sets was explored. Using 19 human and mouse circadian transcriptomic data sets, we found that NR1D1 and NR1D2 which encode heme‐responsive nuclear receptors are the most rhythmic transcripts across sleep conditions and tissues suggesting that they are at the core of circadian rhythm generation. Analyzes of human transcriptomic data show that a core set of transcripts related to processes including immune function, glucocorticoid signalling, and lipid metabolism is rhythmically expressed independently of the sleep‐wake cycle. We also identify key transcripts associated with transcription and translation that are disrupted by sleep manipulations, and through network analysis identify putative mechanisms underlying the adverse health outcomes associated with sleep disruption, such as diabetes and cancer. Comparative bioinformatics applied to existing and future data sets will be a powerful tool for the identification of core circadian‐ and sleep‐dependent molecules.
Highlights
The advent of high-resolution high-throughput technologies has enabled the simultaneous measurement of mRNA levels for all genes within a mammalian system under particular conditions
Under baseline conditions of sleeping in phase with the melatonin rhythm, 1,396 genes were characterized as having a circadian expression profile (Fig. 1A, Supplemental Table 1). These included the majority of the core circadian clock genes (Table 1)
In this paper we report a comparative analysis of circadian genes identified from two human gene expression datasets that comprise four conditions: 40 hours of total sleep deprivation (TSD) following sufficient sleep (8.5 hours per night), and 40 hours of TSD
Summary
The advent of high-resolution high-throughput technologies has enabled the simultaneous measurement of mRNA levels for all genes within a mammalian system under particular conditions. As each research group performs the most suitable experiment to test their own hypotheses, there are large differences in protocols (perturbations), the type of sample taken (tissue, blood etc.), the resolution of sampling times, the numbers of subjects, technological platforms, and the analytical algorithms used to identify rhythmic signals. This variability across experiments can be a significant obstacle in the comparative analysis of gene specific responses across conditions and/or mammalian systems.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
