Modeling the influence of circadian rhythms on the acute inflammatory response

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Abstract

A wide variety of modeling techniques have been applied towards understanding inflammation. These models have broad potential applications, from optimizing clinical trials to improving clinical care. Models have been developed to study specific systems and diseases, but the effect of circadian rhythms on the inflammatory response has not been modeled. Circadian rhythms are normal biological variations obeying the 24-h light/dark cycle and have been shown to play a critical role in the treatment and progression of many diseases. Several of the key components of the inflammatory response, including cytokines and hormones, have been observed to undergo significant diurnal variations in plasma concentration. It is hypothesized that these diurnal rhythms are entrained by the cyclic production of the hormones cortisol and melatonin, as stimulated by the central clock in the suprachiasmatic nucleus. Based on this hypothesis, a mathematical model of the interplay between inflammation and circadian rhythms is developed. The model is validated by its ability to reproduce diverse sets of experimental data and clinical observations concerning the temporal sensitivity of the inflammatory response.

Introduction

The acute inflammatory response is a critical component of the body's defense against a variety of harmful stimuli, such as an invading pathogen or trauma. Inflammation consists of a complex, coordinated set of interactions between the immune system and the neuroendocrine system to initiate 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 abating after healing has been initiated. However, inflammation does not always resolve appropriately; in some cases, a heightened level of inflammation persists, which can damage 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 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 (Annane et al., 2002, Bernard et al., 2001) remain limited and other novel therapies remain elusive (Freeman and Natanson, 2000). This is likely due to the inherent challenges in applying reductionist techniques to non-linear systems (Seely and Christou, 2000). In fact, it may be impossible to predict the outcome of perturbing a pathway involved in inflammation given only a knowledge of its isolated behavior (Vodovotz et al., 2004). For this reason, there is interest in applying techniques from systems biology towards the development of models of inflammation, with the goal of attaining a systems-level understanding of the key interactions in the inflammatory response.

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) (An, 2008, Foteinou et al., 2009c, Jit et al., 2005, Kumar et al., 2008, Li et al., 2008, Lipniacki et al., 2006, Mi et al., 2007, Prince et al., 2006, Zuev et al., 2006). These models have been developed with the practical goals of impacting healthcare through translational systems biology (Foteinou et al., 2009d, Vodovotz et al., 2008) and rationalizing the design of experiments and clinical trials (Clermont 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 periodic processes that are synchronized to the 24 h light/dark 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 2 am and 6 am (Hrushesky et al., 1994).

This paper presents a mathematical model of the interplay between circadian rhythms in inflammation that synthesizes disparate biological knowledge about these systems. Circadian variability is introduced into our previous multiscale model of inflammation (Foteinou et al. in press) under the hypothesis that the observed circadian variations in the inflammatory response are governed by the hormones cortisol and melatonin and their interactions with immune cells. The model is validated by its ability to reproduce experimental results from a variety of sources and its qualitatively accurate predictions of diurnal variability in the strength of the inflammatory response.

Section snippets

Modeling inflammation

In vivo human endotoxin challenge is a commonly used model for studying acute inflammation because it evokes signs and symptoms of systemic inflammation along with significant transcriptional and neuroendocrine responses (Lowry, 2005). Lipopolysaccharides (LPS, endotoxin), found in the outer membrane of gram-negative bacteria are pathogen-associated molecular patterns (PAMPs) that are recognized by innate immune system pattern recognition receptors (PRRs), most notably Toll-like receptor 4

Results

Eqs. (2a), (2b), (3), (4a), (4b), (5a), combined with the remaining unmodified equations from Eq. (1), comprise a model of human endotoxemia that takes into account circadian variations in most of its variables. A network diagram of these interactions is shown in Fig. 1. The model consists of several interacting modules representing various different scales; at the cellular level, the three essential transcriptional responses (pro-inflammatory P, anti-inflammatory A, and energetic E) are

Discussion

Circadian rhythms are of critical importance in inflammation because so many of the biological components that regulate the outcome of inflammation are themselves under circadian regulation. This work presents the first model that incorporates the effect of circadian variability on the inflammatory response. Proper treatment of inflammatory diseases requires an appreciation of circadian effects (Hrushesky and Wood, 1997), so a quantitative understanding of diurnal variations on inflammation is

Acknowledgements

JDS and IPA acknowledge support from NIH GM082974. JDS, SEC and SFL are supported, in part, from NIH GM34695.

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