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Exploiting human and mouse transcriptomic data: Identification of circadian genes and pathways influencing health

Laing, EE, Johnston, JD, Möller-Levet, CS, Bucca, G, Smith, CP, Dijk, D-J and Archer, SN (2015) Exploiting human and mouse transcriptomic data: Identification of circadian genes and pathways influencing health BioEssays, 37 (5). pp. 544-556.

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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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine > Department of Biochemical Sciences
Authors :
AuthorsEmailORCID
Laing, EEUNSPECIFIEDUNSPECIFIED
Johnston, JDUNSPECIFIEDUNSPECIFIED
Möller-Levet, CSUNSPECIFIEDUNSPECIFIED
Bucca, GUNSPECIFIEDUNSPECIFIED
Smith, CPUNSPECIFIEDUNSPECIFIED
Dijk, D-JUNSPECIFIEDUNSPECIFIED
Archer, SNUNSPECIFIEDUNSPECIFIED
Date : 1 May 2015
Identification Number : 10.1002/bies.201400193
Additional Information : © 2015 The Authors. Bioessays published by WILEY Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Depositing User : Symplectic Elements
Date Deposited : 10 Jun 2015 16:50
Last Modified : 10 Jun 2015 16:50
URI: http://epubs.surrey.ac.uk/id/eprint/807652

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