(313b) Modeling Circadian Interactions Between Peripheral Clocks and Metabolism
The circadian system or "biological clock" orchestrates the temporal organization of several essential physiological processes. This system enables the organism to adapt to common daily changes, such as day-night cycle and food availability. In mammals, the central pacemaker resides within the suprachiasmatic nucleus (SCN), which receives entraining signals from the environment and coordinates the oscillating activity of peripheral clocks that are present in almost all tissues. Circadian oscillators share the same molecular make up in SCN neurons and peripheral cell types. The predominant external cue of the central clock is light which, at the level of SCN neurons, stimulates a cascade of signaling pathways that lead to the activation of a transcriptional program. Peripheral tissues also contain functional circadian oscillators that are self-sustained at the single cell level, but they do not respond to light-dark cycles. Instead, cycling feeding behavior is a strong timing cue for peripheral clocks which are important for driving local rhythmic activity, probably through modifications of nutrient availability. While many metabolic functions are subjected to circadian variation metabolic signals also feedback into the circadian system (Green, Takahashi et al. 2008). Such observation reflects the now recognized relationship between circadian transcription and metabolic regulatory networks.
Disruption of circadian rhythms has a profound impact on several pathologies including mental, behavioral and metabolic disorders (Bass and Takahashi). Linking circadian misalignment to physiological behavior requires a systems-level understanding of the hierarchical organization of the biological clock. A vital enabler in that respect is the development of in silico methodologies that integrate circadian regulatory processes at various scales (brain, periphery). Prior modeling efforts from our group have focused on the development of a cell autonomous mathematical model of a circadian neuron (Mirsky, Liu et al. 2009). Due to the critical role peripheral clocks play in health and disease, the work to be discussed here extends such an integrative approach in assessing the impact of circadian dysregulation in peripheral tissue pathophysiology. Using quantitative approaches to reverse-engineer the mechanisms underlying the generation of circadian rhythms, we expand the envelope of circadian interactions by quantifying the regulation of cell autonomous circadian rhythms by metabolic sensors.
Of particular relevance to our study are a few recent groundbreaking studies (Asher, Gatfield et al. 2008; Nakahata, Kaluzova et al. 2008) showing that the NAD+-dependent enzyme SIRT1 functions as a histone deacetylase that counteracts the histone acetyltransferase activity (HAT) of CLOCK. Both studies show that the histone deacetylase SIRT1 is involved in regulating the amplitude of circadian clock-controlled gene expression. Further studies (Nakahata, Sahar et al. 2009; Ramsey, Yoshino et al. 2009) went on by demonstrating that, in turn, the clock regulates the expression of nicotinamide phosphoribosyltransferase the rate limiting enzyme in the NAD+ salvage pathway, as it catalyzes the first step in the biosynthesis of NAD+. These findings imply that not only the clock modulates metabolism but also the other way around, the metabolic status of the cell influences the clock machinery; thus generating an interlocking of the transcriptional feedback clock loop with the enzymatic loop of the NAD+-salvage pathway (Eckel-Mahan and Sassone-Corsi 2009). It is therefore the purpose of this research to quantify such reciprocal regulation of clock making it a critical enabler for assessing the impact of metabolic activity on the circadian clock machinery.
To meet this challenge, a cell autonomous model that quantifies the reciprocal regulation of clock by metabolic signaling has been developed. This work operates along the following three axes: (i) generation of circadian (self-sustained) oscillations governed by the canonical PER-CRY/CLOCK-BMAL1 feedback loop, (ii) circadian control of the enzymatic NAD+-salvage pathway and finally (iii) integration of the mammalian circadian clock with intracellular metabolism through the oscillating biosynthesis of NAD+. This new time-keeping loop is closed by feedback of SIRT1 on CLOCK-BMAL1 dependent transcription by counteracting CLOCK HAT activity, particularly inhibiting CLOCK-mediated BMAL1 acetylation. Since CLOCK-dependent BMAL1 acetylation potentiates its binding by PER-CRY repressor (Hirayama, Sahar et al. 2007), we explore the hypothesis that SIRT1 inhibits such interaction. Driven by the premise that SIRT1 mRNA accumulation does not display robust circadian rhythm (similar to the almost constant levels of CLOCK expression in most mammalian tissues), SIRT1 mRNA dynamics are not explicitly modeled. Instead, its enzymatic activity oscillates in phase with NAD+ levels and therefore the dynamics of NAD+ metabolite serve as a surrogate signal for SIRT1 deacetylase activity.
The performance of the proposed model is evaluated through its potential to capture experimentally observed cell-autonomous phenotypes of gene knockouts including circadian amplitude variation due to loss of metabolic activity (e.g. SIRT1-/-). Interestingly, the simulated increase in the amplitude of oscillations due to lack of SIRT1 is consistent with the experimental findings as reported by Nakahata et al. (Nakahata, Kaluzova et al. 2008). In another study (Asher, Gatfield et al. 2008), Asher et al. propose another mechanism by which SIRT1 activity might impinge on the circadian rhythm, possibly through SIRT1-mediated PER deacetylation. By considering that such deacetylation occurs while PER protein interacts with CLOCK-BMAL complex facilitating its degradation rate, the simulated circadian amplitude decreases (rather than increases) under SIRT1 knockout conditions. Such simulations capture the experimentally observed differential response of clock gene expression to loss of SIRT1. Further modeling results involve simulations related to circadian dysfunction under mutations of clock genes (e.g. BMAL1-/-, CRY-/-). In addition to arrhythmias, the model simulates lower constitutive levels of clock-controlled genes due to loss of the essential BMAL1 activator while greater expression levels are simulated for the CRY double knockout phenotype.
This work demonstrates the feasibility of a circadian transcriptional-enzymatic model that captures for the first time metabolic and physiological core components of the overall control of the mammalian circadian rhythm. Modeling the reciprocal regulation of clock by metabolic signaling provides the necessary ammunition to move forward with the proposed model assessing the influence of circadian rhythms on metabolic homeostasis. By integrating how metabolism impinges upon the circadian rhythm and vice versa, the ultimate goal is to explore mechanisms of synchronization between central and peripheral clocks both at the single cell and tissue level. Such in silico methodologies have the potential of elucidating putative mechanisms at various scales that can be functionally modulated for resetting the clock.
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