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

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. Antioxid. Redox Signal. 23, 1370?1387.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140310/1/ars.2015.6398.pd

Computational evidence for an early, amplified systemic inflammation program in polytrauma patients with severe extremity injuries

Extremity and soft tissue injuries contribute significantly to inflammation and adverse in-hospital outcomes for trauma survivors; accordingly, we examined the complex association between clinical outcomes inflammatory responses in this setting using in silico tools. Two stringently propensity-matched, moderately/severely injured (Injury Severity Score > 16) patient sub-cohorts of ~30 patients each were derived retrospectively from a cohort of 472 blunt trauma survivors and segregated based on their degree of extremity injury severity (above or below 3 on the Abbreviated Injury Scale). Serial blood samples were analyzed for 31 plasma inflammatory mediators. In addition to standard statistical analyses, Dynamic Network Analysis (DyNA) and Principal Component Analysis (PCA) were used to model systemic inflammation following trauma. Patients in the severe extremity injury sub-cohort experienced longer intensive care unit length of stay (LOS), total LOS, and days on a mechanical ventilator, with higher Marshall Multiple Organ Dysfunction (MOD) Scores over the first 7 days post-injury as compared to the mild/moderate extremity injury sub-cohort. The higher severity cohort had statistically significant elevated lactate, base deficit, and creatine phosphokinase on first blood draw, along with significant changes in multiple circulating inflammatory mediators. DyNA pointed to a sustained role for type 17 immunity in both sub-cohorts, along with IFN-γ in the severe extremity injury group. DyNA network complexity increased over 7 days post-injury in the severe injury group, while generally decreasing over this same time period in the mild/moderate injury group. PCA suggested a more robust activation of multiple pathways in the severe extremity injury group as compared to the mild/moderate injury group. These studies thus point to the possibility of self-sustaining inflammation following severe extremity injury vs. resolving inflammation following less severe extremity injury

Predicting Experimental Sepsis Survival with a Mathematical Model of Acute Inflammation

Sepsis is characterized by an overactive, dysregulated inflammatory response that drives organ dysfunction and often results in death. Mathematical modeling has emerged as an essential tool for understanding the underlying complex biological processes. A system of four ordinary differential equations (ODEs) was developed to simulate the dynamics of bacteria, the pro- and anti-inflammatory responses, and tissue damage (whose molecular correlate is damage-associated molecular pattern [DAMP] molecules and which integrates inputs from the other variables, feeds back to drive further inflammation, and serves as a proxy for whole-organism health status). The ODE model was calibrated to experimental data from E. coli infection in genetically identical rats and was validated with mortality data for these animals. The model demonstrated recovery, aseptic death, or septic death outcomes for a simulated infection while varying the initial inoculum, pathogen growth rate, strength of the local immune response, and activation of the pro-inflammatory response in the system. In general, more septic outcomes were encountered when the initial inoculum of bacteria was increased, the pathogen growth rate was increased, or the host immune response was decreased. The model demonstrated that small changes in parameter values, such as those governing the pathogen or the immune response, could explain the experimentally observed variability in mortality rates among septic rats. A local sensitivity analysis was conducted to understand the magnitude of such parameter effects on system dynamics. Despite successful predictions of mortality, simulated trajectories of bacteria, inflammatory responses, and damage were closely clustered during the initial stages of infection, suggesting that uncertainty in initial conditions could lead to difficulty in predicting outcomes of sepsis by using inflammation biomarker levels

Early Dynamic Orchestration of Immunologic Mediators Identifies Multiply Injured Patients who are Tolerant or Sensitive to Hemorrhage

BACKGROUND Multiply injured patients (MIPs) are at risk of complications including infections, and acute and prolonged organ dysfunction. The immunologic response to injury has been shown to affect outcomes. Recent advances in computational capabilities have shown that early dynamic coordination of the immunologic response is associated with improved outcomes after trauma. We hypothesized that patients who were sensitive or tolerant of hemorrhage would demonstrate differences in dynamic immunologic orchestration within hours of injury. METHODS We identified two groups of MIPs who demonstrated distinct clinical tolerance to hemorrhage (n = 10) or distinct clinical sensitivity to hemorrhage (n = 9) from a consecutive cohort of 100 MIPs. Hemorrhage was quantified by integrating elevated shock index values for 24 hours after injury (shock volume). Clinical outcomes were quantified by average Marshall Organ Dysfunction Scores from days 2 to 5 after injury. Shock-sensitive patients had high cumulative organ dysfunction after lower magnitude hemorrhage. Shock-tolerant (ST) patients had low cumulative organ dysfunction after higher magnitude hemorrhage. Computational methods were used to analyze a panel of 20 immunologic mediators collected serially over the initial 72 hours after injury. RESULTS Dynamic network analysis demonstrated the ST patients had increased orchestration of cytokines that are reparative and protective including interleukins 9, 17E/25, 21, 22, 23, and 33 during the initial 0- to 8-hour and 8- to 24-hour intervals after injury. Shock-sensitive patients had delayed immunologic orchestration of a network of largely proinflammatory and anti-inflammatory mediators. Elastic net linear regression demonstrated that a group of five mediators could discriminate between shock-sensitive and ST patients. CONCLUSIONS Preliminary evidence from this study suggests that early immunologic orchestration discriminates between patients who are notably tolerant or sensitive to hemorrhage. Early orchestration of a group of reparative/protective mediators was amplified in shock-tolerant patients

Insights into the association between coagulopathy and inflammation: abnormal clot mechanics are a warning of immunologic dysregulation following major injury

Background: Severe injury initiates a complex physiologic response encompassing multiple systems and varies phenotypically between patients. Trauma-induced coagulopathy may be an early warning of a poorly coordinated response at the molecular level, including a deleterious immunologic response and worsening of shock states. The onset of trauma-induced coagulopathy (TIC) may be subtle however. In previous work, we identified an early warning sign of coagulopathy from the admission thromboelastogram, called the MAR ratio. We hypothesized that a low MAR ratio would be associated with specific derangements in the inflammatory response. Methods: In this prospective, observational study, 88 blunt trauma patients admitted to the intensive care unit (ICU) were identified. Concentrations of inflammatory mediators were recorded serially over the course of a week and the MAR ratio was calculated from the admission thromboelastogram. Correlation analysis was used to assess the relationship between MAR and inflammatory mediators. Dynamic network analysis was used to assess coordination of immunologic response. Results: Seventy-nine percent of patients were male and mean age was 37 years (SD 12). The mean ISS was 30.2 (SD 12) and mortality was 7.2%. CRITICAL patients (MAR ratio ≤14.2) had statistically higher shock volumes at three time points in the first day compared to NORMAL patients (MAR ratio >14.2). CRITICAL patients had significant differences in IL-6 (P=0.0065), IL-8 (P=0.0115), IL-10 (P=0.0316) and MCP-1 (P=0.0039) concentrations compared to NORMAL. Differences in degree of expression and discoordination of immune response continued in CRITICAL patients throughout the first day. Conclusions: The admission MAR ratio may be the earliest warning signal of a pathologic inflammatory response associated with hypoperfusion and TIC. A low MAR ratio is an early indication of complicated dysfunction of multiple molecular processes following trauma

Combined In Silico, In Vivo, and In Vitro Studies Shed Insights into the Acute Inflammatory Response in Middle-Aged Mice

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. © 2013 Namas et al

Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies

The translation of in vitro findings to clinical outcomes is often elusive. Trauma/hemorrhagic shock (T/HS) results in hepatic hypoxia that drives inflammation. We hypothesize that in silico methods would help bridge in vitro hepatocyte data and clinical T/HS, in which the liver is a primary site of inflammation. Primary mouse hepatocytes were cultured under hypoxia (1% O 2) or normoxia (21% O2) for 1-72 h, and both the cell supernatants and protein lysates were assayed for 18 inflammatory mediators by Luminex™ technology. Statistical analysis and data-driven modeling were employed to characterize the main components of the cellular response. Statistical analyses, hierarchical and k-means clustering, Principal Component Analysis, and Dynamic Network Analysis suggested MCP-1/CCL2 and IL-1α as central coordinators of hepatocyte-mediated inflammation in C57BL/6 mouse hepatocytes. Hepatocytes from MCP-1-null mice had altered dynamic inflammatory networks. Circulating MCP-1 levels segregated human T/HS survivors from non-survivors. Furthermore, T/HS survivors with elevated early levels of plasma MCP-1 post-injury had longer total lengths of stay, longer intensive care unit lengths of stay, and prolonged requirement for mechanical ventilation vs. those with low plasma MCP-1. This study identifies MCP-1 as a main driver of the response of hepatocytes in vitro and as a biomarker for clinical outcomes in T/HS, and suggests an experimental and computational framework for discovery of novel clinical biomarkers in inflammatory diseases. © 2013 Ziraldo et al

Multi-omic analysis in injured humans: Patterns align with outcomes and treatment responses

Trauma is a leading cause of death and morbidity worldwide. Here, we present the analysis of a longitudinal multi-omic dataset comprising clinical, cytokine, endotheliopathy biomarker, lipidome, metabolome, and proteome data from severely injured humans. A "systemic storm" pattern with release of 1,061 markers, together with a pattern suggestive of the "massive consumption" of 892 constitutive circulating markers, is identified in the acute phase post-trauma. Data integration reveals two human injury response endotypes, which align with clinical trajectory. Prehospital thawed plasma rescues only endotype 2 patients with traumatic brain injury (30-day mortality: 30.3 versus 75.0%; p = 0.0015). Ubiquitin carboxy-terminal hydrolase L1 (UCHL1) was identified as the most predictive circulating biomarker to identify endotype 2-traumatic brain injury (TBI) patients. These response patterns refine the paradigm for human injury, while the datasets provide a resource for the study of critical illness, trauma, and human stress responses