Abstract
We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO2 −/NO3 − data from “middle-aged” (6–8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for “young” (2–3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging.
Highlights
Inflammation ensues upon acute biological stress such as bacterial infection or tissue trauma [1]
We confirmed this finding initially by measuring plasma levels of IL-6 (Fig. 1A), tumor necrosis factor-a (TNF-a) (Fig. 1B), IL-10 (Fig. 1C) and NO22/NO32 (Fig. 1D) in ‘‘young’’ (2–3 month old) and ‘‘middle-aged’’ (6–8 month old) C57BL/6 mice subjected to bolus intraperitoneal injection of 3 mg/kg LPS
We have previously developed a mechanistic mathematical model in which dynamics of leukocytes and their products affect the whole-organism inflammatory response assessed as circulating levels of mediators, that we calibrated using data from young C57BL/6 mice [12,16,26,27]
Summary
Inflammation ensues upon acute biological stress such as bacterial infection or tissue trauma [1]. To address the complexity of the acute inflammatory response in vivo, we have constructed mechanistic mathematical models of increasing complexity, using ordinary differential equations (ODE), which encompass the dynamics of relevant cells and cytokines as well as aspects of physiology and global tissue damage/dysfunction [12,13,14,15]. These computational models are capable of quantitative predictions, since they are calibrated to circulating levels of inflammatory mediators in mice [12,16], rats [17], and swine [18]. These models’ ability to predict mortality is based on the incorporation of a global tissue damage/dysfunction variable, which is both a marker of the adverse effects of inflammation as well as a driver of further inflammation
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