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Hepatic Clocks

  • Ueli SchiblerEmail author
  • Gad Asher
  • Camille Saini
  • Jörg Morf
  • Hans Reinke
Chapter

Abstract

Most physiological processes are subject to daily ­oscillations that are driven by an endogenous circadian clock. These include rest–activity cycles, cardiovascular functions such as heart rate and blood pressure, the production and secretion of hormones, renal plasma flow and urine production, in addition to metabolic functions of organs associated with the gastrointestinal tract (for review and references, see [1–4]). Since most metabolic functions oscillate in a daily manner, the liver is an organ for which circadian timing is particularly obvious. Thus, genome-wide transcriptome profiling studies have revealed that depending on the stringency of algorithms used for the extraction of oscillating transcripts between 2 and 10% of all liver mRNAs accumulate in a rhythmic fashion [5–10]. The majority of these transcripts encode enzymes and regulators involved in the metabolism of fatty acids, cholesterol, bile acids, carbohydrates, and xenobiotics. Several signaling pathways relevant for hepatic clock outputs (e.g., signaling through PPARs, CAR, LXR, and FXR) are elaborated in previous chapters of this issue. In this chapter, we shall thus focus on putative signaling pathways related to input pathways into the liver clock. Specifically, we will discuss current views and hypothesis on how the master pacemaker in the brain’s suprachiasmatic nucleus (SCN) synchronizes peripheral clocks, in particular those operative in liver. We will also present some findings made with cultured fibroblasts, since these cells have served as a model system in most in vitro studies. Some of the signaling routes outlined below remain speculative, and their detailed analysis requires additional investigations.

Keywords

Bile Acid Circadian Clock Serum Response Factor Circadian Oscillator Peripheral Clock 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ueli Schibler
    • 1
    Email author
  • Gad Asher
    • 1
  • Camille Saini
    • 1
  • Jörg Morf
    • 1
  • Hans Reinke
    • 1
  1. 1.Department of Molecular Biology and National Center of Competence in Research “Frontiers in Genetics”, Sciences IIIUniversity of GenevaGeneva-4Switzerland

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