THE ACUTE INFLAMMATORY RESPONSE IN DIVERSE SHOCK STATES

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

Modeling the inflammatory response in the hypothalamus ensuing heat stroke: iterative cycle of model calibration, identifiability analysis, experimental design and data collection

Heat Stroke (HS) is a life-threatening illness caused by prolonged exposure to heat that causes severe hyperthermia and nervous system abnormalities. The long term consequences of HS are poorly understood and deeper insight is required to find possible treatment strategies. Elevated pro- and anti-inflammatory cytokines during HS recovery suggest to play a major role in the immune response. In this study, we developed a mathematical model to understand the interactions and dynamics of cytokines in the hypothalamus, the main thermoregulatory center in the brain. Uncertainty and identifiability analysis of the calibrated model parameters revealed non-identifiable parameters due to the limited amount of data. To overcome the lack of identifiability of the parameters, an iterative cycle of optimal experimental design, data collection, re-calibration and model reduction was applied and further informative experiments were suggested. Additionally, a new method of approximating the prior distribution of the parameters for Bayesian optimal experimental design based on the profile likelihood is presented.

Global sensitivity analysis used to interpret biological experimental results

Modeling host/pathogen interactions provides insight into immune defects that allow bacteria to overwhelm the host, mechanisms that allow vaccine strategies to be successful, and illusive interactions between immune components that govern the immune response to a challenge. However, even simplified models require a fairly high dimensional parameter space to be explored. Here we use global sensitivity analysis for parameters in a simple model for biofilm infections in mice. The results indicate which parameters are insignificant and are 'frozen' to yield a reduced model. The reduced model replicates the full model with high accuracy, using approximately half of the parameter space. We used the sensitivity to investigate the results of the combined biological and mathematical experiments for osteomyelitis. We are able to identify parts of the compartmentalized immune system that were responsible for each of the experimental outcomes. This model is one example for a technique that can be used generally.

Insights from computational modeling in inflammation and acute rejection in limb transplantation

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.

Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses

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.

Modelling the interaction between the host immune response, bacterial dynamics and inflammatory damage in comparison with immunomodulation and vaccination experiments

The immune system is a complex system of chemical and cellular interactions that responds quickly to queues that signal infection and then reverts to a basal level once the challenge is eliminated. Here, we present a general, four-component model of the immune system's response to a Staphylococcal aureus (S. aureus) infection, using ordinary differential equations. To incorporate both the infection and the immune system, we adopt the style of compartmenting the system to include bacterial dynamics, damage and inflammation to the host, and the host response. We incorporate interactions not previously represented including cross-talk between inflammation/damage and the infection and the suppression of the anti-inflammatory pathway in response to inflammation/damage. As a result, the most relevant equilibrium of the system, representing the health state, is an all-positive basal level. The model is able to capture eight different experimental outcomes for mice challenged with intratibial osteomyelitis due to S. aureus, primarily involving immunomodulation and vaccine therapies. For further validation and parameter exploration, we perform a parameter sensitivity analysis which suggests that the model is very stable with respect to variations in parameters, indicates potential immunomodulation strategies and provides a possible explanation for the difference in immune potential for different mouse strains.

Inhibition of inflammatory injure by polysaccharides from Bupleurum chinense through antagonizing P-selectin

P-selectin-mediated adhesion between endothelium and neutrophils is a crucial process leading to acute inflammatory injure. Thus, P-selectin has been considered as promising target for therapeutics of acute inflammatory-related diseases. In the present study, the water-soluble polysaccharides (BCPs) were isolated from Bupleurum chinense, and we evaluated their therapeutical effects on acute inflammatory injure and antagonistic function against P-selectin-mediated neutrophil adhesion. Our results showed that BCPs significantly impaired the leukocyte infiltration and relieve lung injury in LPS-induced acute pneumonia model. BCPs significantly blocked the binding of P-selectin to neutrophils and inhibited P-selectin-mediated neutrophils rolling along CHO-P cell monolayer. The result from in vitro protein binding assay showed a direct evidence indicating that BCPs-treatment significantly eliminated the interaction between rhP-Fc and its physiological ligand PSGL-1 at protein level. Together, these results provide a novel therapeutical strategy for amelioration of inflammation-related disease processes by polysaccharides from B. chinense.

Innate immunity in disease: insights from mathematical modeling and analysis

The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. This robust responsiveness to biological stress likely endows the host with increased fitness, but over-robust or inadequate inflammation predisposes the host to various diseases. Importantly, well-compartmentalized inflammation is generally beneficial, but spillover of inflammation into the blood is a hallmark-and likely also a driver-of self-maintaining inflammation. The blood is also a key entry point and immunological interface for vectors of parasitic diseases, diseases that themselves incite systemic inflammation. The complex role of inflammation in health and disease has made this biological system difficult to understand comprehensively and modulate rationally for therapeutic purposes. Consequently, systems approaches have been applied in order to characterize dynamical properties and identify key control points in inflammation. This process begins with the collection of high-dimensional, experimental, and clinical data, followed by data reduction and data-driven modeling that finally informs mechanistic computational models for analysis, prediction, and rational modulation. These studies have suggested that the overall architecture of the inflammatory response includes a multiscale positive feedback from inflammation → tissue damage → inflammation, which is often inadequately controlled by negative feedback from anti-inflammatory mediators. Given the importance of the blood interface for the inflammatory response, and the accessibility of this compartment both as an immunological sampling reservoir for vectors as well as for diagnosis and therapy, we suggest that any rational efforts at modulating inflammation via the blood compartment must involve computational modeling.

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

The translation of in vitro findings to clinical outcomes is often elusive. Trauma/hemorrhagic shock (T/HS) results in hepatic hypoxia that drives inflammation. We hypothesize that in silico methods would help bridge in vitro hepatocyte data and clinical T/HS, in which the liver is a primary site of inflammation. Primary mouse hepatocytes were cultured under hypoxia (1% 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.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

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.

Inhibitory effect of Coptis chinensis on inflammation in LPS-induced endotoxemia

ETHNOPHARMACOLOGICAL RELEVANCE

Rhizoma coptidis (RC) has been used as a remedy for inflammation-related diseases in traditional medicine. Although it is known to have anti-inflammatory activities, its mechanism of action on lipopolysaccharide (LPS)-induced inflammation has not yet been identified in detail.

AIM OF THE STUDY

This study was designed to assess the beneficial effects of pretreatment with RC in ameliorating LPS-induced liver inflammation.

MATERIALS AND METHODS

Mice were orally administered RC (500, 1000 mg/kg) for three days in a row. 1h after the last RC administration, the mice were intraperitoneally injected with LPS (35 mg/kg). After treatment, histological alterations and inflammatory factor levels in the liver and proinflammatory cytokines in the serum associated with inflammation were examined.

RESULTS

We found that pretreatment with RC (500 and 1000 mg/kg) exerted a significant protective effect by attenuating liver histopathological changes in endotoxemic mice. The results also demonstrated that RC suppressed secretion of LPS-stimulated pro-inflammatory cytokines, such as interleukin-6 (IL-6). Furthermore, RC inhibited LPS-mediated nuclear factor (NF)-κB activation via the prevention of IκB-α phosphorylation, as well as the phosphorylation of ERK1/2, JNK, and p38 MAPKs. These results were associated with decreases in the expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (I-NOS).

CONCLUSIONS

The results presented here clearly demonstrate that RC could significantly protect mice against LPS-induced acute liver injury.

Cumulative effects of bone and soft tissue injury on systemic inflammation: a pilot study

BACKGROUND

In multiply injured patients, bilateral femur fractures invoke a substantial systemic inflammatory impact and remote organ dysfunction. However, it is unclear whether isolated bone or soft tissue injury contributes to the systemic inflammatory response and organ injury after fracture.

QUESTIONS/PURPOSES

We therefore asked whether the systemic inflammatory response and remote organ dysfunction are attributable to the bone fragment injection, adjacent soft tissue injury, or both.

METHODS

Male C57/BL6 mice (8-10 weeks old, 20-30 g) were assigned to four groups: bone fragment injection (BF, n = 9) group; soft tissue injury (STI, n = 9) group; BF + STI (n = 9) group, in which both insults were applied; and control group, in which neither insult was applied. Animals were sacrificed at 6 hours. As surrogates for systemic inflammation, we measured serum IL-6, IL-10, osteopontin, and alanine aminotransferase (ALT) and nuclear factor (NF)-κB and myeloperoxidase (MPO) in the lung.

RESULTS

The systemic inflammatory response (mean IL-6 level) was similar in the BF (61.8 pg/mL) and STI (67.9 pg/mL) groups. The combination (BF + STI) of both traumatic insults induced an increase in mean levels of inflammatory parameters (IL-6: 189.1 pg/mL) but not in MPO levels (1.21 ng/mL) as compared with the BF (0.82 ng/mL) and STI (1.26 ng/mL) groups. The model produced little evidence of remote organ inflammation.

CONCLUSIONS

Our findings suggest both bone and soft tissue injury are required to induce systemic changes. The absence of remote organ inflammation suggests further fracture-associated factors, such as hemorrhage and fat liberation, may be more critical for induction of remote organ damage.

CLINICAL RELEVANCE

Both bone and soft tissue injuries contribute to the systemic inflammatory response.

Combined in silico, in vivo, and in vitro studies shed insights into the acute inflammatory response in middle-aged mice

We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO₂^−/NO₃⁻ data from “middle-aged” (6–8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for “young” (2–3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging.

Numerical and analytical study of an atherosclerosis inflammatory disease model

We study a reaction-diffusion mathematical model for the evolution of atherosclerosis as an inflammation process by combining analytical tools with computer-intensive numerical calculations. The computational work involved the calculation of more than sixty thousand solutions of the full reaction-diffusion system and lead to the complete characterisation of the ω-limit for every initial condition. Qualitative properties of the solution are rigorously proved, some of them hinted at by the numerical study.

Clinical review: Statins and trauma--a systematic review

Statins, in addition to their lipid-lowering properties, have anti-inflammatory actions. The aim of this review is to evaluate the effect of pre-injury statin use, and statin treatment following injury. MEDLINE, EMBASE, and CENTRAL databases were searched to January 2012 for randomised and observational studies of statins in trauma patients in general, and in patients who have suffered traumatic brain injury, burns, and fractures. Of 985 identified citations, 7 (4 observational studies and 3 randomised controlled trials (RCTs)) met the inclusion criteria. Two studies (both observational) were concerned with trauma patients in general, two with patients who had suffered traumatic brain injury (one observational, one RCT), two with burns patients (one observational, one RCT), and one with fracture healing (RCT). Two of the RCTs relied on surrogate outcome measures. The observational studies were deemed to be at high risk of confounding, and the RCTs at high risk of bias. Three of the observational studies suggested improvements in a number of clinical outcomes in patients taking statins prior to injury (mortality, infection, and septic shock in burns patients; mortality in trauma patients in general; mortality in brain injured patients) whereas one, also of trauma patients in general, showed no difference in mortality or infection, and an increased risk of multi-organ failure. Two of three RCTs on statin treatment in burns patients and brain injured patients showed improvements in E-selectin levels and cognitive function. The third, of patients with radial fractures, showed no acceleration in fracture union. In conclusion, there is some evidence that pre-injury statin use and post-injury statin treatment may have a beneficial effect in patients who have suffered general trauma, traumatic brain injury, and burns. However, these studies are at high risk of confounding and bias, and should be regarded as 'hypothesisgenerating'. A well-designed RCT is required to determine the therapeutic efficacy in improving outcomes in this patient population.

MALP-2 pre-treatment modulates systemic inflammation in hemorrhagic shock

Background

TLR-2 is expressed on the surface of leucocytes, lung and liver tissue and initiates the activation of immune response after interaction with components of the bacterial cell wall. In this experiment we investigated whether immunostimulation with TLR-2 agonists under conditions of sterile inflammation (hemorrhagic shock (HS)) may affect the immune response and remote organ inflammation.

Methods

Male C57/BL6 mice were subjected to standardized pressure-controlled HS (MAP of 35 mmHg for 90 minutes). The TLR-2 agonist macrophage-activated lipopeptide-2 (MALP-2) was administered (i.p.) either 12 hours prior to the induction of HS (Group MALP PT) or after the hypotensive period (90 minutes) (Group MALP T). After six hours, plasma cytokine levels (IL-6, KC, IL-10, and MCP-1) and lung and liver MPO activity were assessed.

Results

Pre-treatment with MALP-2 resulted in a significant attenuation of the systemic pro-inflammatory (IL-6) response (MALP PT: 0.83±0.2 ng/ml vs. MALP T: 1.7±0.09 ng/ml) (p<0.05). In comparison to the liver MPO activity, lung MPO levels in in group MALP PT did not show differences to levels measured in MALP T mice (1.200±200 ng/mg vs. 1.800±200 ng/mg).

Conclusions

After initial inflammation, MALP-2 pre-treatment was associated with attenuated systemic immune response after sterile stimulus. The TLR-2 agonist appears to affect sterile inflammation pathways. The exact mechanisms should be studied further to better understand these affects.

Augmenter of liver regeneration (ALR) is a novel biomarker of hepatocellular stress/inflammation: in vitro, in vivo and in silico studies

The liver is a central organ involved in inflammatory processes, including the elaboration of acute-phase proteins. Augmenter of liver regeneration (ALR) protein, expressed and secreted by hepatocytes, promotes liver regeneration and maintains viability of hepatocytes. ALR also stimulates secretion of inflammatory cytokines (tumor necrosis factor [TNF]-α and interleukin [IL]-6) and nitric oxide from Kupffer cells. We hypothesized that ALR may be involved in modulating inflammation induced by various stimuli. We found that hepatic ALR levels are elevated at 24 h, before or about the same time as an increase in the mRNA expression of TNF-α and IL-6, after portacaval shunt surgery in rats. Serum ALR also increased, but significantly only on d 4 when pathological changes in the liver become apparent. In rats, serum ALR was elevated after intraperitoneal administration of lipopolysaccharide alone and in a model of gram-negative sepsis. Serum ALR increased before alanine aminotransferase (ALT) in endotoxemia and in the same general time frame as TNF-α and IL-6 in the bacterial sepsis model. Furthermore, mathematical prediction of tissue damage correlated strongly with alterations in serum ALR in a mouse model of hemorrhagic shock. In vitro, monomethyl sulfonate, TNF-α, actinomycin D and lipopolysaccharide all caused increased release of ALR from rat hepatocytes, which preceded the loss of cell viability and/or inhibition of DNA synthesis. ALR may thus serve as a potential diagnostic marker of hepatocellular stress and/or acute inflammatory conditions.

Models of hemorrhagic shock: differences in the physiological and inflammatory response

INTRODUCTION

The hemorrhagic shock (HS) model is commonly used to initiate a systemic post-traumatic inflammatory response. Numerous experimental protocols exist and it is unclear how differences in these models affect the immune response making it difficult to compare results between studies. The aim of this study was to compare the inflammatory response of different established protocols for volume-controlled shock in a murine model.

METHODS

Male C57/BL6 mice 6-10 weeks and weighing 20-25 g were subjected to volume-controlled or pressure-controlled hemorrhagic shock. In the volume-controlled group 300 μl, 500 μl, or 700 μl blood was collected over 15 min and mean arterial pressure was continuously monitored during the period of shock. In the pressure-controlled hemorrhagic shock group, blood volume was depleted with a goal mean arterial pressure of 35 mmHg for 90 min. Following hemorrhage, mice from all groups were resuscitated with the extracted blood and an equal volume of lactated ringer solution. Six hours from the initiation of hemorrhagic shock, serum IL-6, KC, MCP-1 and MPO activity within the lung and liver tissue were assessed.

RESULTS

In the volume-controlled group, the mice were able to compensate the initial blood loss within 30 min. Approximately 800 μl of blood volume was removed to achieve a MAP of 35 mmHg (p<0.001). No difference in the pro-inflammatory cytokine (IL-6 and KC) profile was measured between the volume-controlled groups (300 μl, 500 μl, or 700 μl). The pressure-controlled group demonstrated significantly higher cytokine levels (IL-6 and KC) than all volume-controlled groups. Pulmonary MPO activity increased with the severity of the HS (p<0.05). This relationship could not be observed in the liver.

CONCLUSION

Volume-controlled hemorrhagic shock performed following current literature recommendations may be insufficient to produce a profound post-traumatic inflammatory response. A decrease in the MAP following blood withdrawal (300 μl, 500 μl or 700 μl) was usually compensated within 30 min. Pressure-controlled hemorrhagic shock is a more reliable for induction of a systemic inflammatory response.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

Trauma alarmins as activators of damage-induced inflammation

BACKGROUND

A systemic inflammatory response syndrome (SIRS) is frequently observed after traumatic injury. The response is sterile and the activating stimulus is tissue damage. Endogenous molecules, called alarmins, are reputed to be released by injured tissues but the precise identity of these mediators is unclear. This review summarizes current preclinical and clinical evidence for trauma alarmins and their role in innate immune activation.

METHODS

A comprehensive literature review of putative alarmins in tissue damage after traumatic injury was conducted.

RESULTS

The presence of SIRS at admission is an independent predictor of mortality after trauma. The primary initiators of the human immune response are unclear. Several endogenous substances display alarmin characteristics in vitro. Preclinical studies demonstrate that blockade of certain endogenous substances can reduce adverse clinical sequelae after traumatic injury. Human evidence for trauma alarmins is extremely limited.

CONCLUSION

The magnitude of acute inflammation is predictive of outcome after trauma, suggesting that an early opportunity for immune modulation may exist. An understanding of the mechanisms of innate immune activation following trauma may lead to new therapeutic agents and improved patient survival.

Minocycline ameliorates lung and liver dysfunction in a rodent model of hemorrhagic shock/resuscitation plus abdominal compartment syndrome

BACKGROUND

We sought to elucidate whether minocycline, a broad-spectrum tetracycline antibiotic with potent anti-inflammation capacity, could mitigate inflammatory response and organ dysfunction in the lungs and liver induced by hemorrhagic shock/resuscitation (HS) plus abdominal compartment syndrome (ACS).

MATERIALS AND METHODS

Adult male rats were randomized to receive HS plus ACS or HS plus ACS plus minocycline (denoted as the HS/A and HS/A-M group, respectively; n = 12). Sham-instrumentation groups were employed to serve as the controls. Hemorrhagic shock/resuscitation was induced by blood drawing (mean arterial pressure: 40-45 mm Hg for 60 min) followed by shed blood/saline mixture reinfusion. Subsequently, intra-abdominal pressure (IAP) was increased to 25 mm Hg by injecting air into the preplaced intraperitoneal latex balloon to induce ACS. Minocycline (20 mg/kg) was intravenously administered immediately after resuscitation. IAP was maintained at 25 mm Hg for 6 h. Then, all rats were euthanized.

RESULTS

The levels of polymorphonuclear leukocyte infiltration, the wet/dry weight ratio, and the concentrations of inflammatory molecules (e.g., chemokine, cytokine, and prostaglandin E2) in lung and liver tissues of the HS/A group were significantly higher than those of the HS/A-M groups (all P < 0.05). Moreover, the levels of lung dysfunction (assayed by arterial blood gas) and liver dysfunction (assayed by plasma concentrations of bilirubin, aspartate aminotransferase, and alaninine aminotransferase) of the HS/A group were significantly higher than those of the HS/A-M group (all P < 0.05).

CONCLUSIONS

Minocycline ameliorates inflammatory response and organ dysfunction in the lungs and liver induced by hemorrhagic shock/resuscitation plus abdominal compartment syndrome.

A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine

OBJECTIVE

To gain insights into individual variations in acute inflammation and physiology.

DESIGN

Large-animal study combined with mathematical modeling.

SETTING

Academic large-animal and computational laboratories.

SUBJECTS

Outbred juvenile swine.

INTERVENTIONS

Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling.

MEASUREMENTS AND MAIN RESULTS

Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO(2)/FIO(2) ratio μ200 and static compliance μ10 L/cm H(2)O). Plasma interleukin-1β, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO(2)/NO(3) were significantly (p μ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1β from resident macrophages. The ordinary differential equation model also included blood pressure, PaO(2), and FIO(2), and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine.

CONCLUSIONS

The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.

Toward computational identification of multiscale "tipping points" in acute inflammation and multiple organ failure

Sepsis accounts annually for nearly 10% of total U.S. deaths, costing nearly $17 billion/year. Sepsis is a manifestation of disordered systemic inflammation. Properly regulated inflammation allows for timely recognition and effective reaction to injury or infection, but inadequate or overly robust inflammation can lead to Multiple Organ Dysfunction Syndrome (MODS). There is an incongruity between the systemic nature of disordered inflammation (as the target of inflammation-modulating therapies), and the regional manifestation of organ-specific failure (as the subject of organ support), that presents a therapeutic dilemma: systemic interventions can interfere with an individual organ system's appropriate response, yet organ-specific interventions may not help the overall system reorient itself. Based on a decade of systems and computational approaches to deciphering acute inflammation, along with translationally-motivated experimental studies in both small and large animals, we propose that MODS evolves due to the feed-forward cycle of inflammation → damage → inflammation. We hypothesize that inflammation proceeds at a given, "nested" level or scale until positive feedback exceeds a "tipping point." Below this tipping point, inflammation is contained and manageable; when this threshold is crossed, inflammation becomes disordered, and dysfunction propagates to a higher biological scale (e.g., progressing from cellular, to tissue/organ, to multiple organs, to the organism). Finally, we suggest that a combination of computational biology approaches involving data-driven and mechanistic mathematical modeling, in close association with studies in clinically relevant paradigms of sepsis/MODS, are necessary in order to define scale-specific "tipping points" and to suggest novel therapies for sepsis.

Sepsis: from pattern to mechanism and back

Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.

Integrating rehabilitation engineering technology with biologics

Rehabilitation engineers apply engineering principles to improve function or to solve challenges faced by persons with disabilities. It is critical to integrate the knowledge of biologics into the process of rehabilitation engineering to advance the field and maximize potential benefits to patients. Some applications in particular demonstrate the value of a symbiotic relationship between biologics and rehabilitation engineering. In this review we illustrate how researchers working with neural interfaces and integrated prosthetics, assistive technology, and biologics data collection are currently integrating these 2 fields. We also discuss the potential for further integration of biologics and rehabilitation engineering to deliver the best technologies and treatments to patients. Engineers and clinicians must work together to develop technologies that meet clinical needs and are accessible to the intended patient population.

A physiological model for autonomic heart rate regulation in human endotoxemia

The systemic inflammatory response syndrome often accompanies critical illnesses and can be an important cause of morbidity and mortality. Marked abnormalities in cardiovascular function accompany acute illnesses manifested as sustained tachyarrhythmias, which are but one component of systemic dysregulation. The realization that cardiac pacemaker activity is under control of the autonomic nervous system has promoted the analysis of heart rate (HR) variation for assessing autonomic activities. In acute illnesses, autonomic imbalance manifesting in part as parasympathetic attenuation is associated with increased morbidity in patients who manifest systemic inflammatory response syndrome phenotype. Driven by the premise that biological phenotypes emerge as the outcome of the coordinated action of network elements across the host, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is developed that quantifies critical aspects of the complex relationship between inflammation and autonomic HR regulation. In the present study, changes in HR response to acute injury, phenotypically expressed as tachycardia, are simulated as a result of autonomic imbalance that reflects sympathetic activity excess and parasympathetic attenuation. The proposed model assesses both the anti-inflammatory and cardiovascular effects of antecedent stresses upon the systemic inflammatory manifestations of human endotoxemia as well as a series of nonlinear inflammatory relevant scenarios. Such a modeling approach provides a comprehensive conceptual framework linking inflammation and physiological complexity via a multiscale model that may advance the translational potential of systems modeling in clinical research.

Sepsis: Something old, something new, and a systems view

Sepsis is a clinical syndrome characterized by a multisystem response to a microbial pathogenic insult consisting of a mosaic of interconnected biochemical, cellular, and organ-organ interaction networks. A central thread that connects these responses is inflammation that, while attempting to defend the body and prevent further harm, causes further damage through the feed-forward, proinflammatory effects of damage-associated molecular pattern molecules. In this review, we address the epidemiology and current definitions of sepsis and focus specifically on the biologic cascades that comprise the inflammatory response to sepsis. We suggest that attempts to improve clinical outcomes by targeting specific components of this network have been unsuccessful due to the lack of an integrative, predictive, and individualized systems-based approach to define the time-varying, multidimensional state of the patient. We highlight the translational impact of computational modeling and other complex systems approaches as applied to sepsis, including in silico clinical trials, patient-specific models, and complexity-based assessments of physiology.

An agent-based model of inflammation and fibrosis following particulate exposure in the lung

Inflammation and airway remodeling occur in a variety of airway diseases. Modeling aspects of the inflammatory and fibrotic processes following repeated exposure to particulate matter may provide insights into a spectrum of airway diseases, as well as prevention/treatment strategies. An agent-based model (ABM) was created to examine the response of an abstracted population of inflammatory cells (nominally macrophages, but possibly including other inflammatory cells such as lymphocytes) and cells involved in remodeling (nominally fibroblasts) to particulate exposure. The model focused on a limited number of relevant interactions, specifically those among macrophages, fibroblasts, a pro-inflammatory cytokine (TNF-α), an anti-inflammatory cytokine (TGF-β1), collagen deposition, and tissue damage. The model yielded three distinct states that were equated with (1) self-resolving inflammation and a return to baseline, (2) a pro-inflammatory process of localized tissue damage and fibrosis, and (3) elevated pro- and anti-inflammatory cytokines, persistent tissue damage, and fibrosis outcomes. Experimental results consistent with these predicted states were observed in histology sections of lung tissue from mice exposed to particulate matter. Systematic in silico studies suggested that the development of each state depended primarily upon the degree and duration of exposure. Thus, a relatively simple ABM resulted in several, biologically feasible, emergent states, suggesting that the model captures certain salient features of inflammation following exposure of the lung to particulate matter. This ABM may hold future utility in the setting of airway disease resulting from inflammation and fibrosis following particulate exposure.

A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks

BACKGROUND

Complex biological processes such as acute inflammation induced by trauma/hemorrhagic shock/ (T/HS) are dynamic and multi-dimensional. We utilized multiplexing cytokine analysis coupled with data-driven modeling to gain a systems perspective into T/HS.

METHODOLOGY/PRINCIPAL FINDINGS

Mice were subjected to surgical cannulation trauma (ST) ± hemorrhagic shock (HS; 25 mmHg), and followed for 1, 2, 3, or 4 h in each case. Serum was assayed for 20 cytokines and NO(2) (-)/NO(3) (-). These data were analyzed using four data-driven methods (Hierarchical Clustering Analysis [HCA], multivariate analysis [MA], Principal Component Analysis [PCA], and Dynamic Network Analysis [DyNA]). Using HCA, animals subjected to ST vs. ST + HS could be partially segregated based on inflammatory mediator profiles, despite a large overlap. Based on MA, interleukin [IL]-12p40/p70 (IL-12.total), monokine induced by interferon-γ (CXCL-9) [MIG], and IP-10 were the best discriminators between ST and ST/HS. PCA suggested that the inflammatory mediators found in the three main principal components in animals subjected to ST were IL-6, IL-10, and IL-13, while the three principal components in ST + HS included a large number of cytokines including IL-6, IL-10, keratinocyte-derived cytokine (CXCL-1) [KC], and tumor necrosis factor-α [TNF-α]. DyNA suggested that the circulating mediators produced in response to ST were characterized by a high degree of interconnection/complexity at all time points; the response to ST + HS consisted of different central nodes, and exhibited zero network density over the first 2 h with lesser connectivity vs. ST at all time points. DyNA also helped link the conclusions from MA and PCA, in that central nodes consisting of IP-10 and IL-12 were seen in ST, while MIG and IL-6 were central nodes in ST + HS.

CONCLUSIONS/SIGNIFICANCE

These studies help elucidate the dynamics of T/HS-induced inflammation, complementing other forms of dynamic mechanistic modeling. These methods should be applicable to the analysis of other complex biological processes.

Systemic LPS administration induces brain inflammation but not dopaminergic neuronal death in the substantia nigra

It has been suggested that brain inflammation is important in aggravation of brain damage and/or that inflammation causes neurodegenerative diseases including Parkinson's disease (PD). Recently, systemic inflammation has also emerged as a risk factor for PD. In the present study, we evaluated how systemic inflammation induced by intravenous (iv) lipopolysaccharides (LPS) injection affected brain inflammation and neuronal damage in the rat. Interestingly, almost all brain inflammatory responses, including morphological activation of microglia, neutrophil infiltration, and mRNA/protein expression of inflammatory mediators, appeared within 4-8 h, and subsided within 1-3 days, in the substantia nigra (SN), where dopaminergic neurons are located. More importantly, however, dopaminergic neuronal loss was not detectable for up to 8 d after iv LPS injection. Together, these results indicate that acute induction of systemic inflammation causes brain inflammation, but this is not sufficiently toxic to induce neuronal injury.

Modeling the host response to inhalation anthrax

Inhalation anthrax, an often fatal infection, is initiated by endospores of the bacterium Bacillus anthracis, which are introduced into the lung. To better understand the pathogenesis of an inhalation anthrax infection, we propose a two-compartment mathematical model that takes into account the documented early events of such an infection. Anthrax spores, once inhaled, are readily taken up by alveolar phagocytes, which then migrate rather quickly out of the lung and into the thoracic/mediastinal lymph nodes. En route, these spores germinate to become vegetative bacteria. In the lymph nodes, the bacteria kill the host cells and are released into the extracellular environment where they can be disseminated into the blood stream and grow to a very high level, often resulting in the death of the infected person. Using this framework as the basis of our model, we explore the probability of survival of an infected individual. This is dependent on several factors, such as the rate of migration and germination events and treatment with antibiotics.

Selective improvement of tumor necrosis factor capture in a cytokine hemoadsorption device using immobilized anti-tumor necrosis factor

Sepsis is a harmful hyper-inflammatory state characterized by overproduction of cytokines. Removal of these cytokines using an extracorporeal device is a potential therapy for sepsis. We are developing a cytokine adsorption device (CAD) filled with porous polymer beads which efficiently depletes middle-molecular weight cytokines from a circulating solution. However, removal of one of our targeted cytokines, tumor necrosis factor (TNF), has been significantly lower than other smaller cytokines. We addressed this issue by incorporating anti-TNF antibodies on the outer surface of the beads. We demonstrated that covalent immobilization of anti-TNF increases overall TNF capture from 55% (using unmodified beads) to 69%. Passive adsorption increases TNF capture to over 99%. Beads containing adsorbed anti-TNF showed no significant loss in their ability to remove smaller cytokines, as tested using interleukin-6 (IL-6) and interleukin-10 (IL-10). We also detail a novel method for quantifying surface-bound ligand on a solid substrate. This assay enabled us to rapidly test several methods of antibody immobilization and their appropriate controls using dramatically fewer resources. These new adsorbed anti-TNF beads provide an additional level of control over a device which previously was restricted to nonspecific cytokine adsorption. This combined approach will continue to be optimized as more information becomes available about which cytokines play the most important role in sepsis.

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Translational systems biology of inflammation: potential applications to personalized medicine

A central goal of industrialized nations is to provide personalized, preemptive and predictive medicine, while maintaining healthcare costs at a minimum. To do so, we must confront and gain an understanding of inflammation, a complex, nonlinear process central to many diseases that affect both industrialized and developing nations. Herein, we describe the work aimed at creating a rational, engineering-oriented and evidence-based synthesis of inflammation geared towards rapid clinical application. This comprehensive approach, which we call 'Translational Systems Biology', to date has been utilized for in silico studies of sepsis, trauma/hemorrhage/traumatic brain injury, acute liver failure and wound healing. This framework has now allowed us to suggest how to modulate acute inflammation in a rational and individually optimized fashion using engineering principles applied to a biohybrid device. We suggest that we are on the cusp of fulfilling the promise of in silico modeling for personalized medicine for inflammatory disease.

A quantitative model of thermal injury-induced acute inflammation

Severe burns are among the most common causes of death from unintentional injury. The induction and resolution of the burn-induced systemic inflammatory response are mediated by a network of factors and regulatory proteins. Numerous mechanisms operate simultaneously, thus requiring a systems level approach to characterize their overall impact. Towards this goal, we propose an in silico semi-mechanistic model of burn-induced systemic inflammation using liver-specific gene expression from a rat burn model. Transcriptional responses are coupled with extracellular signals through a receptor mediated indirect response (IDR) and transit compartment model. The activation of the innate immune system in response to the burn stimulus involves the interaction between extracellular signals and critical receptors which triggers downstream signal transduction cascades leading to transcriptional changes. The resulting model consists of fifteen (15) coupled ordinary differential equations capturing key aspects of inflammation such as pro-inflammation, anti-inflammation and hypermetabolism. The model was then evaluated through a series of biologically relevant scenarios aiming at revealing the non-linear behavior of acute inflammation including: investigating the implication of effect of different severity of thermal injury; examining possible mechanistic dysregulation of IKK-NF-κB system which may reflect secondary effects that lead to potential malfunction of the response; and exploring the outcome of administration of receptor antagonist or anti-body to significant cytokines.

Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

Translational systems approaches to the biology of inflammation and healing

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.

Assessing shock resuscitation strategies by oxygen debt repayment

Identification of occult shock is a major clinical problem compounded by inadequate criteria for assessing the efficacy of fluid resuscitation. We suggest that these problems may be resolved in part by understanding both the physiological mechanisms underlying oxygen debt accumulation and, more importantly, the debt repayment schedule during resuscitation. We present a simplified tutorial that incorporates the concept of the oxygen supply-delivery relationship with that of oxygen debt and show how this is relevant to the understanding of shock and resuscitation. Use of oxygen debt metrics as end points for shock have been controversial; however, much of the controversy may have been due to incomplete understanding of basic physiology of shock and semantic confusion between the various metrics proposed as end points. Here, we provide working definitions for the frequently misunderstood concepts of oxygen deficit and oxygen debt and discuss the relatively novel concept of oxygen debt repayment schedule. We introduce predictions made on the basis of data derived from animal models of hemorrhagic shock. Our calculations suggest that the amount of debt repaid in the first 2 h of resuscitation, rather than the restoration of volume per se, influences the likelihood of organ damage. Because of difficulties inherent in measuring oxygen debt in the prehospital and emergency settings, various metabolic end points such as lactate and base deficit have been proposed as surrogates. We demonstrate the heuristic value of this model in providing a predictive framework for both the optimum therapeutic time window and optimum fluid loadings before critical transitions to an irreversible shock state can occur. The model also provides an unambiguous and objective standard for quantifying the behavior of various postulated shock "markers".

Bistability and bacterial infections

Bacterial infections occur when the natural host defenses are overwhelmed by invading bacteria. The main component of the host defense is impaired when neutrophil count or function is too low, putting the host at great risk of developing an acute infection. In people with intact immune systems, neutrophil count increases during bacterial infection. However, there are two important clinical cases in which they remain constant: a) in patients with neutropenic-associated conditions, such as those undergoing chemotherapy at the nadir (the minimum clinically observable neutrophil level); b) in ex vivo examination of the patient's neutrophil bactericidal activity. Here we study bacterial population dynamics under fixed neutrophil levels by mathematical modelling. We show that under reasonable biological assumptions, there are only two possible scenarios: 1) Bacterial behavior is monostable: it always converges to a stable equilibrium of bacterial concentration which only depends, in a gradual manner, on the neutrophil level (and not on the initial bacterial level). We call such a behavior type I dynamics. 2) The bacterial dynamics is bistable for some range of neutrophil levels. We call such a behavior type II dynamics. In the bistable case (type II), one equilibrium corresponds to a healthy state whereas the other corresponds to a fulminant bacterial infection. We demonstrate that published data of in vitro Staphylococcus epidermidis bactericidal experiments are inconsistent with both the type I dynamics and the commonly used linear model and are consistent with type II dynamics. We argue that type II dynamics is a plausible mechanism for the development of a fulminant infection.

Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis

BACKGROUND

Necrotizing enterocolitis (NEC) is a severe disease of the gastrointestinal tract of pre-term babies and is thought to be related to the physiological immaturity of the intestine and altered levels of normal flora in the gut. Understanding the factors that contribute to the pathology of NEC may lead to the development of treatment strategies aimed at re-establishing the integrity of the epithelial wall and preventing the propagation of inflammation in NEC. Several studies have shown a reduced incidence and severity of NEC in neonates treated with probiotics (beneficial bacteria species).

METHODOLOGY/PRINCIPAL FINDINGS

The objective of this study is to use a mathematical model to predict the conditions under which probiotics may be successful in promoting the health of infants suffering from NEC. An ordinary differential equation model is developed that tracks the populations of pathogenic and probiotic bacteria in the intestinal lumen and in the blood/tissue region. The permeability of the intestinal epithelial layer is treated as a variable, and the role of the inflammatory response is included. The model predicts that in the presence of probiotics health is restored in many cases that would have been otherwise pathogenic. The timing of probiotic administration is also shown to determine whether or not health is restored. Finally, the model predicts that probiotics may be harmful to the NEC patient under very specific conditions, perhaps explaining the detrimental effects of probiotics observed in some clinical studies.

CONCLUSIONS/SIGNIFICANCE

The reduced, experimentally motivated mathematical model that we have developed suggests how a certain general set of characteristics of probiotics can lead to beneficial or detrimental outcomes for infants suffering from NEC, depending on the influences of probiotics on defined features of the inflammatory response.

Translational systems biology of inflammation and healing

Personalized medicine is a major goal for the future of healthcare, and we suggest that computational simulations are necessary in order to achieve it. Inflammatory diseases, both acute and chronic, represent an area in which personalized medicine is especially needed, given the high level of individual variability that characterizes these diseases. We have created such simulations, and have used them to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals; for in silico experiments and clinical trials in sepsis, trauma, and wound healing; and to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ damage via tissue damage/dysfunction. We suggest that computational simulations are the cornerstone of Translational Systems Biology approaches for inflammatory diseases.

Multiscale model for the assessment of autonomic dysfunction in human endotoxemia

Severe injury and infection are associated with autonomic dysfunction. The realization that a dysregulation in autonomic function may predispose a host to excessive inflammatory processes has renewed interest in understanding the role of central nervous system (CNS) in modulating systemic inflammatory processes. Assessment of heart rate variability (HRV) has been used to evaluate systemic abnormalities and as a predictor of the severity of illness. Dissecting the relevance of neuroimmunomodulation in controlling inflammatory processes requires an understanding of the multiscale interplay between CNS and the immune response. A vital enabler in that respect is the development of a systems-based approach that integrates data across multiple scales, and models the emerging host response as the outcome of interactions of critical modules. Thus, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is proposed that integrates processes across the host from the cellular to the systemic host response level. At the cellular level interacting components are associated with elementary signaling pathways that propagate extracellular signals to the transcriptional response level. Further, essential modules associated with the neuroendocrine immune crosstalk are considered. Finally, at the systemic level, phenotypic expressions such as HRV are incorporated to assess systemic decomplexification indicative of the severity of the host response. Thus, the proposed work intends to associate acquired endocrine dysfunction with diminished HRV as a critical enabler for clarifying how cellular inflammatory processes and neural-based pathways mediate the links between patterns of autonomic control (HRV) and clinical outcomes.

A mathematical model of pulmonary gas exchange under inflammatory stress

During a severe local or systemic inflammatory response, immune mediators target lung tissue. This process may lead to acute lung injury and impaired diffusion of gas molecules. Although several mathematical models of gas exchange have been described, none simulate acute lung injury following inflammatory stress. In view of recent laboratory and clinical progress in the understanding of the pathophysiology of acute lung injury, such a mathematical model would be useful. We first derived a partial differential equations model of gas exchange on a small physiological unit of the lung ( approximately 25 alveoli), which we refer to as a respiratory unit (RU). We next developed a simple model of the acute inflammatory response and implemented its effects within a RU, creating a single RU model. Linking multiple RUs with various ventilation/perfusion ratios and taking into account pulmonary venous blood remixing yielded our lung-scale model. Using the lung-scale model, we explored the predicted effects of inflammation on ventilation/perfusion distribution and the resulting pulmonary venous partial pressure oxygen level during systemic inflammatory stresses. This model represents a first step towards the development of anatomically faithful models of gas exchange and ventilation under a broad range of local and systemic inflammatory stimuli resulting in acute lung injury, such as infection and mechanical strain of lung tissue.

Systems biology and inflammation

Inflammation is a complex, multiscale biological response to threats - both internal and external - to the body, which is also required for proper healing of injured tissue. In turn, damaged or dysfunctional tissue stimulates further inflammation. Despite continued advances in characterizing the cellular and molecular processes involved in the interactions between inflammation and tissue damage, there exists a significant gap between the knowledge of mechanistic pathophysiology and the development of effective therapies for various inflammatory conditions. We have suggested the concept of translational systems biology, defined as a focused application of computational modeling and engineering principles to pathophysiology primarily in order to revise clinical practice. This chapter reviews the existing, translational applications of computational simulations and related approaches as applied to inflammation.

An adequately robust early TNF-alpha response is a hallmark of survival following trauma/hemorrhage

Background

Trauma/hemorrhagic shock (T/HS) results in cytokine-mediated acute inflammation that is generally considered detrimental.

Methodology/Principal Findings

Paradoxically, plasma levels of the early inflammatory cytokine TNF-α (but not IL-6, IL-10, or NO₂^-/NO₃^-) were significantly elevated within 6 h post-admission in 19 human trauma survivors vs. 4 non-survivors. Moreover, plasma TNF-α was inversely correlated with Marshall Score, an index of organ dysfunction, both in the 23 patients taken together and in the survivor cohort. Accordingly, we hypothesized that if an early, robust pro-inflammatory response were to be a marker of an appropriate response to injury, then individuals exhibiting such a response would be predisposed to survive. We tested this hypothesis in swine subjected to various experimental paradigms of T/HS. Twenty-three anesthetized pigs were subjected to T/HS (12 HS-only and 11 HS + Thoracotomy; mean arterial pressure of 30 mmHg for 45–90 min) along with surgery-only controls. Plasma obtained at pre-surgery, baseline post-surgery, beginning of HS, and every 15 min thereafter until 75 min (in the HS only group) or 90 min (in the HS + Thoracotomy group) was assayed for TNF-α, IL-6, IL-10, and NO₂^-/NO₃^-. Mean post-surgery±HS TNF-α levels were significantly higher in the survivors vs. non-survivors, while non-survivors exhibited no measurable change in TNF-α levels over the same interval.

Conclusions/Significance

Contrary to the current dogma, survival in the setting of severe, acute T/HS appears to be associated with an immediate increase in serum TNF-α. It is currently unclear if this response was the cause of this protection, a marker of survival, or both. This abstract won a Young Investigator Travel Award at the SHOCK 2008 meeting in Cologne, Germany.

NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION

Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance.