Molecular regulations of circadian rhythm and implications for physiology and diseases

Abstract

The term "circadian rhythms" describes endogenous oscillations with ca. 24-h period associated with the earth's daily rotation and light/dark cycle. Such rhythms reflect the existence of an intrinsic circadian clock that temporally orchestrates physiological processes to adapt the internal environment with the external cues. At the molecular level, the circadian clock consists of multiple sets of transcription factors resulting in autoregulatory transcription-translation feedback loops. Notably, in addition to their primary role as generator of circadian rhythm, the biological clock plays a key role in controlling physiological functions of almost all tissues and organs. It regulates several intracellular signaling pathways, ranging from cell proliferation, DNA damage repair and response, angiogenesis, metabolic and redox homeostasis, to inflammatory and immune response. In this review, we summarize findings showing the crosstalk between the circadian molecular clock and some key intracellular pathways, describing a scenario wherein their reciprocal regulation impinges upon several aspects of mammalian physiology. Moreover, based on evidence indicating that circadian rhythms can be challenged by environmental factors, social behaviors, as well as pre-existing pathological conditions, we discuss implications of circadian misalignment in human pathologies, such as cancer and inflammatory diseases. Accordingly, disruption of circadian rhythm has been reported to affect several physiological processes that are relevant to human diseases. Expanding our understanding of this field represents an intriguing and transversal medicine challenge in order to establish a circadian precision medicine.

Fig. 1

Molecular interaction between the circadian core clock and cell cycle components. The CLOCK:BMAL1 complex transcriptionally activates genes containing E-box regulatory elements in their regulatory regions, such as clock genes and cell-cycle genes. A CLOCK:BMAL1 complex directly controls the transcription of the cell-cycle-related gene Wee-1 contains three B-boxes in its promoter and encodes a protein kinase that inactivates the CDC2/Cyclin B1 complex, thus regulating G2-M transition and cell-cycle progression. B Transcriptional activation of the genes encoding Cyclin D1 and C-MYC by CLOCK:BMAL1 affects cell proliferation and differentiation. C PER1 can complex with the ATM kinase and the checkpoint kinase Chk2, thus impinging upon DNA repair, cell cycle arrest and/or apoptosis. D Both physiological and stress-induced p53 binds to p53 response element in PER2 promoter, which overlaps with the BMAL1/CLOCK-binding site, thereby inhibiting CLOCK:BMAL1-mediated transcription of PER2. (BioRender.com has been used to create the figure)

Fig. 2

Circadian control of basal and inducible expression of inflammatory mediators in immune cells. a Circadian control of basal gene expression. Interaction between PRC2 and the CLOCK:BMAL1 complex rhythmically represses the expression of chemokine genes, such as Ccl2. b Circadian control of inducible gene expression. CLOCK acetylates the p65 subunit of NF-κB, thereby inducing the expression of TNFα. Moreover, recruitment of REV-ERB repressor complexes to inflammatory genes, such as Il6, rhythmically suppresses their expression. Finally, the cellular clock is fundamental for recruitment of GR complexes to the glucocorticoid binding site (GBS) on Cxcl5 to repress its transcription. (BioRender.com has been used to create the figure)

Fig. 3

Protein–protein interaction (PPI) network between the core clock components and clock-related proteins. STRING Protein–Protein Interaction database (Ver 11.5) has been used to build the PPI network. The network contains 48 nodes and the edges represent the crosstalk between the core clock components and proteins belonging to other key intracellular pathways. Line thickness reflects the strength of data support for protein-protein interaction that derives from databases, experiments, or based on computational predictions. The network is clustered based on a specified “MCL inflation parameter” and on a customized clustering coefficient of 0.300. The different biological processes investigated are shown in the network according to the color legend