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Abstract Background Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting. Methods We sought to bridge this gap by linking preclinical data in a porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation. Results The mathematical model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time. Conclusions This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.

Abstract A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

As populations worldwide show increasing levels of stress, understanding emerging links among stress, inflammation, cognition, and behavior is vital to human and planetary health. We hypothesize that inflammation is a multiscale driver connecting stressors that affect individuals to large-scale societal dysfunction and, ultimately, to planetary-scale environmental impacts. We propose a “central inflammation map” hypothesis to explain how the brain regulates inflammation and how inflammation impairs cognition, emotion, and action. According to our hypothesis, these interdependent inflammatory and neural processes, and the inter-individual transmission of environmental, infectious, and behavioral stressors—amplified via high-throughput digital global communications—can culminate in a multiscale, runaway, feed-forward process that could detrimentally affect human decision-making and behavior at scale, ultimately impairing the ability to address these same stressors. This perspective could provide non-intuitive explanations for behaviors and relationships among cells, organisms, and communities of organisms, potentially including population-level responses to stressors as diverse as global climate change, conflicts, and the COVID-19 pandemic. To illustrate our hypothesis and elucidate its mechanistic underpinnings, we present a mathematical model applicable to the individual and societal levels to test the links among stress, inflammation, control, and healing, including the implications of transmission, intervention (e.g., via lifestyle modification or medication), and resilience. Future research is needed to validate the model’s assumptions and conclusions against empirical benchmarks and to expand the factors/variables employed. Our model illustrates the need for multilayered, multiscale stress mitigation interventions, including lifestyle measures, precision therapeutics, and human ecosystem design. Our analysis shows the need for a coordinated, interdisciplinary, international research effort to understand the multiscale nature of stress. Doing so would inform the creation of interventions that improve individuals’ lives; enhance communities’ resilience to stress; and mitigate the adverse effects of stress on the world.

Introduction: The vagus nerve innervates multiple organs, but its role in regulating cross-tissue spread of inflammation is as yet unclear. We hypothesized that the vagus nerve may regulate cross-tissue inflammation via modulation of the putatively neurally regulated chemokine IP-10/CXCL10.Methods: Rate-of-change analysis, dynamic network analysis, and dynamic hypergraphs were used to model intra- and inter-tissue trends, respectively, in inflammatory mediators from mice that underwent either vagotomy or sham surgery.Results: This analysis suggested that vagotomy primarily disrupts the cross-tissue attenuation of inflammatory networks involving IP-10 as well as the chemokines MIG/CXCL9 and CCL2/MCP-1 along with the cytokines IFN-γ and IL-6. Computational analysis also suggested that the vagus-dependent rate of expression of IP-10 and MIG/CXCL9 in the spleen impacts the trajectory of chemokine expression in other tissues. Perturbation of this complex system with bacterial lipopolysaccharide (LPS) revealed a vagally regulated role for MIG in the heart. Further, LPS-stimulated expression of IP-10 was inferred to be vagus-independent across all tissues examined while reducing connectivity to IL-6 and MCP-1, a hypothesis supported by Boolean network modeling.Discussion: Together, these studies define novel spatiotemporal dimensions of vagus-regulated acute inflammation.

Objectives: The aims of the study were to (1) describe types of pain in cancer patients, (2) examine the predictors and consequences of pain, (3) investigate the association between type of pain and survival, and (4) examine potential biological mediators of pain and survival. Methods: This was a secondary analysis of baseline data from patients diagnosed with cancer. Patients answered questionnaires that assessed sociodemographic characteristics, pain, depression, sleep, and fatigue. Blood was collected and cytokine assays were performed. Analysis of variance, Kaplan–Meier, and Cox regression survival analyses were used to test the aims. Results: Of the 779 patients diagnosed with cancer, the mean age was 63.5 years, 57.8% male, and 90.6% White. Of those who reported pain (total 70.3%), 46.5% stated their pain was cancer-related while 53.5% stated their pain was non-cancer-related. While both cancer and non-cancer-related pain was associated with depressive symptoms, fatigue, and sleep duration, those with cancer-related pain had significantly higher rates of depressive symptoms (F(1,516) = 21.217, p < 0.001) and fatigue (F(1,516) = 30.973, p < 0.001) but not poorer sleep (F(1,497) = 0.597, p = 0.440). After adjusting for sociodemographic, disease-related characteristics, depression, sleep duration, and morphine milligram equivalent, patient reports of cancer-related pain were significantly associated with poorer survival (HR = 0.646, 95% CI = 0.459–0.910, p = 0.012) compared to those with non-cancer-related pain, which was not associated with survival (HR = 1.022, 95% CI = 0.737–1.418, p = 0.896). Cytokines did not significantly mediate the link between pain and survival. Conclusion: While nearly half of the pain reported was cancer-related, both types of pain resulted in greater symptom burden, but only cancer-related pain was associated with survival.

Abstract Dynamic Network Analysis (DyNA) and Dynamic Hypergraphs (DyHyp) were used to define protein-level inflammatory networks at the local (wound effluent) and systemic circulation (serum) levels from 140 active-duty, injured service members (59 with TBI and 81 non-TBI). Interleukin (IL)-17A was the only biomarker elevated significantly in both serum and effluent in TBI vs. non-TBI casualties, and the mediator with the most DyNA connections in TBI wounds. DyNA combining serum and effluent data to define cross-compartment correlations suggested that IL-17A bridges local and systemic circulation at late time points. DyHyp suggested that systemic IL-17A upregulation in TBI patients was associated with tumor necrosis factor-α, while IL-17A downregulation in non-TBI patients was associated with interferon-γ. Correlation analysis suggested differential upregulation of pathogenic Th17 cells, non-pathogenic Th17 cells, and memory/effector T cells. This was associated with reduced procalcitonin in both effluent and serum of TBI patients, in support of an antibacterial effect of Th17 cells in TBI patients. Dysregulation of Th17 responses following TBI may drive cross-compartment inflammation following combat injury, counteracting wound infection at the cost of elevated systemic inflammation.

Summary: Acute inflammation is heterogeneous in critical illness and predictive of outcome. We hypothesized that genetic variability in novel, yet common, gene variants contributes to this heterogeneity and could stratify patient outcomes. We searched algorithmically for significant differences in systemic inflammatory mediators associated with any of 551,839 SNPs in one derivation (n = 380 patients with blunt trauma) and two validation (n = 75 trauma and n = 537 non-trauma patients) cohorts. This analysis identified rs10404939 in the LYPD4 gene. Trauma patients homozygous for the A allele (rs10404939AA; 27%) had different trajectories of systemic inflammation along with persistently elevated multiple organ dysfunction (MOD) indices vs. patients homozygous for the G allele (rs10404939GG; 26%). rs10404939AA homozygotes in the trauma validation cohort had elevated MOD indices, and non-trauma patients displayed more complex inflammatory networks and worse 90-day survival compared to rs10404939GG homozygotes. Thus, rs10404939 emerged as a common, broadly prognostic SNP in critical illness.

BACKGROUND:. Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown. METHODS AND RESULTS:. The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium–heparin, EDTA, sodium fluoride, and citrate tubes. CONCLUSIONS:. The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.

Abstract Background Peripheral artery disease (PAD) affects over 230 million people worldwide and is due to systemic atherosclerosis with etiology linked to chronic inflammation, hypertension, and smoking status. PAD is associated with walking impairment and mobility loss as well as a high prevalence of coronary and cerebrovascular disease. Intermittent claudication (IC) is the classic presenting symptom for PAD, although many patients are asymptomatic or have atypical presentations. Few effective medical therapies are available, while surgical and exercise therapies lack durability. Metformin, the most frequently prescribed oral medication for Type 2 diabetes, has salient anti-inflammatory and promitochondrial properties. We hypothesize that metformin will improve function, retard the progression of PAD, and improve systemic inflammation and mitochondrial function in non-diabetic patients with IC. Methods 200 non-diabetic Veterans with IC will be randomized 1:1 to 180-day treatment with metformin extended release (1000 mg/day) or placebo to evaluate the effect of metformin on functional status, PAD progression, cardiovascular disease events, and systemic inflammation. The primary outcome is 180-day maximum walking distance on the 6-min walk test (6MWT). Secondary outcomes include additional assessments of functional status (cardiopulmonary exercise testing, grip strength, Walking Impairment Questionnaires), health related quality of life (SF-36, VascuQoL), macro- and micro-vascular assessment of lower extremity blood flow (ankle brachial indices, pulse volume recording, EndoPAT), cardiovascular events (amputations, interventions, major adverse cardiac events, all-cause mortality), and measures of systemic inflammation. All outcomes will be assessed at baseline, 90 and 180 days of study drug exposure, and 180 days following cessation of study drug. We will evaluate the primary outcome with linear mixed-effects model analysis with covariate adjustment for baseline 6MWT, age, baseline ankle brachial indices, and smoking status following an intention to treat protocol. Discussion MOBILE IC is uniquely suited to evaluate the use of metformin to improve both systematic inflammatory responses, cellular energetics, and functional outcomes in patients with PAD and IC. Trial Registration: The prospective MOBILE IC trial was publicly registered (NCT05132439) November 24, 2021.

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis.

Alterations in lipid metabolism and circulating lipid species have been reported in patients with acute critical illness. Here the authors show that selective rise in systemic phosphatidylethanolamine levels is a common feature of critical illness that associates with worse clinical outcomes.

IntroductionVascularized composite allotransplantation (VCA), with nerve repair/coaptation (NR) and tacrolimus (TAC) immunosuppressive therapy, is used to repair devastating traumatic injuries but is often complicated by inflammation spanning multiple tissues. We identified the parallel upregulation of transcriptional pathways involving chemokine signaling, T-cell receptor signaling, Th17, Th1, and Th2 pathways in skin and nerve tissue in complete VCA rejection compared to baseline in 7 human hand transplants and defined increasing complexity of protein-level dynamic networks involving chemokine, Th1, and Th17 pathways as a function of rejection severity in 5 of these patients. We next hypothesized that neural mechanisms may regulate the complex spatiotemporal evolution of rejection-associated inflammation post-VCA.MethodsFor mechanistic and ethical reasons, protein-level inflammatory mediators in tissues from Lewis rats (8 per group) receiving either syngeneic (Lewis) or allogeneic (Brown-Norway) orthotopic hind limb transplants in combination with TAC, with and without sciatic NR, were compared to human hand transplant samples using computational methods.ResultsIn cross-correlation analyses of these mediators, VCA tissues from human hand transplants (which included NR) were most similar to those from rats undergoing VCA + NR. Based on dynamic hypergraph analyses, NR following either syngeneic or allogeneic transplantation in rats was associated with greater trans-compartmental localization of early inflammatory mediators vs. no-NR, and impaired downregulation of mediators including IL-17A at later times.DiscussionThus, NR, while considered necessary for restoring graft function, may also result in dysregulated and mis-compartmentalized inflammation post-VCA and therefore necessitate mitigation strategies. Our novel computational pipeline may also yield translational, spatiotemporal insights in other contexts.

Introduction: Systemic acute inflammation accompanies and underlies the pathobiology of sepsis but is also central to tissue healing. We demonstrated previously the in vivo feasibility of modulating the key inflammatory mediator tumor necrosis factor-alpha (TNF-α) through the constitutive production and systemic administration of soluble TNF-α receptor (sTNFR) via a biohybrid device.Methods: We have now created multiple, stably transfected human HepG2 cell line variants expressing the mouse NF-κB/sTNFR. In vitro, these cell lines vary with regard to baseline production of sTNFR, but all have ~3.5-fold elevations of sTNFR in response to TNF-α.Results: Both constitutive and TNF-α-inducible sTNFR constructs, seeded into multicompartment, capillary-membrane liver bioreactors could reprogram dynamic networks of systemic inflammation and modulate PaO2, a key physiological outcome, in both endotoxemic and septic rats.Discussion: Thus, Control of TNF-α may drive a new generation of tunable biohybrid devices for the rational reprogramming of acute inflammation.

Severe injury is known to cause a systemic cytokine storm that is associated with adverse outcomes. However, a comprehensive assessment of the time-dependent changes in circulating levels of a broad spectrum of protein immune mediators and soluble immune mediator receptors in severely injured trauma patients remains uncharacterized. To address this knowledge gap, we defined the temporal and outcome-based patterns of 184 known immune mediators and soluble cytokine receptors in the circulation of severely injured patients. Proteomics (aptamer-based assay, SomaLogic, Inc) was performed on plasma samples drawn at 0, 24, and 72 hours (h) from time of admission from 150 trauma patients, a representative subset from the Prehospital Plasma during Air Medical Transport in Trauma Patients at Risk for Hemorrhagic Shock (PAMPer) trial. Patients were categorized into outcome groups including Early Non-Survivors (died within 72 h; ENS; n=38), Non-Resolvers (died after 72 h or required ≥7 days of intensive care; NR; n=78), and Resolvers (survivors that required < 7 days of intensive care; R; n=34), with low Injury Severity Score (ISS) patients from the Tranexamic Acid During Prehospital Transport in Patients at Risk for Hemorrhage After Injury (STAAMP) trial as controls. The major findings include an extensive release of immune mediators and cytokine receptors at time 0h that is more pronounced in ENS and NR patients. There was a selective subset of mediators elevated at 24 and 72 h to a greater degree in NR patients, including multiple cytokines and chemokines not previously described in trauma patients. These findings were validated in a quantitative fashion using mesoscale discovery immunoassays (MSD) from an external validation cohort (VC) of samples from 58 trauma patients matched for R and NR status. This comprehensive longitudinal description of immune mediator patterns associated with trauma outcomes provides a new level of characterization of the immune response that follows severe injury.

Abstract Background Bacterial lipopolysaccharide (LPS) induces a multi-organ, Toll-like receptor 4 (TLR4)-dependent acute inflammatory response. Methods Using network analysis, we defined the spatiotemporal dynamics of 20, LPS-induced, protein-level inflammatory mediators over 0–48 h in the heart, gut, lung, liver, spleen, kidney, and systemic circulation, in both C57BL/6 (wild-type) and TLR4-null mice. Results Dynamic Network Analysis suggested that inflammation in the heart is most dependent on TLR4, followed by the liver, kidney, plasma, gut, lung, and spleen, and raises the possibility of non-TLR4 LPS signaling pathways at defined time points in the gut, lung, and spleen. Insights from computational analyses suggest an early role for TLR4-dependent tumor necrosis factor in coordinating multiple signaling pathways in the heart, giving way to later interleukin-17A—possibly derived from pathogenic Th17 cells and effector/memory T cells—in the spleen and blood. Conclusions We have derived novel, systems-level insights regarding the spatiotemporal evolution acute inflammation.

Abstract Systemic inflammation is complex and likely drives clinical outcomes in critical illness such as that which ensues following severe injury. We obtained time course data on multiple inflammatory mediators in the blood of blunt trauma patients. Using dynamic network analyses, we inferred a novel control architecture for systemic inflammation: a three-way switch comprising the chemokines MCP-1/CCL2, MIG/CXCL9, and IP-10/CXCL10. To test this hypothesis, we created a logical model comprising this putative architecture. This model predicted key qualitative features of systemic inflammation in patient sub-groups, as well as the different patterns of hospital discharge of moderately vs. severely injured patients. Thus, a rational transition from data to data-driven models to mechanistic models suggests a novel, chemokine-based mechanism for control of acute inflammation in humans and points to the potential utility of this workflow in defining novel features in other complex diseases.

Trauma/hemorrhagic shock followed by resuscitation (T/HS-R) results in multi-system inflammation and organ dysfunction, in part driven by binding of damage-associated molecular pattern molecules to Toll-like Receptor 4 (TLR4). We carried out experimental T/HS-R (pseudo-fracture plus 2 h of shock followed by 0-22 h of resuscitation) in C57BL/6 (wild type [WT]) and TLR4-null (TLR4-/-) mice, and then defined the dynamics of 20 protein-level inflammatory mediators in the heart, gut, lung, liver, spleen, kidney, and systemic circulation. Cross-correlation and Principal Component Analysis (PCA) on data from the 7 tissues sampled suggested that TLR4-/- samples express multiple inflammatory mediators in a small subset of tissue compartments as compared to the WT samples, in which many inflammatory mediators were localized non-specifically to nearly all compartments. We and others have previously defined a central role for type 17 immune cells in human trauma. Accordingly, correlations between IL-17A and GM-CSF (indicative of pathogenic Th17 cells); between IL-17A and IL-10 (indicative of non-pathogenic Th17 cells); and IL-17A and TNF (indicative of memory/effector T cells) were assessed across all tissues studied. In both WT and TLR4-/- mice, positive correlations were observed between IL-17A and GM-CSF, IL-10, and TNF in the kidney and gut. In contrast, the variable and dynamic presence of both pathogenic and non-pathogenic Th17 cells was inferred in the systemic circulation of TLR4-/- mice over time, suggesting a role for TLR4 in efflux of these cells into peripheral tissues. Hypergraph analysis – used to define dynamic, cross compartment networks – in concert with PCA-suggested that IL-17A was present persistently in all tissues at all sampled time points except for its absence in the plasma at 0.5h in the WT group, supporting the hypothesis that T/HS-R induces efflux of Th17 cells from the circulation and into specific tissues. These analyses suggest a complex, context-specific role for TLR4 and type 17 immunity following T/HS-R.

Summary: Background: While bulk and single cell transcriptomic patterns in circulating leukocytes from trauma patients have been reported, how these relate to changes in open chromatin patterns remain unstudied. Here, we investigated whether single-cell ATAC-seq would provide further resolution of transcriptomic patterns that align with patient outcomes. Methods: We performed scATAC-seq on peripheral blood mononuclear cells from four trauma patients at

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated.Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients.Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020.Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers.Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

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.

Background Trauma elicits a complex inflammatory response that, among multiple presenting factors, is greatly impacted by the magnitude of injury severity. Herein, we compared the changes in circulating levels of mediators with known proinflammatory roles to those with known protective/reparative actions as a function of injury severity in injured humans.Methods Clinical and biobank data were obtained from 472 (trauma database-1 (TD-1), University of Pittsburgh) and 89 (trauma database-2 (TD-2), Indiana University) trauma patients admitted to the intensive care unit (ICU) and who survived to discharge. Injury severity was estimated based on the Injury Severity Score (ISS), and this was used as both a continuous variable and for the purpose of grouping patients into severity-based cohorts. Samples within the first 24 hours were obtained from all patients and then daily up to day 7 postinjury in TD-1. Sixteen cytokines were assayed using Luminex and were analyzed using two-way analysis of variance (p

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

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 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, 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.

Systemic inflammation ensues following traumatic injury, driving immune dysregulation and multiple organ dysfunction (MOD). While a balanced immune/inflammatory response is ideal for promoting tissue regeneration, most trauma patients exhibit variable and either overly exuberant or overly damped responses that likely drive adverse clinical outcomes. We hypothesized that these inflammatory phenotypes occur in the context of severe injury, and therefore sought to define clinically distinct endotypes of trauma patients based on their systemic inflammatory responses. Using Patient-Specific Principal Component Analysis followed by unsupervised hierarchical clustering of circulating inflammatory mediators obtained in the first 24 h after injury, we segregated a cohort of 227 blunt trauma survivors into three core endotypes exhibiting significant differences in requirement for mechanical ventilation, duration of ventilation, and MOD over 7 days. Nine non-survivors co-segregated with survivors. Dynamic network inference, Fisher Score analysis, and correlations of IL-17A with GM-CSF, IL-10, and IL-22 in the three survivor sub-groups suggested a role for type 3 immunity, in part regulated by Th17 and γδ 17 cells, and related tissue-protective cytokines as a key feature of systemic inflammation following injury. These endotypes may represent archetypal adaptive, over-exuberant, and overly damped inflammatory responses.

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.

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.

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.

Purpose: We sought to identify a MODS score parameter that highly correlates with adverse outcomes and then use this parameter to test the hypothesis that multiple severity-based MODS clusters could be identified after blunt trauma.Methods: MOD score across days (D) 2–5 was subjected to Fuzzy C-means Clustering Analysis (FCM) followed by eight Clustering Validity Indices (CVI) to derive organ dysfunction patterns among 376 blunt trauma patients admitted to the intensive care unit (ICU) who survived to discharge. Thirty-one inflammation biomarkers were assayed (Luminex™) in serial blood samples (3 samples within the first 24 h and then daily up to D 5) and were analyzed using Two-Way ANOVA and Dynamic Network analysis (DyNA).Results: The FCM followed by CVI suggested four distinct clusters based on MOD score magnitude between D2 and D5. Distinct patterns of organ dysfunction emerged in each of the four clusters and exhibited statistically significant differences with regards to in-hospital outcomes. Interleukin (IL)-6, MCP-1, IL-10, IL-8, IP-10, sST2, and MIG were elevated differentially over time across the four clusters. DyNA identified remarkable differences in inflammatory network interconnectivity.Conclusion: These results suggest the existence of four distinct organ failure patterns based on MOD score magnitude in blunt trauma patients admitted to the ICU who survive to discharge.

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.

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 could 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. Circulating IL-17A was elevated significantly in the mNight group from 24 h to 7 days post-injury. Circulating MIP-1α, 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-β1, 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 clinical outcomes in severely injured patients in a manner associated with an altered systemic inflammation program, possibly implying a role for diurnal or circadian variation in the response to traumatic injury.

Abstract The absence of early outcome biomarkers for pediatric acute liver failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome subgroups. Serum samples from 101 participants in the PALF study, collected over the first 7 d following enrollment, were assayed for 27 inflammatory mediators. Outcomes (spontaneous survivors [S, n = 61], nonsurvivors [NS, n = 12] and liver transplant patients [LTx, n = 28]) were assessed at 21 d post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian network inference identified a common network motif, with HMGB1 as a central node in all patient subgroups. The networks in S and LTx were similar, and differed from NS. Dynamic network analysis suggested similar dynamic connectivity in S and LTx, but a more highly interconnected network in NS that increased with time. A dynamic robustness index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient subgroups. Our results suggest that increasing inflammatory network connectivity is associated with nonsurvival in PALF and could ultimately lead to better patient outcome stratification.

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.

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.

We hypothesized that isolated primary mouse hepatic stellate cells (HSC) and hepatocytes (HC) would elaborate different inflammatory responses to hypoxia with or without reoxygenation. We further hypothesized that intracellular information processing (“thinking”) differs from extracellular information transfer (“talking”) in each of these two liver cell types. Finally, we hypothesized that the complexity of these autocrine responses might only be defined in the absence of other non-parenchymal cells or trafficking leukocytes. Accordingly, we assayed 19 inflammatory mediators in the cell culture media (CCM) and whole cell lysates (WCLs) of HSC and HC during hypoxia with and without reoxygenation. We applied a unique set of statistical and data-driven modeling techniques including Two-Way ANOVA, hierarchical clustering, Principal Component Analysis (PCA) and Network Analysis to define the inflammatory responses of these isolated cells to stress. HSC, under hypoxic and reoxygenation stresses, both expressed and secreted larger quantities of nearly all inflammatory mediators as compared to HC. These differential responses allowed for segregation of HSC from HC by hierarchical clustering. PCA suggested, and network analysis supported, the hypothesis that above a certain threshold of cellular stress, the inflammatory response becomes focused on a limited number of functions in both HSC and HC, but with distinct characteristics in each cell type. Network analysis of separate extracellular and intracellular inflammatory responses, as well as analysis of the combined data, also suggested the presence of more complex inflammatory “talking” (but not “thinking”) networks in HSC than in HC. This combined network analysis also suggested an interplay between intracellular and extracellular mediators in HSC under more conditions than that observed in HC, though both cell types exhibited a qualitatively similar phenotype under hypoxia/reoxygenation. Our results thus suggest that a stepwise series of computational and statistical analyses may help decipher how cells respond to environmental stresses, both within the cell and in its secretory products, even in the absence of cooperation from other cells in the liver.

Trauma combined with hemorrhagic shock (HS/T) leads to systemic inflammation, which 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 (DCs) 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 × TLR4loxP/loxP, and CD11c-Cre × 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 HS.

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.

Timothy Billiar summarizes the role of the innate immune response in the clinical course following severe injury and envisions a field of "trauma immunology."

Background: Ex vivo machine perfusion (MP) can better preserve organs for transplantation. We have recently reported on the first application of a MP protocol in which liver allografts were fully oxygenated, under dual pressures and subnormothermic conditions, with a new hemoglobin-based oxygen carrier 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- 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.

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 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.

Extracorporeal organ perfusion, in which organs are preserved in an isolated, ex vivo environment over an extended time-span, is a concept that has led to the development of numerous alternative preservation protocols designed to better maintain organ viability prior to transplantation. These protocols offer researchers a novel opportunity to obtain extensive sampling of isolated organs, free from systemic influences. Data-driven computational modeling is a primary means of integrating the extensive and multivariate data obtained in this fashion. In this review, we focus on the application of dynamic data-driven computational modeling to liver pathophysiology and transplantation based on data obtained from ex vivo organ perfusion.

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU.

Acute skin rejection in vascularized composite allotransplantation (VCA) is the major obstacle for wider adoption in clinical practice. This study utilized computational modeling to identify biomarkers for diagnosis and targets for treatment of skin rejection. Protein levels of 14 inflammatory mediators in skin and muscle biopsies from syngeneic grafts [n = 10], allogeneic transplants without immunosuppression [n = 10] and allografts treated with tacrolimus [n = 10] were assessed by multiplexed analysis technology. Hierarchical Clustering Analysis, Principal Component Analysis, Random Forest Classification and Multinomial Logistic Regression models were used to segregate experimental groups. Based on Random Forest Classification, Multinomial Logistic Regression and Hierarchical Clustering Analysis models, IL-4, TNF-α and IL-12p70 were the best predictors of skin rejection and identified rejection well in advance of histopathological alterations. TNF-α and IL-12p70 were the best predictors of muscle rejection and also preceded histopathological alterations. Principal Component Analysis identified IL-1α, IL-18, IL-1β, and IL-4 as principal drivers of transplant rejection. Thus, inflammatory patterns associated with rejection are specific for the individual tissue and may be superior for early detection and targeted treatment of rejection.

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% O2) 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.

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.

Pressure ulcers are costly and life-threatening complications for people with spinal cord injury (SCI). People with SCI also exhibit differential blood flow properties in non-ulcerated skin. We hypothesized that a computer simulation of the pressure ulcer formation process, informed by data regarding skin blood flow and reactive hyperemia in response to pressure, could provide insights into the pathogenesis and effective treatment of post-SCI pressure ulcers. Agent-Based Models (ABM) are useful in settings such as pressure ulcers, in which spatial realism is important. Ordinary Differential Equation-based (ODE) models are useful when modeling physiological phenomena such as reactive hyperemia. Accordingly, we constructed a hybrid model that combines ODEs related to blood flow along with an ABM of skin injury, inflammation, and ulcer formation. The relationship between pressure and the course of ulcer formation, as well as several other important characteristic patterns of pressure ulcer formation, was demonstrated in this model. The ODE portion of this model was calibrated to data related to blood flow following experimental pressure responses in non-injured human subjects or to data from people with SCI. This model predicted a higher propensity to form ulcers in response to pressure in people with SCI vs. non-injured control subjects, and thus may serve as novel diagnostic platform for post-SCI ulcer formation.