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      The CRTC1-SIK1 Pathway Regulates Entrainment of the Circadian Clock

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Retinal photoreceptors entrain the circadian system to the solar day. This photic resetting involves cAMP response element binding protein (CREB)-mediated upregulation of Per genes within individual cells of the suprachiasmatic nuclei (SCN). Our detailed understanding of this pathway is poor, and it remains unclear why entrainment to a new time zone takes several days. By analyzing the light-regulated transcriptome of the SCN, we have identified a key role for salt inducible kinase 1 (SIK1) and CREB-regulated transcription coactivator 1 (CRTC1) in clock re-setting. An entrainment stimulus causes CRTC1 to coactivate CREB, inducing the expression of Per1 and Sik1. SIK1 then inhibits further shifts of the clock by phosphorylation and deactivation of CRTC1. Knockdown of Sik1 within the SCN results in increased behavioral phase shifts and rapid re-entrainment following experimental jet lag. Thus SIK1 provides negative feedback, acting to suppress the effects of light on the clock. This pathway provides a potential target for the regulation of circadian rhythms.

          Graphical Abstract

          Highlights

          • Nocturnal light induces widespread transcriptional changes in the SCN
          • The CRTC1-SIK1 cascade regulates entrainment of the circadian clock
          • Negative feedback by SIK1 limits the effects of light on the clock
          • Homeostatic regulation of entrainment ensures gradual adaptation to a new time zone

          Abstract

          A negative-feedback loop involving the kinase SIK1 and the transcriptional coactivator CRTC1 delays re-entrainment of the circadian clock to new time zones, causing jet lag. Remarkably, inhibition of SIK1 allows rapid re-entrainment after an experimental jet lag protocol in mice.

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          Most cited references 73

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes

              The TRANSFAC® database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel® on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC®. The list of databases which are linked to the common GENE table of TRANSFAC® and TRANSCompel® has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel® contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC®, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC® 7.0 and TRANSCompel® 7.0, are accessible under .
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                29 August 2013
                29 August 2013
                : 154
                : 5
                : 1100-1111
                Affiliations
                [1 ]Nuffield Department of Clinical Neurosciences (Nuffield Laboratory of Ophthalmology), University of Oxford, Levels 5-6 West Wing, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
                [2 ]Department of Pharmacology, University of Oxford, Mansfield Road, Oxford OX1 3QT, UK
                [3 ]Department of Biological Sciences, University of Notre Dame, Galvin Life Sciences Center, Notre Dame, IN 46556, USA
                [4 ]Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
                [5 ]pRED Pharma Research and Development F. Hoffmann-La Roche, 4070 Basel, Switzerland
                [6 ]Axolabs GmbH Fritz-Hornschuch-Straße 9, 95326 Kulmbach, Germany
                Author notes
                []Corresponding author russell.foster@ 123456eye.ox.ac.uk
                [∗∗ ]Corresponding author stuart.peirson@ 123456eye.ox.ac.uk
                [7]

                These authors contributed equally to this work

                Article
                S0092-8674(13)00961-6
                10.1016/j.cell.2013.08.004
                3898689
                23993098
                © 2013 ELL & Excerpta Medica.

                This document may be redistributed and reused, subject to certain conditions.

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                Cell biology

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