Modeling endotoxin-induced systemic inflammation using an indirect response approach

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Abstract

A receptor mediated model of endotoxin-induced human inflammation is proposed. The activation of the innate immune system in response to the endotoxin stimulus involves the interaction between the extracellular signal and critical receptors driving downstream signal transduction cascades leading to transcriptional changes. We explore the development of an in silico model that aims at coupling extracellular signals with essential transcriptional responses through a receptor mediated indirect response model. The model consists of eight (8) variables and is evaluated in a series of biologically relevant scenarios indicative of the non-linear behavior of inflammation. Such scenarios involve a self-limited response where the inflammatory stimulus is cleared successfully; a persistent infectious response where the inflammatory instigator is not eliminated, leading to an aberrant inflammatory response, and finally, a persistent non-infectious inflammatory response that can be elicited under an overload of the pathogen-derived product; as such high dose of the inflammatory insult can disturb the dynamics of the host response leading to an unconstrained inflammatory response. Finally, the potential of the model is demonstrated by analyzing scenarios associated with endotoxin tolerance and potentiation effects.

Introduction

Although systemic inflammation is but one component of the sepsis syndrome, inflammation can be studied in the absence of complex pathophysiology and co-morbidities of human sepsis using surrogate models. Human endotoxin challenge is one well-accepted surrogate model for studying the acute inflammatory response as it captures many of the clinically observed features of systemic inflammatory phenotype [1], [2], [3], [4], [5]. Endotoxin is a major component of the outer membrane of Gram-negative bacteria, and the inflammation caused by the activation of the innate immune system by this moiety can be a complicating factor in a variety of situations including trauma, burns, invasive surgery and organ-specific illnesses. The prototypical examples of endotoxin are lipopolysaccharides (LPS).

The response following endotoxin administration in human subjects include core temperature, cardiac, vasomotor, hematologic, metabolic, hormonal, acute phase reactant, and cytokine components that have been well described [4], [6], [7], [8]. Innate immune cell activation leads to production and release of pro-inflammatory cytokines, which are proximal mediators of the systemic inflammatory response. Although the bulk of this pro-inflammatory mediator release likely originates in cells of the reticuloendothelial system [9], the leukocytes present in peripheral blood are also activated, and importantly, are available for sampling with minimal invasiveness.

In order to study the underlying complexity of the dynamics of inflammation and to establish quantifiable relationships among the various components of the inflammatory response, model-based approaches have been proposed [10], [11], [12]. A number of excellent prior studies [11], [13], [14], [15], [16], [17] have placed significant emphasis on simulating inflammation based on the kinetics of well-defined features of the overall response. One of the key characteristics of these models is the a priori postulation of certain components that are consistent with prior biological knowledge and are known to play a major role in triggering the inflammatory response. The computational integration of such components can provide us with significant insight of how such elements behave over time empowering their translational application as predictive controls in clinical settings.

However, one of the big challenges is the systematic identification of such representative biological features that can adequately represent the complex dynamics of a host undergoing an inflammatory response. This requires the decomposition of the non-linear dynamics of the response into an elementary set that can serve as a surrogate for predicting the collective behavior of the system. A possible answer to this can be identified through the analysis of gene expression data aimed at monitoring the dynamics of the host response to an inflammatory agent, exploring the idea that cellular responses correspond to dynamically converging high-dimensional transcriptional trajectories [18].

Given the transcriptional profiling analysis of human blood leukocytes we are driven by the premise that genes that are most responsive to an external perturbation (endotoxin, LPS) are governed by a definite mechanism and have concerted changes in their expression profile. Thus, we test the hypothesis that there exists a representative set of ensuing responses that emerge from the dynamic evolution of the inflammatory response after exposure to endotoxin (LPS). Such responses include the pro-inflammatory response that consists of the early increased expression of cytokines and chemokines; the anti-inflammatory response which serves as the immunoregulatory arm of the host defense system and ultimately the energetic response that involves the decreased expression of genes that participate in cellular bio-energetic processes. In this paper we explore the development of a semi-mechanistic model of endotoxin-induced human inflammation though the integration of transcriptional profiling and indirect response models. Indirect response models have been widely used in pharmacokinetic/pharmacodynamic models simulating the physiological response of a system exposed to an external signal or perturbation [19], [20], [21]. The activation of the innate immune system in response to the endotoxin stimulus involves the interaction between the extracellular signal and critical receptors driving downstream signal transduction cascades that lead to transcriptional changes. Our inability to precisely model the complex signaling events that characterize system’s adaptation process to environmental changes makes indirect response model (IDR) modeling appealing. We opt therefore to explore the development of an in silico representation that aims at coupling extracellular signals with the essential transcriptional responses through a receptor mediates indirect response model.

The dynamics of the system are described by eight (8) variables and the proposed model is evaluated in a series of biological relevant scenarios indicative of the non-linear behavior of inflammation. Such scenarios involve a self-limited response where the inflammatory stimulus (endotoxin) is cleared successfully; a persistent response where the inflammatory instigator is not eliminated leading to an aberrant inflammatory response, and finally, a persistent non-infectious inflammatory response that can be elicited under an overload of the LPS; as such high dose of the inflammatory insult can disturb the dynamics of the host response leading to an unconstrained inflammatory response. In this study the terms endotoxin and inflammatory stimulus are used interchangeably.

Finally, we demonstrate the potential of our model by analyzing scenarios associated with endotoxin tolerance and potentiation effects. The pre-exposure of the host to controlled levels of inflammatory agents affects the eventual fate of the response. These controlled perturbations aim at further deciphering the kinetics and dynamics of the host response. It is therefore believed that the dynamic response defining the fate of the host response is critically affected by the appropriate priming. We loosely connect such responses with the emergence of memory effects in the sense that preconditioning the system with low levels of endotoxin modulates the host response dynamics causing either a tolerance or a potentiation effect. It has been observed that repeated doses of endotoxin insult might lead to a less vigorous innate immune response [22]. Such an effect can reverse the lethal outcome of a high dose of the inflammatory stimulus. That is to say, in spite of the potent efficacy of LPS, if the system is pre-exposed to lower sub-lethal doses of LPS then this induces an acquired state of resistance to a subsequent endotoxin challenge [23]. Importantly, endotoxin tolerance or hypo-responsiveness is a multifactorial problem that can be associated with receptor desensitization as well as with the modulated activity of negative regulators that target intracellular or transcriptional events. On the other hand, the successive administration of sublethal doses of endotoxin can potentiate the system in that, because of the lack of an acquired state in the dynamics of the system, such an insult may dysregulate the host response dynamics leading to an exacerbated inflammation that cannot resolve. Thus, based on our model we further explore the behavior of the system when it is either pre-exposed to lower levels of endotoxin for ‘adequate’ time as well as when the system has not manifested its ‘dynamic memory’ to tolerate the second endotoxin challenge.

Section snippets

Human endotoxin model and data collection

The data analyzed in this study were generated by the Inflammation and Host Response to Injury Large Scale Collaborative Project funded by the USPHS, U54 GM621119 [1], [24]. In brief, human subjects (n = 8) were injected intravenously with either endotoxin (CC-RE, lot 2) at a dose of 2-ng/kg body weight or 0.9% sodium chloride (placebo treated subjects). Blood samples were collected before endotoxin infusion (0 h) and 2, 4, 6, 9 and 24 h after injection of endotoxin or saline. Cellular RNA was

Transcriptional analysis and major response elements

The symbolic transformation of the expression motifs and the subsequent assignment of hash values to each expression profile [27] produces a distribution of motif values for all the available probes, Fig. 1. In order to estimate a p-value for each expression motif we generate random data with the same dimensions as the original dataset (5093 probe sets and 6 time points). Genes that hash to the same integer value for the random data are characterized by a distribution that approximately follows

Disclaimer

The Inflammation and the Host Response to Injury ‘Glue Grant’ program is supported by the National Institute of General Medical Sciences. This manuscript was prepared using a dataset obtained from the Glue Grant program and does not necessarily reflect the opinions or views of the Inflammation and the Host Response to Injury Investigators or the NIGMS.

Acknowledgments

P.T.F. and I.P.A. acknowledge support from NSF Grant 0519563, EPA Grant GAD R 832721-010-RRA and a Busch Biomedical Research Grant. SEC and SFL are supported, in part, from USPHS Grant GM34695. The authors acknowledge critical input and guidance from Prof. W.J. Jusko and R.R. Almon (SUNY Buffalo). The investigators acknowledge the contribution of the Inflammation and the Host Response to Injury Large-Scale Collaborative Project Award #2-U54-GM062119 from the National Institute of General

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