Nabil Azhar

Analysis of Serum Inflammatory Mediators Identifies Unique Dynamic Networks Associated with Death and Spontaneous Survival in Pediatric Acute Liver Failure

Background Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF. Methods We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference. Findings Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA. Interpretation The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.

Inducible protein-10, a potential driver of neurally controlled interleukin-10 and morbidity in human blunt trauma.

Objective:Blunt trauma and traumatic spinal cord injury induce systemic inflammation that contributes to morbidity. Dysregulated neural control of systemic inflammation postinjury is likely exaggerated in patients with traumatic spinal cord injury. We used in silico methods to discern dynamic inflammatory networks that could distinguish systemic inflammation in traumatic spinal cord injury from blunt trauma. Design:Retrospective study. Settings:Tertiary care institution. Patients:Twenty-one severely injured thoracocervical traumatic spinal cord injury patients and matched 21 severely injured blunt trauma patients without spinal cord injury. Intervention:None. Measurements and Main Results:Serial blood samples were obtained from days 1 to 14 postinjury. Twenty-four plasma inflammatory mediators were quantified. Statistical significance between the two groups was determined by two-way analysis of variance. Dynamic Bayesian network inference was used to suggest dynamic connectivity and central inflammatory mediators. Circulating interleukin-10 was significantly elevated in thoracocervical traumatic spinal cord injury group versus non–spinal cord injury group, whereas interleukin-1&bgr;, soluble interleukin-2 receptor-&agr;, interleukin-4, interleukin-5, interleukin-7, interleukin-13, interleukin-17, macrophage inflammatory protein 1&agr; and 1&bgr;, granulocyte-macrophage colony-stimulating factor, and interferon-&ggr; were significantly reduced in traumatic spinal cord injury group versus non–spinal cord injury group. Dynamic Bayesian network suggested that post-spinal cord injury interleukin-10 is driven by inducible protein-10, whereas monocyte chemotactic protein-1 was central in non–spinal cord injury dynamic networks. In a separate validation cohorts of 356 patients without spinal cord injury and 85 traumatic spinal cord injury patients, individuals with plasma inducible protein-10 levels more than or equal to 730 pg/mL had significantly prolonged hospital and ICU stay and days on mechanical ventilator versus patients with plasma inducible protein-10 level less than 730 pg/mL. Conclusion:This is the first study to compare the dynamic systemic inflammatory responses of traumatic spinal cord injury patients versus patients without spinal cord injury, suggesting a key role for inducible protein-10 in driving systemic interleukin-10 and morbidity and highlighting the potential utility of in silico tools to identify key inflammatory drivers.

Removal of inflammatory ascites is associated with dynamic modification of local and systemic inflammation along with prevention of acute lung injury: in vivo and in silico studies.

ABSTRACT Background: Sepsis-induced inflammation in the gut/peritoneal compartment occurs early in sepsis and can lead to acute lung injury (ALI). We have suggested that inflammatory ascites drives the pathogenesis of ALI and that removal of ascites with an abdominal wound vacuum prevents ALI. We hypothesized that the time- and compartment-dependent changes in inflammation that determine this process can be discerned using principal component analysis (PCA) and Dynamic Bayesian Network (DBN) inference. Methods: To test this hypothesis, data from a previous study were analyzed using PCA and DBN. In that study, two groups of anesthetized, ventilated pigs were subjected to experimental sepsis via intestinal ischemia/reperfusion and placement of a peritoneal fecal clot. The control group (n = 6) had the abdomen opened at 12 h after injury (T12) with attachment of a passive drain. The peritoneal suction treatment (PST) group (n = 6) was treated in an identical fashion except that a vacuum was applied to the peritoneal cavity at T12 to remove ascites and maintained until T48. Multiple inflammatory mediators were measured in ascites and plasma and related to lung function (PaO2/FIO2 ratio and oxygen index) using PCA and DBN. Results: Peritoneal suction treatment prevented ALI based on lung histopathology, whereas control animals developed ALI. Principal component analysis revealed that local to the insult (i.e., ascites), primary proinflammatory cytokines play a decreased role in the overall response in the treatment group as compared with control. In both groups, multiple, nested positive feedback loops were inferred from DBN, which included interrelated roles for bacterial endotoxin, interleukin 6, transforming growth factor &bgr;1, C-reactive protein, PaO2/FIO2 ratio, and oxygen index. von Willebrand factor was an output in control, but not PST, ascites. Conclusions: These combined in vivo and in silico studies suggest that in this clinically realistic paradigm of sepsis, endotoxin drives the inflammatory response in the ascites, interplaying with lung dysfunction in a feed-forward loop that exacerbates inflammation and leads to endothelial dysfunction, systemic spillover, and ALI; PST partially modifies this process.

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

SIGNIFICANCE Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. RECENT ADVANCES Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. CRITICAL ISSUES Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments. FUTURE DIRECTIONS These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.

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

Objective:Blunt trauma patients may present with similar demographics and injury severity yet differ with regard to survival. We hypothesized that this divergence was due to different trajectories of systemic inflammation and utilized computational analyses to define these differences. Design:Retrospective clinical study and experimental study in mice. Setting:Level 1 trauma center and experimental laboratory. Patients:From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24 hours but ultimately died (nonsurvivors; n = 19) and patients who, after ICU admission, went on to be discharged(survivors; n = 19). Interventions:None in patients. Neutralizing anti-interleukin-17A antibody in mice. Measurements and Main Results:Data on systemic inflammatory mediators assessed within the first 24 hours and over 7 days were analyzed with computational modeling to infer dynamic networks of inflammation. Network density among inflammatory mediators in nonsurvivors increased in parallel with organ dysfunction scores over 7 days, suggesting the presence of early, self-sustaining, pathologic inflammation involving high-mobility group protein B1, interleukin-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine interleukin-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-interleukin-17A. Conclusions:Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality after blunt trauma and suggest a lymphoid cell–based switch from self-resolving to self-sustaining inflammation.

In vivo and systems biology studies implicate IL-18 as a central mediator in chronic pain

Inflammation is associated with peripheral neuropathy, however the interplay among cytokines, chemokines, and neurons is still unclear. We hypothesized that this neuroinflammatory interaction can be defined by computational modeling based on the dynamics of protein expression in the sciatic nerve of rats subjected to chronic constriction injury. Using Dynamic Bayesian Network inference, we identified interleukin (IL)-18 as a central node associated with neuropathic pain in this animal model. Immunofluorescence supported a role for inflammasome activation and induction of IL-18 at the site of injury. Combined in vivo and in silico approaches may thus highlight novel targets in peripheral neuropathy.

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.

Modeling Host–Vector–Pathogen Immuno-inflammatory Interactions in Malaria

Half of the global population is at risk for malaria, which results in nearly one million deaths annually, 86 % of which are in children [1]. Plasmodium falciparum, the most important human malaria parasite, is transmitted by female Anopheles mosquitoes. Parasite development in the mosquito begins with the ingestion of blood containing sexualstage gametocytes. Mobile ookinetes penetrate the midgut epithelium 24–36 h later and transform into midgut-bound oocysts within the open circulatory system of the mosquito. Oocysts grow and develop for 10–12 days and then release thousands of sporozoites, which invade the salivary glands and are released during later blood feeding by the mosquito.

Identification of a novel pathway of transforming growth factor-β1 regulation by extracellular NAD+ in mouse macrophages: in vitro and in silico studies.

Background: Both extracellular NAD+ and the cytokine TGF-β1 are anti-inflammatory. Results: NAD+ increases both active and latent TGF-β1 in mouse macrophages. A mathematical model partially explains the complex effects of NAD+ on TGF-β1. Conclusion: NAD+ is a novel modulator of TGF-β1. Significance: Combined in vitro and in silico approaches may help elucidate novel pathways of TGF-β1 regulation. Extracellular β-nicotinamide adenine dinucleotide (NAD+) is anti-inflammatory. We hypothesized that NAD+ would modulate the anti-inflammatory cytokine Transforming Growth Factor (TGF)-β1. Indeed, NAD+ led to increases in both active and latent cell-associated TGF-β1 in RAW 264.7 mouse macrophages as well as in primary peritoneal macrophages isolated from both C3H/HeJ (TLR4-mutant) and C3H/HeOuJ (wild-type controls for C3H/HeJ) mice. NAD+ acts partially via cyclic ADP-ribose (cADPR) and subsequent release of Ca2+. Treatment of macrophages with the cADPR analog 3-deaza-cADPR or Ca2+ ionophores recapitulated the effects of NAD+ on TGF-β1, whereas the cADPR antagonist 8-Br-cADPR, Ca2+ chelation, and antagonism of L-type Ca2+ channels suppressed these effects. The time and dose effects of NAD+ on TGF-β1 were complex and could be modeled both statistically and mathematically. Model-predicted levels of TGF-β1 protein and mRNA were largely confirmed experimentally but also suggested the presence of other mechanisms of regulation of TGF-β1 by NAD+. Thus, in vitro and in silico evidence points to NAD+ as a novel modulator of TGF-β1.

Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma

Inflammation can drive both homeostasis and disease via dynamic, multiscale processes. The inflammatory response can be studied using multiplexed platforms, but there is no straightforward means by which to deal with the consequent “data deluge” in order to glean basic insights and clinically useful applications. Systems approaches, including data-driven and mechanistic computational modeling, have been employed in order to study the acute inflammatory response in the settings of trauma/hemorrhage and sepsis. Through combined data-driven and mechanistic modeling based on such “meso-dimensional” datasets, computational models of acute inflammation applicable to multiple preclinical species as well as humans were generated. A key hypothesis derived from these studies is that inflammation may be regulated via positive feedback loops that control switching between beneficial and detrimental inflammatory responses. Self-resolving inflammation may occur when specific signals feedback in a positive fashion to drive anti-inflammatory responses, while proinflammatory signals remain below certain thresholds. In contrast, self-amplifying, detrimental inflammation may occur when different signals feedback in a positive fashion to drive proinflammatory responses, setting in motion the positive feedback loop of inflammation → tissue damage/dysfunction → inflammation driven by damage-associated molecular pattern molecules. These insights may drive a future generation of targeted, personalized therapies for acute inflammation.