Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

Abstract

Significance: Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models.

Recent advances: Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies.

Critical issues: Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments.

Future directions: These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.

FIG. 1.

Course of acute inflammation following injury. Properly regulated and self-resolving inflammation allows for effective resolution, while inadequate or overly exuberant inflammation can result in immune dysregulation and subsequent processes such as persistent critical illness. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 2.

Schematic representation of activation of innate and T-cell-mediated responses following traumatic injury. Tissue injury generates damage-associated molecular patterns (DAMPs) from damaged cells, which initiate innate immune pathways by activation of pattern recognition receptors (PRRs) and, at least in part, through Toll-like receptor (TLR) signaling. This results in the release of inflammatory cytokines and chemokines from both structural cells (epithelial and fibroblasts) and antigen-presenting cells, such as resident macrophages (MΦ) and dendritic cells (DCs). These mediators are responsible for the activation of the endothelium (e.g., upregulation of adhesion molecules) and the recruitment and activation of leukocytes critical for innate immune responses (neutrophils, eosinophils, basophils, natural killer [NK] cells, and monocytes) and T-cell-mediated responses. The activation of these immune responses is essential to eliminate the inciting insult and to repair the destroyed tissue, thereby maintaining homeostasis. However, when uncontrolled, sustained, and exaggerated, the immune response becomes dysregulated, resulting in further damage through vicious feed-forward circuits causing further harm. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 3.

Dynamic Bayesian network inference suggests that interleukin (IL)-6 is regulated by monocyte chemotactic protein 1 (MCP-1) and monokine induced by gamma interferon (MIG) following trauma/hemorrhage in both mice and humans. Plasma inflammatory mediators were assessed over 0–24 h postinjury in hypotensive blunt trauma patients (A) or 0–5 h postinjury in C57Bl/6 mice (B) by Luminex™. Dynamic Bayesian network inference was carried out as described previously (3, 18, 19, 50, 158). Red: chemokines. Green: pro-inflammatory cytokine. Blue: anti-inflammatory cytokine. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 4.

Overview of workflow for integrating data-driven and mechanistic modeling. Multiplexed time course data are measured and causal interactions are inferred by dynamic Bayesian networks (DyBNs). Inferred network topology forms the basis of mechanistic equation-based models that can be simulated to compare with experimental/clinical data, suggest diagnostic initial conditions, and analyzed and validated with further experiments. Along this path, more focused hypotheses are generated, from associating dynamic patterns of inflammatory mediators with phenotype to hypothesizing functional roles for particular interactions in the inflammatory network. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 5.

A hypothetical chemokine-switching network. In this hypothetical framework based on protein-level (Luminex) data and DyBN inference from multiple studies in mice and humans, trauma stimulates the release of three key chemokines (interferon gamma-induced protein 10 [IP-10], MIG, and MCP-1). We hypothesize that MIG drives low-level adaptive production of IL-6, while MCP-1 drives high-level detrimental production of IL-6. We further hypothesize that IP-10 drives IL-10 production, which is beneficial within a range, but detrimental when overproduced. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 6.

A systems view of innate immune and T-cell-mediated acute inflammation. Trauma leads to early release of DAMPs, stimulating either proinflammatory (M1 macrophages, neutrophils, cytokines such as TNF-α) or anti-inflammatory (M2 macrophages, cytokines such as IL-10) pathways via the early production of defined chemokine subsets. This leads to either the resolution of inflammation via chemokines such as IP-10 or exacerbated inflammation via chemokines such as MIG and MCP-1 in concert with secondary release of DAMPs. In the setting of post-trauma infection, proinflammatory agents (e.g., TNF-α) cause further inflammation and tissue damage/dysfunction. When the positive feedback loop of inflammation→damage→inflammation (indicated in red) exceeds certain thresholds (tipping points), T-cell-mediated responses are initiated via activation of dendritic cells, NK, NK-T cells, cytotoxic T lymphocytes (CTL), and innate lymphoid cells (ILC). T-cell-mediated responses include early (min) γδ T cells and later (4–12 h) Th17 cells. This response either resolves via IL-10, with relatively low systemic spillover of mediators and little organ damage, or propagates in a feed-forward manner, with worsening of organ damage and the attendant elevation of IL-6. Thus, chemokines such as IP-10, MCP-1, and MIG, as well as cytokines such as IL-6 and IL-10 (indicated in blue), can be both biomarkers and potential therapeutic targets under the appropriate circumstances. TNF-α, tumor necrosis factor alpha. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 7.

Predicted dynamics of Th1 and Th2 cells in mouse models of acute inflammation. A differential equation model of acute inflammation was modified to include DC, Th1 cells, and Th2 cells. The model was partially calibrated against trajectories of TNF-α, IL-6, IL-10, and NO₂⁻/NO₃⁻ obtained from C57Bl/6 mice subjected to endotoxemia (A), surgical cannulation trauma (B), or surgical cannulation+hemorrhagic shock (C). Predicted trajectories of Th1 and Th2 cells are shown for the three inflammatory scenarios. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars

FIG. 8.

From data to models: a roadmap. Cells respond to cues regarding injury by elaborating chemokines that form defined networks, which can be detected using dynamic network analysis techniques. As the presence of signals and networks persists, early regulatory cytokines such as TNF-α and IL-1β begin to be secreted. These mediators are present at low levels, often with high variance, and their presence and effect may be inferred using techniques such as principal component analysis (PCA). Dynamic chemokine networks and initial cytokines together overcome thresholds of activation for later innate and lymphoid mediators such as IL-4 and IL-13, which would then be significantly elevated as defined by standard statistical analyses. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/ars