Computational Analysis Supports an Early, Type 17 Cell-Associated Divergence of Blunt Trauma Survival and Mortality*

Abstract

Objective Blunt trauma patients may present with similar demographics and injury severity, yet differ with regard to survival. We hypothesized that this divergence was due to different trajectories of systemic inflammation, and utilized computational analyses to define these differences. Design, Setting, and Patients From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24h but ultimately died (non-survivors; n=19) and patients who, following ICU admission, went on to be discharged (survivors; n=19). Data on systemic inflammatory mediators assessed within the first 24h and over 7d were analyzed with computational modeling to infer dynamic networks of inflammation. A mouse model of trauma/hemorrhage was used to verify hypotheses derived from the clinical study. Interventions None in patients. Neutralizing anti-IL-17A antibody in mice. Measurements and Main Results Network density among inflammatory mediators in non-survivors increased in parallel with organ dysfunction scores over 7d, suggesting the presence of early, self-sustaining, pathological inflammation involving HMGB1, IL-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine IL-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-IL-17A. Conclusions Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality following blunt trauma, and suggest a lymphoid cell-based switch from self-resolving to self-sustaining inflammation.

Solving Immunology?

“…Mechanistic modeling has provided non-intuitive mechanistic insight into many areas of immunology, ranging from molecular systems, such as how analog signals from TCR engagement become digital further downstream and the basis for non-linearities in the system [22–25] to clinical responses in disease, as described below. A major rationale for pursuing modeling in immunology (and systems biology more generally) is that complex networks with cooperativity and feedback (which broadly describes biological systems at every scale) exhibit complex, nonlinear relationships (a characteristic that can be inferred from, but not tested explicitly with, data-driven modeling [13, 26]). Simply put, it is difficult to predict how a given perturbation at the molecular level will affect system behavior at higher scales over time or at homeostasis without the aid of a mechanistic model that can be “played forward in time” under a variety of conditions that would be impractical to test only using experimentation.…”

Reverse engineering the inflammatory “clock”: from computational modeling to rational resetting

“…Perhaps the most striking of these network-based discoveries concerned the differences in dynamic inflammation networks in blunt trauma patients that succumbed to their critical illness vs. highly matched patients that survived, despite nearly identical injury and treatment characteristics [19]. That study derived multiple novel insights either directly or indirectly from computational modeling of dynamic networks of systemic inflammation: 1) that trauma-induced mortality which occurs in the intensive care unit is not simply a function of elevated injury severity, but rather appears to be a feature specific to a minority of trauma patients; 2) that trauma survivors and non-survivors can be distinguished by their inflammatory networks very early following injury; 3) that survivors exhibit inflammation that in many way resembles chronic inflammation whose hallmark is the production of lymphoid-derived mediators, rather than the classic concept of acute inflammation characterized primarily by innate immune mediators; and 4) the presence of self-sustaining inflammation network characterized by early activation of the Th17 axis in non-survivors, with dynamics that mirror those of multiple organ dysfunction [19].…”

“…We were faced with the realization that inflammation induced by traumatic injury, severe infection, or organ failure progresses extremely rapidly to drive a life or death bifurcation [18,19,23,32,33], and were driven by the aforementioned hypothesis regarding a feed-forward process of inflammation to damage/dysfunction to more inflammation [1]. Thus, the concept of finding an appropriate diagnostic test and administering an individual-specific therapy in a timely fashion is daunting, even given the possibility of mathematically modeling acute inflammation in individuals and populations in order to predict clinical outcomes [24,38].…”

“…This raises the potential for a device for patient-specific, self-adaptive diagnosis and therapy for acute inflammation, driven by insights from computational models. Future approaches may focus on key mediators such as IL-17 and related cytokines [19]. …”

Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

“…While multiple network analysis methods have been developed (see our later discussion of Dynamic Bayesian Networks), we developed Dynamic Network Analysis (DyNA) as a bridge between traditional statistics and mathematical modeling [22,[51][52][53][54][55][56][57]. It combines two basic statistical measures, t-testing and correlation, with the added dimension of time and uses a traditional node-and-link output to create networks that can be analyzed both visually and quantitatively.…”