Exploiting human and mouse transcriptomic data: Identification of circadian genes and pathways influencing health. (14th March 2015)

• Record Type: Journal Article
• Authors: Laing, Emma E.; Johnston, Jonathan D.; Möller‐Levet, Carla S.; Bucca, Giselda; Smith, Colin P.; Dijk, Derk‐Jan; Archer, Simon N.
• Is Part Of: BioEssays. Volume 37:Number 5(2015:May)
• Page Start: 544
• Page End: 556
• Publication Date: 2015-03-14
• Subjects: Molecular biology -- Periodicals; Cytology -- Periodicals; Developmental biology -- Periodicals; 572.8
• DOI: 10.1002/bies.201400193
• Languages: English
• ISSNs: 0265-9247

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="bies201400193-sec-0001" sec-type="section"> <p>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 <italic>NR1D1</italic> and <italic>NR1D2</italic> 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.</p> </sec> </abstract>