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Significance: The immunoinflammatory responses that follow trauma contribute to clinical trajectory and patient outcomes. While remarkable advances have been made in trauma services and injury management, clarity on how the immune system in humans responds to trauma is lagging. Recent Advances: Multiplexing platforms have transformed our ability to analyze comprehensive immune mediator responses in human trauma. In parallel, with the establishment of large data sets, computational methods have been adapted to yield new insights based on mediator patterns. These efforts have added an important data layer to the emerging multiomic characterization of the human response to injury. Critical Issues: Outcome after trauma is greatly affected by the host immunoinflammatory response. Excessive or sustained responses can contribute to organ damage. Hence, understanding the pathophysiology behind traumatic injury is of vital importance. Future Directions: This review summarizes our work in the study of circulating immune mediators in trauma patients. Our foundational studies into dynamic patterns of inflammatory mediators represent an important contribution to the concepts and computational challenges that these large data sets present. We hope to see further integration and understanding of multiomics strategies in the field of trauma that can aid in patient endotyping and in potentially identifiying certain therapeutic targets in the future. Antioxid. Redox Signal. 35, 1393–1406.

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.

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

The alpha 7 nicotinic acetylcholine receptor, α7 nAChR, plays a central role in regulating inflammatory responses. Previous studies showed that pharmacological inhibitors of α7nAChR have a...

Acetaminophen (APAP) overdose (APAPo) is predominant in the NIH Pediatric Acute Liver Failure (PALF) Study. We assayed multiple inflammatory mediators in serial serum samples from 13 PALF survivors with APAPo + N-acetylcysteine (NAC, the frontline therapy for APAPo), 8 non-APAPo + NAC, 40 non-APAPo non-NAC, and 12 non-survivors. High Mobility Group Box 1 (HMGB1) was a dominant mediator in dynamic inflammation networks in all sub-groups, associated with a threshold network complexity event at d1–2 following enrollment that was exceeded in non-survivors vs. survivors. We thus hypothesized that differential HMGB1 network connectivity after day 2 is related to the putative threshold event in non-survivors. DyNA showed that HMGB1 is most connected in non-survivors on day 2–3, while no connections were observed in APAPo + NAC and non-APAPo + NAC survivors. Inflammatory dynamic networks, and in particular HMGB1 connectivity, were associated with the use of NAC in the context of APAPo. To recapitulate hepatocyte (HC) damage in vitro, primary C57BL/6 HC and HC-specific HMGB1-null HC were treated with APAP + NAC. Network phenotypes of survivors were recapitulated in C57BL/6 mouse HC and were greatly altered in HMGB1-null HC. HC HMGB1 may thus coordinate a pro-inflammatory program in PALF non-survivors (which is antagonized by NAC), while driving an anti-inflammatory/repair program in survivors.

Systems-level insights into inflammatory events after vascularized composite allotransplantation (VCA) are critical to the success of immunomodulatory strategies of these complex procedures. To date, the effects of tacrolimus (TAC) immunosuppression on inflammatory networks in VCA, such as in acute rejection (AR), have not been investigated. We used a systems biology approach to elucidate the effects of tacrolimus on dynamic networks and principal drivers of systemic inflammation in the context of dynamic tissue-specific immune responses following VCA. Lewis (LEW) rat recipients received orthotopic hind limb VCA from fully major histocompatibility complex-mismatched Brown Norway (BN) donors or matched LEW donors. Group 1 (syngeneic controls) received LEW limbs without TAC, and Group 2 (treatment group) received BN limbs with TAC. Time-dependent changes in 27 inflammatory mediators were analyzed in skin, muscle, and peripheral blood using Principal Component Analysis (PCA), Dynamic Bayesian Network (DyBN) inference, and Dynamic Network Analysis (DyNA) to define principal characteristics, central nodes, and putative feedback structures of systemic inflammation. Analyses were repeated on skin + muscle data to construct a “Virtual VCA”, and in skin + muscle + peripheral blood data to construct a “Virtual Animal.” PCA, DyBN, and DyNA results from individual tissues suggested important roles for leptin, VEGF, various chemokines, the NLRP3 inflammasome (IL-1β, IL-18), and IL-6 after TAC treatment. The chemokines MCP-1, MIP-1α; and IP-10 were associated with AR in controls. Statistical analysis suggested that 24/27 inflammatory mediators were altered significantly between control and TAC-treated rats in peripheral blood, skin, and/or muscle over time. “Virtual VCA” and “Virtual Animal” analyses implicated the skin as a key control point of dynamic inflammatory networks, whose connectivity/complexity over time exhibited a U-shaped trajectory and was mirrored in the systemic circulation. Our study defines the effects of TAC on complex spatiotemporal evolution of dynamic inflammation networks in VCA. We also demonstrate the potential utility of computational analyses to elucidate nonlinear, cross-tissue interactions. These approaches may help define precision medicine approaches to better personalize TAC immunosuppression in VCA recipients.

The inflammatory response is a dynamic, complex process that involves multiple interacting cell types and molecular mediators. Proinflammatory, feed-forward processes allow for proper ramp-up of inflammation but can also drive a maladaptive, overly exuberant response that harms tissues. Antiinflammatory, negative feedback processes keep these feed-forward aspects of inflammation in check and often stimulate healing, but when produced inappropriately can either impair healing or, at the other extreme, drive excessive scaring and fibrosis. Biomaterials are used to repair injured tissues but may be suboptimal or even harmful if they exacerbate the inflammatory response in those tissues. In this chapter, we describe how computational approaches to modeling inflammation in silico—including some studies involving biomaterials—have helped us decipher some of the complexity of inflammation, suggesting applications of computational modeling to optimize the preclinical and clinical use of biomaterials.

The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infants and children diagnosed with this complex clinical syndrome. Outcomes in PALF reflect interactions among the child’s clinical condition, response to supportive care, disease severity, potential for recovery, and, if needed, availability of a suitable organ for liver transplantation (LTx). Previously, we used computational analyses of immune/inflammatory mediators that identified three distinct dynamic network patterns of systemic inflammation in PALF associated with spontaneous survivors, non-survivors (NS), and LTx recipients. To date, there are no data exploring age-specific immune/inflammatory responses in PALF. Accordingly, we measured a number of clinical characteristics and PALF-associated systemic inflammatory mediators in daily serum samples collected over the first 7 days following enrollment from five distinct PALF cohorts (all spontaneous survivors without LTx): infants (INF, NO2−/NO3−. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all sub-groups, with MIG/CXCL9 being a central node in all groups except INF. Dynamic Network Analysis (DyNA) inferred different dynamic patterns and overall dynamic inflammatory network complexity as follows: OCH>INF>TOD>ADO>YCH. Hypothesizing that systemically elevated but sparsely connected inflammatory mediators represent pathological inflammation, we calculated the AuCon score (area under the curve derived from multiple measures over time divided by DyNA connectivity) for each mediator, and identified HMGB1, MIG, IP-10/CXCl10, sIL-2Rα, and MCP-1/CCL2 as potential correlates of PALF pathophysiology, largely in agreement with the results of Partial Least Squares Discriminant Analysis. Since NS were in the INF age group, we compared NS to INF and found greater inflammatory coordination and dynamic network connectivity in NS vs. INF. HMGB1 was the sole central node in both INF and NS, though NS had more downstream nodes. Thus, multiple machine learning approaches were used to gain both basic and potentially translational insights into a complex inflammatory disease.

The liver is an organ key to myriad metabolic and immune/inflammatory function. Thus, liver diseases have a tremendous impact on quality of life and can be lethal. Liver failure is a prototype of complex disease that has defied therapeutic modulation due to complex interactions among multiple cell types present in the liver, inflammation that occurs as a consequence of those interactions, and the systemic spillover of inflammatory mediators that can drive a process of multiple organ dysfunction. This chapter describes multiple computational modeling approaches aimed at gaining both basic and clinically actionable knowledge in the context of liver failure. While this chapter is focused on the liver, the methods and approaches are likely to be informative and useful to investigators in other fields as well.

The α7 neuronal nicotinic acetylcholine receptors (α7nAChRs) are essential for anti-inflammatory responses. The human-specific CHRFAM7A gene and its 2bp deletion polymorphism (Δ2bp variant) encodes a structurally-deficient α7nAChRs that may impact the anti-inflammatory function. We studied 45 spinal cord injury (SCI) patients for up to six weeks post SCI to investigate the role of the Δ2bp variant on multiple circulating inflammatory mediators and two outcome measures (neuropathic pain and risk of pressure ulcers). The patient’s SCI were classified as either severe or mild. Missing values were imputed. Overall genetic effect was conducted with independent sample t-test and corrected with false discovery rate (FDR). Univariate analysis and regression analysis were applied to evaluate the Δ2bp effects on temporal variation of inflammatory mediators post SCI and their interaction with outcome measures. In severe SCI, the Δ2bp carriers showed higher levels of circulating inflammatory mediators than the Δ2bp non-carriers in TNF-α (FDR = 9.6x10-4), IFN-γ (FDR = 1.3x10-3), IL-13 (FDR = 1.6x10-3), CCL11 (FDR = 2.1x10-3), IL-12p70 (FDR = 2.2x10-3), IL-8 (FDR = 2.2x10-3), CXCL10 (FDR = 3.1x10-3), CCL4 (FDR = 5.7x10-3), IL-12p40 (FDR = 7.1x10-3), IL-1b (FDR = 0.014), IL-15 (FDR = 0.024), and IL-2 (FDR = 0.037). IL-8 and CCL2 were negatively associated with days post injury (DPI) for the Δ2bp carriers (P = 2x10-7 and P = 2x10-8, respectively) and IL-5 was positively associated with DPI for the Δ2bp non-carriers (P = 0.015). Neuropathic pain was marginally positively associated with IL-13 for the Δ2bp carriers (P = 0.056). In mild SCI, the Δ2bp carriers had lower circulating levels of IL-15 (FDR = 0.04) than the Δ2bp non-carriers. Temporal variation of inflammatory mediators post SCI was not associated with the Δ2bp variant. For the mild SCI Δ2bp carriers, risk of pressure ulcers was positively associated with circulating levels of IFN-γ, CXCL10, and CCL4 and negatively associated with circulating levels of IL-12p70. These findings support an important role for the human-specific CHRFAM7A Δ2bp gene variant in modifying anti-inflammatory function of α7nAChRs following SCI.

The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices.

Immune dysfunction is an important factor driving mortality and adverse outcomes after trauma but remains poorly understood, especially at the cellular level. To deconvolute the trauma-induced immune response, we applied single-cell RNA sequencing to circulating and bone marrow mononuclear cells in injured mice and circulating mononuclear cells in trauma patients. In mice, the greatest changes in gene expression were seen in monocytes across both compartments. After systemic injury, the gene expression pattern of monocytes markedly deviated from steady state with corresponding changes in critical transcription factors, which can be traced back to myeloid progenitors. These changes were largely recapitulated in the human single-cell analysis. We generalized the major changes in human CD14+ monocytes into 6 signatures, which further defined 2 trauma patient subtypes (SG1 vs. SG2) identified in the whole-blood leukocyte transcriptome in the initial 12 hours after injury. Compared with SG2, SG1 patients exhibited delayed recovery, more severe organ dysfunction, and a higher incidence of infection and noninfectious complications. The 2 patient subtypes were also recapitulated in burn and sepsis patients, revealing a shared pattern of immune response across critical illness. Our data will be broadly useful to further explore the immune response to inflammatory diseases and critical illness.

Declining life expectancy and increasing all-cause mortality in the United States have been associated with unhealthy behaviors, socioecological factors, and preventable disease. A growing body of basic science, clinical research, and population health evidence points to the benefits of healthy behaviors, environments and policies to maintain health and prevent, treat, and reverse the root causes of common chronic diseases. Similarly, innovations in research methodologies, standards of evidence, emergence of unique study cohorts, and breakthroughs in data analytics and modeling create new possibilities for producing biomedical knowledge and clinical translation. To understand these advances and inform future directions research, The Lifestyle Medicine Research Summit was convened at the University of Pittsburgh on December 4–5, 2019. The Summit's goal was to review current status and define research priorities in the six core areas of lifestyle medicine: plant-predominant nutrition, physical activity, sleep, stress, addictive behaviors, and positive psychology/social connection. Forty invited subject matter experts (1) reviewed existing knowledge and gaps relating lifestyle behaviors to common chronic diseases, such as cardiovascular disease, diabetes, many cancers, inflammatory- and immune-related disorders and other conditions; and (2) discussed the potential for applying cutting-edge molecular, cellular, epigenetic and emerging science knowledge and computational methodologies, research designs, and study cohorts to accelerate clinical applications across all six domains of lifestyle medicine. Notably, federal health agencies, such as the Department of Defense and Veterans Administration have begun to adopt “whole-person health and performance” models that address these lifestyle and environmental root causes of chronic disease and associated morbidity, mortality, and cost. Recommendations strongly support leveraging emerging research methodologies, systems biology, and computational modeling in order to accelerate effective clinical and population solutions to improve health and reduce societal costs. New and alternative hierarchies of evidence are also be needed in order to assess the quality of evidence and develop evidence-based guidelines on lifestyle medicine. Children and underserved populations were identified as prioritized groups to study. The COVID-19 pandemic, which disproportionately impacts people with chronic diseases that are amenable to effective lifestyle medicine interventions, makes the Summit's findings and recommendations for future research particularly timely and relevant.

Background and aims Liver ischemia/reperfusion injury (IRI) induces local and systemic inflammation in which neutrophil extracellular traps (NETs) are major drivers. IRI markedly augments metastatic growth, which is consistent with the notion that the liver IRI can serve as a premetastatic niche. Exercise training (ExT) confers a sustainable protection, reducing IRI in some animal models, and has been associated with improved survival in patients with cancer; however, the impact of ExT on liver IRI or development of hepatic metastases is unknown. Approach and results Mice were randomized into exercise (ExT) and sedentary groups before liver IRI and tumor injection. Computerized dynamic network analysis of 20 inflammatory mediators was used to dissect the sequence of mediator interactions after ischemia/reperfusion (I/R) that induce injury. ExT mice showed a significant decrease in hepatic IRI and tissue necrosis. This coincided with disassembly of complex networks among inflammatory mediators seen in sedentary mice. Neutrophil infiltration and NET formation were decreased in the ExT group, which suppressed the expression of liver endothelial cell adhesion molecules. Concurrently, ExT mice revealed a distinct population of infiltrating macrophages expressing M2 phenotypic genes. In a metastatic model, fewer metastases were present 3 weeks after I/R in the ExT mice, a finding that correlated with a marked increase in tumor-suppressing T cells within the tumor microenvironment. Conclusions ExT preconditioning mitigates the inflammatory response to liver IRI, protecting the liver from injury and metastases. In light of these findings, potential may exist for the reduction of liver premetastatic niches induced by liver IRI through the use of ExT as a nonpharmacologic therapy before curative surgical approaches.

BACKGROUND: Prehospital plasma improves survival in severely injured patients transported by air ambulance. We hypothesized that prehospital plasma would be associated with a reduction in immune imbalance and endothelial damage. METHODS: We sampled blood from 405 trauma patients enrolled in the Prehospital Air Medical Plasma (PAMPer) trial upon hospital admission (0 hours) and 24 hours post admission across 6 U.S. sites. We assayed samples for 21 inflammatory mediators and 7 markers associated with endothelial function and damage. We performed hierarchical clustering analysis (HCA) of these biomarkers of the immune response and endothelial injury. Regression analysis was used to control for differences across study and to assess any association with prehospital plasma resuscitation. RESULTS: HCA distinguished two patient clusters with different injury patterns and outcomes. Patients in cluster A had greater injury severity and incidence of blunt trauma, traumatic brain injury, and mortality. Cluster A patients that received prehospital plasma showed improved 30-day survival. Prehospital plasma did not improve survival in cluster B patients. In an adjusted analysis of the most seriously injured patients, prehospital plasma was associated with an increase in adiponectin, IL-1β, IL-17A, IL-23, and IL-17E upon admission, and a reduction in syndecan-1, TM, VEGF, IL-6, IP-10, MCP-1, and TNF-α, and an increase in IL-33, IL-21, IL-23, and IL-17E 24 hours later. CONCLUSION: Prehospital plasma may ameliorate immune dysfunction and the endotheliopathy of trauma. These effects of plasma may contribute to improved survival in injured patients. TRIAL REGISTRATION: NCT01818427. FUNDING: Department of Defense; National Institutes of Health, U.S. Army.

Objective Chronic venous insufficiency (CVI) affects 25 million adults in the United States. Little emphasis has been placed on inflammatory changes associated with CVI. We hypothesize that in patients with early to mid-stage benign varicose vein disease, differences in circulating inflammatory mediators will be manifested in blood draining the involved area vs circulating blood in control subjects. Methods Patients undergoing either endovenous ablation or sclerotherapy for Clinical, Etiology, Anatomy, and Pathophysiology clinical class 3 to 5 disease underwent phlebotomy from regional veins at the time of the procedure. The patient's age, gender, clinical class, duration of symptoms, presence of superficial truncal reflux by duplex ultrasound, and treatment modality were recorded. Plasma from patients and banked blood samples from healthy volunteers (HVs) were subjected to Luminex (EMD Millipore, Billerica, Mass) to evaluate the expression of an established panel of 20 inflammatory mediators. Mediator concentrations were compared between patients and HVs using Mann-Whitney U tests. Importantly, computational analysis allowed us to compare not only the panel of inflammatory mediators but also the inflammatory networks connecting these mediators to one another. Principal components were analyzed to assess network robustness in each group. Results CVI venous blood revealed significantly lower levels of monokine induced by γ interferon, soluble interleukin (IL) 2 receptor α chain, IL-4, IL-6, IL-7, tumor necrosis factor α, eotaxin, and granulocyte-macrophage colony-stimulating factor than blood from controls. Inflammatory networks were significantly less complex and less robust in the CVI patients compared with HVs. Based on principal component analysis, responses among HVs were more varied than those of CVI patients. Conclusions We demonstrate that patients with CVI have significant differences not only in blood-borne inflammatory mediators but also in the interconnectedness of these mediators with one another and in their principal inflammatory characteristics. Results suggest hypoinflammation in chronic nonhealing changes in CVI. These novel findings, if validated in larger cohorts, may help predict the risk of disease progression or response to therapy in the future and may guide mechanistic studies on tissue responses to CVI.

Purpose We hypothesized that early inflammation can drive, or impact, later multiple organ dysfunction syndrome (MODS), that patient-specific principal component analysis (PCA) of circulating inflammatory mediators could reveal conserved dynamic responses which would not be apparent from the unprocessed data, and that this computational approach could segregate trauma patients with regard to subsequent MODS. Methods From a cohort of 472 blunt trauma survivors, 2 separate subcohorts of moderately/severely injured patients were studied. Multiple inflammatory mediators were assessed in serial blood samples in the first 24 hours postinjury. PCA of these time course data was used to derive patient-specific “inflammation barcodes,” followed by hierarchical clustering to define patient subgroups. To define the generalizability of this approach, 2 different but overlapping Luminex kits were used. Results PCA/hierarchical clustering of 24-hour Luminex data segregated the patients into 2 groups that differed significantly in their Marshall multiple organ dysfunction score on subsequent days, independently of the specific set of inflammatory mediators analyzed. Multiple inflammatory mediators and their dynamic networks were significantly different in the 2 groups in both patient cohorts, demonstrating that the groups were defined based on “core” early responses exhibit truly different dynamic inflammatory trajectories. Conclusion Identification of patient-specific “core responses” can lead to early segregation of diverse trauma patients with regard to later MODS. Hence, we suggest that a focus on dynamic inflammatory networks rather than individual biomarkers is warranted.

Circadian oscillations of immunity and inflammation have been characterized, and animal studies suggest that the time of day is a determinant of the immunological response to both injury and infection. We hypothesized that due to this diurnal variation, time of injury can affect the systemic inflammatory response and outcomes post-trauma and tested this hypothesis by examining the dynamics of circulating inflammatory mediators in blunt trauma patients injured during daytime vs. nighttime. From a cohort of 472 blunt trauma survivors, two stringently matched sub-cohorts of moderately/severely injured patients [injury severity score (ISS) >20] were identified. Fifteen propensity-matched, daytime-inured (“mDay”) patients (age 43.6 ± 5.2, M/F 11/4, ISS 22.9 ± 0.7) presented during the shortest local annual period (8:00 am-5:00 pm), and 15 propensity-matched “mNight” patients (age 43 ± 4.3, M/F 11/4, ISS 24.5 ± 2.5) presented during the shortest night period (10:00 pm-5:00 am). Serial blood samples were obtained (3 samples within the first 24 h and daily from days 1-7) from all patients. Thirty-two plasma inflammatory mediators were assayed. Two-way Analysis of Variance (ANOVA) was used to compare groups. Dynamic Network Analysis (DyNA) and Dynamic Bayesian Network (DyBN) inference were utilized to infer dynamic interrelationships among inflammatory mediators. Both total hospital and intensive care unit length of stay were significantly prolonged in the mNight group, despite a greater need for surgical interventions in mDay group. Circulating IL-17A was elevated significantly in the mNight group from 24 h – 7 days post-injury. Circulating MIP-1alpha, IL-7, IL-15, GM-CSF, and sST2 were elevated in the mDay group. DyNA demonstrated elevated network complexity in the mNight vs. the mDay group. DyBN suggested that cortisol and sST2 were central nodes upstream of TGF-beta1, chemokines, and Th17/protective mediators in both groups, with IL-6 being an additional downstream node in the mNight group only. Our results suggest that time of injury affects the clinical outcomes in severely injured patients in a manner associated with an altered systemic inflammation program, possibly implying a role for circadian variation in the response to traumatic injury.

Trauma is a leading cause of morbidity and mortality. It is unclear why some trauma victims follow a complicated clinical course and die, while others, with apparently similar injury characteristics, do not. Interpatient genomic differences, in the form of single nucleotide polymorphisms (SNPs), have been associated previously with adverse outcomes after trauma. Recently, we identified seven novel SNPs associated with mortality following trauma. The aim of the present study was to determine if one or more of these SNPs was also associated with worse clinical outcomes and altered inflammatory trajectories in trauma survivors. Accordingly, of 413 trauma survivors, DNA samples, full blood samples, and clinical data were collected at multiple time points in the first 24 h and then daily over 7 days following hospital admission. Subsequently, single-SNP groups were created and outcomes, such as hospital length of stay (LOS), ICU LOS, and requirement for mechanical ventilation, were compared. Across a broad range of Injury Severity Scores (ISS), patients carrying the rs2065418 TT SNP in the metallophosphoesterase domain-containing 2 (MPPED2) gene exhibited higher Marshall MODScores vs. the control group of rs2065418 TG/GG patients. In patients with high-severity trauma (ISS ≥ 25, n = 94), those carrying the rs2065418 TT SNP in MPPED2 exhibited higher Marshall MODScores, longer hospital LOS (21.8 ± 2 days), a greater requirement for mechanical ventilation (9.2 ± 1.4 days on ventilator, DOV), and higher creatinine plasma levels over 7 days vs. the control group of rs2065418 TG/GG high-severity trauma patients (LOS: 15.9 ± 1.2 days, p = 0.03; DOV: 5.7 ± 1 days, p = 0.04; plasma creatinine; p < 0.0001 MODScore: p = 0.0003). Furthermore, rs2065418 TT patients with ISS ≥ 25 had significantly different plasma levels of nine circulating inflammatory mediators and elevated dynamic network complexity. These studies suggest that the rs2065418 TT genotype in the MPPED2 gene is associated with altered systemic inflammation, increased organ dysfunction, and greater hospital resource utilization. A screening for this specific SNP at admission might stratify severely injured patients regarding their lung and kidney function and clinical complications.

Background: The cellular and molecular response of liver cells to hypoxic stress is not fully understood. We used computational modeling to gain insights into the inflammatory response of primary human hepatocytes (HC) to hypoxic stress in vitro. Methods: Primary HC from cancer patients were exposed to hypoxia (1% O 2 ) or normoxia (21% O 2 ) for 1–48 h, and the cell supernatants were assayed for 21 inflammatory mediators. Data were analyzed by Two-Way ANOVA, Dynamic Bayesian Network (DBN) inference, Dynamic Network Analysis (DyNA), and Time-interval Principal Component Analysis (TI-PCA). Results: The chemokines MCP-1/CCL2 and IP-10/CXCL10, along with the cytokines interleukin (IL)-2 and IL-15 were altered significantly over time in hypoxic vs . normoxic HC. DBN inference suggested central, coordinating roles for MCP-1 and IL-8 in regulating a largely conserved inflammatory program in both hypoxic and normoxic HC. DyNA likewise suggested similar network trajectories of decreasing complexity over time in both hypoxic and normoxic HC, though with differential connectivity of MCP-1, IP-10, IL-8, and Eotaxin. TI-PCA pointed to IL-1β as a central characteristic of inflammation in hypoxic HC across all time intervals, along with IL-15 and IL-10, vs . Eotaxin, IL-7, IL-10, IL-15, and IL-17A in normoxic HC. Conclusions: Thus, diverse human HC appear to respond in a largely conserved fashion to cell culture stress, with distinct characteristics based on the presence or absence of hypoxia.

The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients is unknown. The AA genotype of the aging-related single-nucleotide polymorphism (SNP) rs2075650 in TOMM40 has been associated with longevity, while the AG and GG genotypes are associated with an increased risk of Alzheimer’s disease. Here, we studied the effect of rs2075650 on clinical outcomes and dynamic biomarker patterns after traumatic injury. Genomic DNA was obtained from blunt trauma patients admitted to the ICU and examined for 551,839 SNPs using an Illumina microarray kit. Plasma was sampled from each patient three times within the first 24 hours and daily from day 1 to 7 then assayed for 31 biomarkers using Luminex. Aged patients (65–90 years) were segregated into AA (n = 77) and AG/GG (n = 17) genotypes. Additional comparisons were made with matched groups of young patients (18–30 years), controlling for injury severity score (ISS) and sex ratio, and also segregated into AA (n = 56) and AG/GG (n = 19) genotypes. Aged patients with the AA genotype had a significantly lower requirement for ventilation and fewer days on mechanical ventilation, as well as significantly higher levels of one mediator and lower levels of two mediators. Dynamic Bayesian Network inference revealed IL-23 as a central node in each network regardless of age or genotype, with MIG and IP-10 also as key mediators in the networks of the aged patients. These findings suggest that an aging-related SNP, rs2075650, may influence clinical outcomes and inflammation networks in aged patients following blunt trauma, and thus may serve as a predictive outcome biomarker in the setting of polytrauma

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

Critical illness, a constellation of interrelated inflammatory and physiological derangements occurring subsequent to severe infection or injury, affects a large number of individuals in both developed and developing countries. The prototypical complex system embodied in critical illness has largely defied therapy beyond supportive care. We have focused on the utility of data-driven and mechanistic computational modelling to help address the complexity of critical illness and provide pathways towards discovering potential therapeutic options and combinations. Herein, we review recent progress in this field, with a focus on both animal and computational models of critical illness. We suggest that therapy for critical illness can be posed as a model-based dynamic control problem, and discuss novel theoretical and experimental approaches involving biohybrid devices aimed at reprogramming inflammation dynamically. Together, these advances offer the potential for Model-based Precision Medicine for critical illness.

Acute inflammation following sterile injury is both inevitable and necessary to restore homeostasis and promote tissue repair. However, when excessive, inflammation can jeopardize the viability of organs and cause detrimental systemic effects. Identifying key-regulators of the immune cascade induced by surgery is vital to attenuating excessive inflammation and its subsequent effects. In this review, we describe the emerging role of IL-17A as a key-regulator in acute inflammation. The role of IL-17A in chronic disease states, such as rheumatoid arthritis, psoriasis and cancer has been well documented, but its significance in acute inflammation following surgery, sepsis, or traumatic injury has not been well studied. We aim to highlight the role of IL-17A in acute inflammation caused by trauma, liver ischemia, and organ transplantation, as well as in post-operative surgical infections. Further investigation of the roles of this cytokine in acute inflammation may stimulate novel therapies or diagnostic modalities.

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.

To define the impact of prehospital hypotension on the dynamic, systemic acute inflammatory response to blunt trauma.Retrospective study.Tertiary care institution.Twenty-two hypotensive blunt trauma patients matched with 28 normotensive blunt trauma patients.None.From a cohort of 472 blunt trauma survivors studied following institutional review board approval, two stringently matched subcohorts were derived. Twenty-two patients who sustained prehospital hypotension following blunt trauma (15 males and 7 females; age, 45 ± 3.8; Injury Severity Score, 20.7 ± 1.8) were matched with 28 normotensive trauma patients (20 males and 8 females; age, 46.1 ± 2.5; Injury Severity Score, 20.8 ± 1.3). Serial blood samples (three samples within the first 24 hr and then from days 1 to 7 postinjury) were assessed for 24 inflammatory mediators using Luminex, and No2-/No3- was measured using the nitrate reductase/Griess assay. Two-way analysis of variance was used to compare groups. Dynamic Bayesian Network inference was used to infer causal relationships based on probabilistic measures. Statistically significant differences were observed in ICU length of stay, total length of stay, days on mechanical ventilator, and Marshall Multiple Organ Dysfunction score between hypotensive and normotensive patients. Shock markers (shock index, pH, lactate, and base deficit) were significantly altered in hypotensive patients. Plasma levels of chemokines (monocyte chemotactic protein-1/CCL2, inducible protein-10/CXCL10, macrophage inflammatory protein-1α/CCL3, and interleukin-8/CCL8) and cytokines (interleukin-6, interleukin-10, interleukin-17, granulocyte-macrophage colony-stimulating factor, interleukin-1β, and interleukin-7) as well as soluble interleukin-2 receptor-α were significantly elevated over the first 7 days postinjury in the hypotensive versus normotensive patients. Dynamic Bayesian Network suggested that the chemokines monocyte chemotactic protein-1/CCL2 and monokine induced by gamma interferon/CXCL9 in the hypotensive and normotensive patients, respectively, affect plasma interleukin-6 levels differentially in the initial 24 hours postinjury.Studies in stringently matched patient cohorts suggest that an episode of prehospital hypotension post trauma leads to early, dynamic reprogramming of systemic inflammation (including differential upstream regulation of interleukin-6), which is associated with worse outcomes.

Clinical research characterizing the mechanisms responsible for sex-based outcome differences after injury remains conflicting. Currently lacking is an understanding of the early sex hormone milieu of the injured patient and the effects these early hormone differences have on clinical outcomes and the innate immune response following injury.A prospective cohort study was performed over a 20-month period. Blunt injury patients requiring intensive care unit admission were enrolled. Samples were collected within 6 hours and at 24 hours after injury and were analyzed for total testosterone (TT) and estradiol concentrations. Outcomes of interest included multiple-organ failure (MOF; Marshall Multiple Organ Dysfunction Score [MODScore]5), nosocomial infection (NI), mortality, and serial cytokine/chemokine measurements. Multivariate logistic regression was used to determine the independent risks associated with early sex hormone measurements.In 288 prospectively enrolled patients, 69% were male, with a median Injury Severity Score (ISS) of 16 (interquartile range 10-21). Elevated TT levels at 6 hours were associated with elevated interleukin 6 levels and cytokine/chemokine measurements (18 of 24 measured). Rising TT levels were significantly associated with more than a fivefold and twofold higher independent risk of MOF and NI, respectively (odds ratio [OR], 5.2; p = 0.02; 95% confidence interval [CI], 1.2-22.3; and OR, 2.1; p = 0.03; 95% CI, 1.02-4.2). At 24 hours, TT levels were no longer associated with poor outcome, while estradiol levels were significantly associated with nearly a fourfold higher independent risk of MOF (OR, 3.9; p = 0.04, 95% CI, 1.05-13).Early elevations and increasing testosterone levels over initial 24 hours after injury are associated with an exaggerated inflammatory response and a significantly greater risk of MOF and NI. High estrogen levels at 24 hours are independently associated with an increased risk of MOF. The current analysis suggests that an early evolving testosterone to estrogen hormonal environment is associated with a significantly higher independent risk of poor outcome following traumatic injury.Prognostic/epidemiologic study, level II.

Trauma combined with hemorrhagic shock (HS/T) leads to systemic inflammation that results in organ injury. Toll-like Receptor 4 (TLR4)-signaling activation contributes to the initiation of inflammatory pathways following HS/T but its cell-specific roles in this setting are not known. We assessed the importance of TLR4 on leukocytes of myeloid lineage and dendritic cells (DC) to the early systemic inflammatory response following HS/T. Mice were subjected to HS/T and 20 inflammatory mediators were measured in plasma followed by Dynamic Bayesian Network (DBN) Analysis. Organ damage was assessed by histology and plasma ALT levels. The role of TLR4 was determined using TLR4-/-, MyD88-/-, and Trif-/- C57BL/6 (B6) mice, and by in vivo administration of a TLR4-specific neutralizing monoclonal antibody (mAb). The contribution of TLR4 expressed by myeloid leukocytes and DC was determined by generating cell-specific TLR4-/- B6 mice, including Lyz-Cre x TLR4loxP/loxP, and CD11c-Cre x TLR4loxP/loxP B6 mice. Adoptive transfer of bone marrow-derived TLR4+/+ or TLR4-/- DC into TLR4-/- mice confirmed the contribution of TLR4 on DC to the systemic inflammatory response after HS/T. Using both global knockout mice and the TLR4 blocking mAb 1A6 we established a central role for TLR4 in driving systemic inflammation. Using cell-selective TLR4-/- B6 mice we found that TLR4 expression on both myeloid cells and CD11chigh DC is required for increases in systemic cytokine levels and organ damage after HS/T. We confirmed the capacity of TLR4 on CD11chigh DC to promote inflammation and liver damage using adoptive transfer of TLR4+/+ conventional (CD11chigh) DC into TLR4-/- mice. DBN inference identified CXC chemokines as proximal drivers of dynamic changes in the circulating levels of cytokines/chemokines after HS/T. TLR4 on DC was found to contribute selectively to the elevations in these proximal drivers. TLR4 on both myeloid cells and conventional DC is required for the initial systemic inflammation and organ damage in a mouse model of HS/T. This includes a role for TLR4 on DC in promoting increases in the early inflammatory networks identified in HS/T. These data establish DC along with macrophages as essential to the recognition of tissue damage and stress following tissue trauma with hemorrhagic shock.

Background The immunosuppression and immune dysregulation that follows severe injury includes type 2 immune responses manifested by elevations in interleukin (IL) 4, IL5, and IL13 early after injury. We hypothesized that IL33, an alarmin released early after tissue injury and a known regulator of type 2 immunity, contributes to the early type 2 immune responses after systemic injury. Methods and findings Blunt trauma patients admitted to the trauma intensive care unit of a level I trauma center were enrolled in an observational study that included frequent blood sampling. Dynamic changes in IL33 and soluble suppression of tumorigenicity 2 (sST2) levels were measured in the plasma and correlated with levels of the type 2 cytokines and nosocomial infection. Based on the observations in humans, mechanistic experiments were designed in a mouse model of resuscitated hemorrhagic shock and tissue trauma (HS/T). These experiments utilized wild-type C57BL/6 mice, IL33-/- mice, B6.C3(Cg)-Rorasg/sg mice deficient in group 2 innate lymphoid cells (ILC2), and C57BL/6 wild-type mice treated with anti-IL5 antibody. Severely injured human blunt trauma patients (n = 472, average injury severity score [ISS] = 20.2) exhibited elevations in plasma IL33 levels upon admission and over time that correlated positively with increases in IL4, IL5, and IL13 (P < 0.0001). sST2 levels also increased after injury but in a delayed manner compared with IL33. The increases in IL33 and sST2 were significantly greater in patients that developed nosocomial infection and organ dysfunction than similarly injured patients that did not (P < 0.05). Mechanistic studies were carried out in a mouse model of HS/T that recapitulated the early increase in IL33 and delayed increase in sST2 in the plasma (P < 0.005). These studies identified a pathway where IL33 induces ILC2 activation in the lung within hours of HS/T. ILC2 IL5 up-regulation induces further IL5 expression by CXCR2+ lung neutrophils, culminating in early lung injury. The major limitations of this study are the descriptive nature of the human study component and the impact of the potential differences between human and mouse immune responses to polytrauma. Also, the studies performed did not permit us to make conclusions about the impact of IL33 on pulmonary function. Conclusions These results suggest that IL33 may initiate early detrimental type 2 immune responses after trauma through ILC2 regulation of neutrophil IL5 production. This IL33–ILC2–IL5–neutrophil axis defines a novel regulatory role for ILC2 in acute lung injury that could be targeted in trauma patients prone to early lung dysfunction., In a reverse translation study from a human cohort to a mouse trauma model, Timothy Billiar and colleagues investigate the factors mediating immune dysregulation after severe injury., Author summary Why was this study done? The factors that initiate immune dysregulation after severe injury, including the skewing of the immune system towards type 2 responses, are unknown. Our aim was to determine if interleukin (IL) 33, an alarmin released after tissue injury and a regulator of type 2 immunity, contributes to the early type 2 immune responses after systemic injury. What did the researchers do and find? We measured IL33 and its soluble receptor, soluble suppression of tumorigenicity 2 (sST2), an endogenous antagonist of IL33, in the blood of 472 multiply injured trauma patients. IL33 levels were already markedly elevated on the first blood draw after injury and correlated with increases in IL4, IL5, and IL13 as well as nosocomial infections. Reverse translation experiments using a mouse model of hemorrhagic shock and tissue trauma were carried out to understand the functional link between IL33 and the type 2 cytokine, IL5. IL33 was shown to activate a resident set of innate lymphocytes (group 2 innate lymphoid cells [ILC2]) in the lungs to produce IL5, further leading to neutrophil IL5 production. What do these findings mean? These findings demonstrate the utility of using reverse translation of observations made in humans into mechanistic experimental models that recapitulate key components of the clinical response. IL33 and one of its target cells, ILC2, are identified for the first time as regulators of the early type 2 immune responses after severe injury. The findings include a previously unsuspected role for ILC2 in the regulation of neutrophils and acute lung injury. The findings identify IL33, IL5, or their receptors as potential targets to block early lung injury after polytrauma.

The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.

Clinical research characterizing the mechanisms responsible for sex-based outcome differences postinjury remain conflicting. We sought to characterize an X chromosome-linked IRAK-1 (IL-1 receptor-associated kinase) polymorphism as an alternative mechanism responsible for sex differences postinjury. IRAK-1 is key intermediate in the toll-like receptor (TLR) pathway thought to drive inflammation postinjury.A prospective cohort study was performed over a 24-month period. Bluntly injured patients requiring intensive care unit admission were enrolled, whereas patients with isolated brain and spinal cord injuries were excluded. Outcomes of interest included multiple organ failure (MOF, Marshall MOD score5) and mortality. Logistic regression was utilized to determine the independent risk of poor outcome associated with the IRAK-1 variant after controlling for important differences.In an enrolled cohort of 321 patients, the IRAK-1 variant was common (12.5%). Patients with and without the variant were similar in age, injury severity, and 24hr blood transfusion. After controlling for important confounders, the IRAK1 variant was independently associated with more than eightfold (OR = 8.4, P = 0.005, 95% CI: 1.9-37.1) and 11-fold (OR = 11.8, P = 0.037, 95% CI: 1.1-121) greater risk of MOF and mortality, respectively. These differences were most prominent in men, whereas women heterozygous for the variant demonstrated worse outcome in a dose-dependent fashion.The IRAK1 polymorphism is a strong independent predictor of MOF and mortality postinjury and represents a common variant with prognostic potential. These data demonstrate the importance of TLR signaling postinjury and supports that a genetic mechanism may drive sex outcome differences postinjury.

An excessive and uncontrolled systemic inflammatory response is associated with organ failure, immunodepression, and increased susceptibility to nosocomial infection following trauma. Interleukin 6 (IL-6) plays a particularly prominent role in the host immune response after trauma with hemorrhage. However, as a result of its pleiotropic functions, the effect of IL-6 in trauma and hemorrhage is still controversial. It remains unclear whether suppression of IL-6 after hemorrhagic shock and trauma will attenuate organ injury and immunosuppression. In this study, C57BL/6 mice were treated with anti-mouse IL-6 monoclonal antibody immediately prior to resuscitation in an experimental model combining hemorrhagic shock and lower-extremity injury. Interleukin 6 levels and signaling were transiently suppressed following administrations of anti-IL-6 monoclonal antibody following hemorrhagic shock and lower-extremity injury. This resulted in reduced lung and liver injury, as well as suppression in the levels of key inflammatory mediators including IL-10, keratinocyte-derived chemokine, monocyte chemoattractant protein 1, and macrophage inhibitory protein 1α at both 6 and 24 h. Furthermore, the shift to TH2 cytokine production and suppressed lymphocyte response were partly prevented. These results demonstrate that IL-6 is not only a biomarker but also an important driver of injury-induced inflammation and immune suppression in mice. Rapid measurement of IL-6 levels in the early phase of postinjury care could be used to guide IL-6-based interventions.

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.

Background Aging is accompanied by alterations in immune functions. How these changes translate into levels of circulating inflammatory mediators and network expression after severe trauma is not well characterized. To address this, we compared time-dependent changes in the levels of an extensive biomarker panel in cohorts of severely injured young and aged adults. Study Design Cohorts of young (18 to 30 years old, n = 115) and aged (65 to 90 years old, n = 101) blunt trauma patients admitted to the ICU with plasma sampled 3 times within the first 24 hours and daily from day 1 to day 7 were assayed for 30 inflammatory biomarkers using Luminex analyzer. Stringently matched groups controlling for sex ratio and Injury Severity Score (n = 56 young vs n = 56 aged) were generated. Data were analyzed using 2-way ANOVA, area under the curve analysis, Dynamic Bayesian Network inference, and Dynamic Network Analysis. Results In the overall cohorts, the young group had a significantly higher Injury Severity Score, which was associated with higher circulating levels of 18 inflammatory mediators from admission to day 7. The aged group had higher levels of C-X-C motif chemokine ligand 10/interferon gamma-induced protein 10 and C-X-C motif chemokine ligand 9/monokine induced by gamma interferon. In groups that were matched for Injury Severity Score, the significantly higher levels of interferon gamma-induced protein 10 and monokine induced by gamma interferon persisted in the aged. Dynamic Bayesian Network revealed interferon gamma-induced protein 10 and monokine induced by gamma interferon as key mediators in the aged, and Dynamic Network Analysis revealed higher network complexity in the aged. Conclusions These findings indicate that differences in the early inflammatory networks between young and aged trauma patients are not simply a suppression of pro-inflammatory responses in the aged, but are characterized by a major shift in the mediator profile patterns with high levels of CXC chemokines in the aged.

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon.

Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients.To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing.We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models.A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment.The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.PubMed search and review of relevant medical literature.Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.

Inflammation is a critical component in the body's response to injury. A dysregulated inflammatory response, in which either the injury is not repaired or the inflammatory response does not appropriately self-regulate and end, is associated with a wide range of inflammatory diseases such as sepsis. Clinical management of sepsis is a significant problem, but progress in this area has been slow. This may be due to the inherent nonlinearities and complexities in the interacting multiscale pathways that are activated in response to systemic inflammation, motivating the application of systems biology techniques to better understand the inflammatory response. Here, we review our past work on a multiscale modeling approach applied to human endotoxemia, a model of systemic inflammation, consisting of a system of compartmentalized differential equations operating at different time scales and through a discrete model linking inflammatory mediators with changing patterns in the beating of the heart, which has been correlated with outcome and severity of inflammatory disease despite unclear mechanistic underpinnings. Working towards unraveling the relationship between inflammation and heart rate variability (HRV) may enable greater understanding of clinical observations as well as novel therapeutic targets.

SummaryTissue stress and cell death result in inflammation even in the absence of pathogens. Such sterile inflammation is dependent on a cytosolic complex of proteins inside immune cells termed the inflammasome. This complex converts two groups of extracellular signals into an inflammatory response via activation of caspase-1 and secretion of IL-1β and IL-18. Group 1 signals are typically TOLL like receptor agonists and result in transcriptional upregulation of inflammasome components and pro-cytokines. Group 2 signals are diverse, ranging from uric acid to ATP, and lead to assembly and activation of the inflammasome complex. Inflammasome components are required for a wide range of acute and chronic pathologies, including experimental alcoholic and non-alcoholic steatohepatitis, and drug-induced liver injury. Collectively, group 1 and 2 signals, inflammasome components, and cytokine receptors provide a rich source of therapeutic targets. Many of the advances in the field have come from standard reductionist experiments. Progress in the understanding of complex human systems will, however, be dependent on novel strategies such as systems analysis, which analyze large data sets to provide new insights.

Background: Ex vivo machine perfusion (MP) can better preserve organs for transplantation. We have recently reported on the first application of an MP protocol in which liver allografts were fully oxygenated, under dual pressures and subnormothermic conditions, with a new hemoglobin-based oxygen carrier (HBOC) solution specifically developed for ex vivo utilization. In those studies, MP improved organ function post-operatively and reduced inflammation in porcine livers. Herein, we sought to refine our knowledge regarding the impact of MP by defining dynamic networks of inflammation in both tissue and perfusate. Methods: Porcine liver allografts were preserved either with MP (n = 6) or with cold static preservation (CSP; n = 6), then transplanted orthotopically after 9 h of preservation. Fourteen inflammatory mediators were measured in both tissue and perfusate during liver preservation at multiple time points, and analyzed using Dynamic Bayesian Network (DyBN) inference to define feedback interactions, as well as Dynamic Network Analysis (DyNA) to define the time-dependent development of inflammation networks. Results: Network analyses of tissue and perfusate suggested an NLRP3 inflammasome-regulated response in both treatment groups, driven by the pro-inflammatory cytokine interleukin (IL)-18 and the anti-inflammatory mediator IL-1 receptor antagonist (IL-1RA). Both DyBN and DyNA suggested a reduced role of IL-18 and increased role of IL-1RA with MP, along with increased liver damage with CSP. DyNA also suggested divergent progression of responses over the 9 h preservation time, with CSP leading to a stable pattern of IL-18-induced liver damage and MP leading to a resolution of the pro-inflammatory response. These results were consistent with prior clinical, biochemical, and histological findings after liver transplantation. Conclusion: Our results suggest that analysis of dynamic inflammation networks in the setting of liver preservation may identify novel diagnostic and therapeutic modalities.

Inflammation induced by traumatic brain injury (TBI) is complex, individual-specific, and associated with morbidity and mortality. We sought to develop dynamic, data-driven, predictive computational models of TBI-induced inflammation based on cerebrospinal fluid (CSF) biomarkers. Thirteen inflammatory mediators were determined in serial CSF samples from 27 severe TBI patients. The Glasgow Coma Scale (GCS) score quantifies the initial severity of the neurological status of the patient on a numerical scale from 3 to 15. The 6-month Glasgow Outcome Scale (GOS) score, the outcome variable, was taken as the variable to express and predict as a function of the other input variables. Data on each subject consisting of ten clinical (one-dimensional) variables, such as age, gender, and presence of infection, along with inflammatory biomarker time series were used to generate both multinomial logistic as well as probit models that predict low (poor outcome) or high (favorable outcome) levels of the GOS score. To determine if CSF inflammation biomarkers could predict TBI outcome, a logistic model for low (≤3; poor neurological outcome) or high levels (≥4; favorable neurological outcome) of the GOS score involving a full effect of the pro-inflammatory cytokine tumor necrosis factor-α and both linear and quadratic effects of the anti-inflammatory cytokine interleukin-10 was obtained. To better stratify patients as their pathology progresses over time, a technique called “Dynamic Profiling” was developed in which patients were clustered, using the spectral Laplacian and Hartigan’s k-means method, into disjoint groups at different stages. Initial clustering was based on GCS score; subsequent clustering was performed based on clinical and demographic information and then further, sequential clustering based on the levels of individual inflammatory mediators over time. These clusters assess the risk of mortality of a new patient after each inflammatory mediator reading, based on the existing information in the previous data in the cluster to which the new patient belongs at the time, in essence acting as a “virtual clinician.” Using the Dynamic Profiling method, we show examples that suggest that severe TBI patient neurological outcomes could be predicted as a function of time post-TBI using CSF inflammatory mediators.

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.Retrospective clinical study and experimental study in mice.Level 1 trauma center and experimental laboratory.From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24 hours but ultimately died (nonsurvivors; n = 19) and patients who, after ICU admission, went on to be discharged(survivors; n = 19).None in patients. Neutralizing anti-interleukin-17A antibody in mice.Data on systemic inflammatory mediators assessed within the first 24 hours and over 7 days were analyzed with computational modeling to infer dynamic networks of inflammation. Network density among inflammatory mediators in nonsurvivors increased in parallel with organ dysfunction scores over 7 days, suggesting the presence of early, self-sustaining, pathologic inflammation involving high-mobility group protein B1, interleukin-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine interleukin-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-interleukin-17A.Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality after blunt trauma and suggest a lymphoid cell-based switch from self-resolving to self-sustaining inflammation.

Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

We hypothesized that elevated base deficit (BD) ≥ 4 mEq/L upon admission could be associated with an altered inflammatory response, which in turn may impact differential clinical trajectories. Using clinical and biobank data from 472 blunt trauma survivors, 154 patients were identified after excluding patients who received prehospital IV fluids or had alcohol intoxication. From this subcohort, 84 patients had a BD ≥ 4 mEq/L and 70 patients with BD < 4 mEq/L. Three samples within the first 24 h were obtained from all patients and then daily up to day 7 after injury. Twenty-two cytokines and chemokines were assayed using Luminex™ and were analyzed using two-way ANOVA and dynamic network analysis (DyNA). Multiple mediators of the innate and lymphoid immune responses in the BD ≥ 4 group were elevated differentially upon admission and up to 16 h after injury. DyNA revealed a higher, sustained degree of interconnectivity of the inflammatory response in the BD ≥ 4 patients during the initial 16 h after injury. These results suggest that elevated admission BD is associated with differential immune/inflammatory pathways, which subsequently could predispose patients to follow a complicated clinical course.

Background Severe traumatic injury can lead to immune dysfunction that renders trauma patients susceptible to nosocomial infections (NI) and prolonged intensive care unit (ICU) stays. We hypothesized that early circulating biomarker patterns following trauma would correlate with sustained immune dysregulation associated with NI and remote organ failure. Methods In a cohort of 472 blunt trauma survivors studied over an 8-year period, 127 patients (27%) were diagnosed with NI versus 345 trauma patients without NI. To perform a pairwise, case-control study with 1:1 matching, 44 of the NI patients were compared with 44 no-NI trauma patients selected by matching patient demographics and injury characteristics. Plasma obtained upon admission and over time were assayed for 26 inflammatory mediators and analyzed for the presence of dynamic networks. Results Significant differences in ICU length of stay (LOS), hospital LOS, and days on mechanical ventilation were observed in the NI patients versus no-NI patients. Although NI was not detected until day 7, multiple mediators were significantly elevated within the first 24 hours in patients who developed NI. Circulating inflammation biomarkers exhibited 4 distinct dynamic patterns, of which 2 clearly distinguish patients destined to develop NI from those who did not. Mediator network connectivity analysis revealed a higher, coordinated degree of activation of both innate and lymphoid pathways in the NI patients over the initial 24 hours. Conclusions These studies implicate unique dynamic immune responses, reflected in circulating biomarkers that differentiate patients prone to persistent critical illness and infections following injury, independent of mechanism of injury, injury severity, age, or sex.

Improper compartmentalization of the inflammatory response leads to systemic inflammation in sepsis. Hemoadsorption (HA) is an emerging approach to modulate sepsis-induced inflammation. We sought to define the effects of HA on inflammatory compartmentalization in Escherichia coli-induced fibrin peritonitis in rats.HA both reprograms and recompartmentalizes inflammation in sepsis. Sprague Dawley male rats were subjected to E. coli peritonitis and, after 24 h, were randomized to HA or sham treatment (sepsis alone). Venous blood samples collected at 0, 1, 3 and 6 h (that is, 24-30 h of total experimental sepsis), and peritoneal samples collected at 0 and 6 h, were assayed for 14 cytokines along with NO(2)(-/)NO(3)(-). Bacterial counts were assessed in the peritoneal fluid at 0 and 6 h. Plasma tumor necrosis factor (TNF)-α, interleukin (IL)-6, CXCL-1, and CCL2 were significantly reduced in HA versus sham. Principal component analysis (PCA) suggested that inflammation in sham was driven by IL-6 and TNF-α, whereas HA-associated inflammation was driven primarily by TNF-α, CXCL-1, IL-10 and CCL2. Whereas -peritoneal bacterial counts, plasma aspartate transaminase levels and peritoneal IL-5, IL-6, IL-18, interferon (IFN)-γ and NO(2)(-)/NO(3)(-) were significantly lower, both CXCL-1 and CCL2 as well as the peritoneal-to-plasma ratios of TNF-α, CXCL-1 and CCL2 were significantly higher in HA versus sham, suggesting that HA-induced inflammatory recompartmentalization leads to the different inflammatory drivers discerned in part by PCA. In conclusion, this study demonstrates the utility of combined in vivo/in silico methods and suggests that HA exerts differential effects on mediator gradients between local and systemic compartments that ultimately benefit the host.

Properly regulated inflammation facilitates recognition and reaction to injury or infection, but inadequate or overly robust inflammation can lead to disease. Sepsis is an inflammatory disease that accounts for nearly 10% of total U.S. deaths, costing more than $17 billion. Acute inflammation in sepsis may evolve too rapidly to be modulated appropriately, and we suggest that therapies should focus not on abolishing inflammation, but rather on attenuating the positive feedback cycle of inflammation/damage/inflammation. In Gram-negative sepsis, bacterial endotoxin causes inflammation and is driven and regulated by the cytokine tumor necrosis factor-α (TNF-α), which is, in turn, negatively regulated via its endogenous inhibitor, soluble TNF-α receptor (sTNFR). We generated stably gene-modified variants of human HepG2 hepatocytes, using lentiviral constructs coding for mouse sTNFR driven by the constitutive cytomegalovirus promoter, and seeded them in a scaled-down, experimental liver bioreactor. When connected to anesthetized, cannulated rats subjected to endotoxin infusion and maintained solely by the animals’ circulation, this biohybrid device elevated circulating sTNFR, reduced the levels of TNF-α and other key inflammatory mediators, alleviated hypotension, and reduced circulating markers of organ damage. This novel class of biohybrid devices may bemodified for patient- and disease-specific application, and, thus, may represent a disruptive strategy that offers the potential for rational inflammation reprogramming.

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.

Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation., Author summary Gram-negative bacterial lipopolysaccharide (LPS) is both a central mediator of sepsis and a canonical inducer of acute inflammation via Toll-like receptor 4 (TLR4). Sepsis involves the systemic spillover of inflammation that normally remains localized in individual organs. The goal of this study was to gain insights into 1) early vs. later drivers of LPS-induced inflammation in various compartments, and 2) the systemic spillover from affected organs vs. local production of inflammatory mediators in the blood. This study involved a large number of data points on the dynamics of inflammatory mediators at the protein level, data-driven computational modeling of principal characteristics and cross-correlations, and validation of key hypotheses. In addition to verifying key mechanisms in LPS/TLR4-driven acute inflammation, this approach yielded key insights into the progression of inflammation across tissues, and also suggested the presence of TLR4-independent pathways (especially in the gut). This is, to our knowledge, the first study examining the dynamic evolution of some key inflammatory mediators and their interactions with each other in both the systemic circulation and within a number of targeted parenchymal organs in mice.

Inflammation is a complex, non-linear process central to many of the diseases that affect both developed and emerging nations. A systems-based understanding of inflammation, coupled to translational applications, is therefore necessary for efficient development of drugs and devices, for streamlining analyses at the level of populations, and for the implementation of personalized medicine. We have carried out an iterative and ongoing program of literature analysis, generation of prospective data, data analysis, and computational modeling in various experimental and clinical inflammatory disease settings. These simulations have been used to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals for in silico studies associated with the clinical settings of sepsis, trauma, acute liver failure, and wound healing to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation; and to gain insight into host-pathogen interactions in malaria, necrotizing enterocolitis, and sepsis. These simulations have converged with other systems biology approaches (e.g., functional genomics) to aid in the design of new drugs or devices geared towards modulating inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ impacts via tissue damage/dysfunction. This framework has now allowed us to suggest how to modulate acute inflammation in a rational, individually optimized fashion. This plethora of computational and intertwined experimental/engineering approaches is the cornerstone of Translational Systems Biology approaches for inflammatory diseases.