(468a) The Influence of Circadian Rhythms On the Inflammatory Response

The inflammatory response is a key component in the defense against a variety of harmful stimuli, such as an invading pathogen or trauma. Inflammation, consisting of a complex, coordinated set of interactions between the immune system and the neuroendocrine system, initiates the restoration of homeostasis either through the removal of the pathogen or the repair of damaged tissue. Typically, inflammation is tightly regulated, activating when necessary and returning to homeostasis after healing has been initiated. However, inflammation does not always resolve appropriately; in some cases, an elevated level of inflammation persists, which can damage otherwise healthy tissue. Prolonged systemic inflammation comes with severe consequences, often leading to organ failure and death. This type of overwhelming inflammatory when accompanied by an infection is called sepsis. There are approximately 750,000 cases of severe sepsis every year in the United States alone, leading to over 200,000 deaths annually (Angus, Linde-Zwirble et al. 2001). Thus, the management of inflammation is a major challenge in the treatment of critically ill patients.

Despite our understanding of the importance of this problem and extensive research towards the development of effective therapies, current treatment options are not adequate and the development of new treatments has been slow (Freeman and Natanson 2000). This may be due to the inherent challenges in applying reductionist techniques to complex, nonlinear systems (Seely and Christou 2000). It may not be possible to predict the outcome of perturbing a pathway involved in inflammation if knowledge of its behavior comes only from its study in isolation (Vodovotz, Clermont et al. 2004). This has stimulated interest in the development of quantitative, systems biology models of inflammation.

In recent years, a number of models have been developed by applying different modeling techniques (agent based modeling or equation based modeling), at different scales (molecular, cellular, systemic, or a combination), and focusing on different specific problems (acute inflammation, trauma, or the response to a specific disease) (Vodovotz, Constantine et al. 2009). These models have been developed with the practical goals of impacting healthcare through translational systems biology and rationalizing the design of experiments and clinical trials (Clermont, Bartels et al. 2004). Because of the large number of components involved in inflammation, existing models make assumptions about which interactions are most important, either by simplifying or neglecting certain elements. One aspect that has not previously been studied from the perspective of systems biology is the interplay between circadian rhythms and inflammation.

Circadian rhythms are biochemical, behavioral, or physiological processes that are entrained to a 24 hour periodic cycle. This rhythmicity is widely observed in humans from the scale of biochemical reactions, such as hormone production, to behavioral patterns, such as regular sleeping and feeding times. In the context of healthcare, mouse and rat models have shown that the same dose of a drug can be lethal at certain times and ineffective at others (Levi and Schibler 2007). Thus, it is not surprising that there is also a circadian component to inflammation; in fact, many of the elements typically included in models of inflammation (leukocytes, cytokines, and hormones) are known to have strong diurnal patterns (Coogan and Wyse 2008). The importance of these variations is apparent by observing that sepsis patients have a heightened risk of mortality between 2am and 6am (Hrushesky, Langevin et al. 1994).

Many of the components included in previous attempts at modeling inflammation have been shown to exhibit circadian patterns. Several studies have shown that numerous pro- and anti-inflammatory cytokines undergo diurnal variations in plasma levels, typically peaking in the night (Zabel, Horst et al. 1990; Petrovsky and Harrison 1997; Petrovsky and Harrison 1998; Petrovsky, McNair et al. 1998; Hermann, von Aulock et al. 2006). Plasma cortisol levels also exhibit a circadian pattern, peaking in the early morning. Cortisol is produced by the actions of the hypothalamic-pituitary-adrenal axis, and the circadian production is due to stimulation from the central circadian clock in the suprachiasmatic nucleus (SCN).

Due to the immunomodulatory effects of glucocorticoids and the strong circadian pattern of plasma cortisol levels, cortisol has been implicated in the circadian entrainment of cytokine production (Petrovsky and Harrison 1998). However, glucocorticoids to not affect all cytokines equally; it stimulates the production of anti-inflammatory cytokines while inhibiting the production of pro-inflammatory cytokines (Barber, Coyle et al. 1993; Barnes 1998). Thus, since most cytokines have peak levels around the same time, it seems unlikely that cortisol alone could be responsible for the observed fluctuations in cytokine level, especially in light of the fact that a number of other hormones also vary either in or out of phase with cytokine levels (Petrovsky and Harrison 1998).

One of these hormones that has received particular attention is melatonin, which is important due to its potential role as a mediator in the crosstalk between the SCN and the immune system (Coogan and Wyse 2008). Melatonin is tightly regulated to have a peak in production in the night while remaining at very low levels the rest of the day and it has been shown to stimulate the production of cytokines (Guerrero and Reiter 2002; Skwarlo-Sonta, Majewski et al. 2003). This is supported by experimental evidence showing that pinealectomy leads to decreased cytokine production in mice (Delgobbo, Libri et al. 1989). Thus melatonin is used as the primary circadian regulator of cytokine production in this model. Melatonin and cortisol drive the circadian variation in all of the other model variables. These interactions are used to develop a model that includes the normal diurnal variations in model components as well as time-of-day-dependent responses to inflammatory stimuli. This work has applications in translational systems biology due to the importance of considering circadian rhythms when treating inflammatory diseases (Hrushesky and Wood 1997).

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