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Genome Biology

, 7:R48

First Online: 19 June 2006Received: 11 January 2006Revised: 05 April 2006Accepted: 04 May 2006


BackgroundIdentifying the gene regulatory networks governing physiological signal integration remains an important challenge in circadian biology. Epidermal growth factor receptor EGFR has been implicated in circadian function and is expressed in the suprachiasmatic nuclei SCN, the core circadian pacemaker. The transcription networks downstream of EGFR in the SCN are unknown but, by analogy to other SCN inputs, we expect the response to EGFR activation to depend on circadian timing.

ResultsWe have undertaken a systems-level analysis of EGFR circadian time-dependent signaling in the SCN. We collected gene-expression profiles to study how the SCN response to EGFR activation depends on circadian timing. Mixed-model analysis of variance ANOVA was employed to identify genes with circadian time-dependent EGFR regulation. The expression data were integrated with transcription-factor binding predictions through gene group enrichment analyses to generate robust hypotheses about transcription-factors responsible for the circadian phase-dependent EGFR responses.

ConclusionThe analysis results suggest that the transcriptional response to EGFR signaling in the SCN may be partly mediated by established transcription-factors regulated via EGFR transription-factors AP1, Ets1, C-EBP, transcription-factors involved in circadian clock entrainment CREB, and by core clock transcription-factors Rorα. Quantitative real-time PCR measurements of several transcription-factor expression levels support a model in which circadian time-dependent EGFR responses are partly achieved by circadian regulation of upstream signaling components. Our study suggests an important role for EGFR signaling in SCN function and provides an example for gaining physiological insights through systems-level analysis.

Electronic supplementary materialThe online version of this article doi:10.1186-gb-2006-7-6-r48 contains supplementary material, which is available to authorized users.

Daniel E Zak, Haiping Hao contributed equally to this work.

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Autor: Daniel E Zak - Haiping Hao - Rajanikanth Vadigepalli - Gregory M Miller - Babatunde A Ogunnaike - James S Schwaber


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