Network topologies and dynamics leading to endotoxin tolerance and priming in innate immune cells

Macrophage memory emerges from coordinated transcription factor and chromatin dynamics

Cells of the immune system operate in dynamic microenvironments where the timing, concentration, and order of signaling molecules constantly change. Despite this complexity, immune cells manage to communicate accurately and control inflammation and infection. It is unclear how these dynamic signals are encoded and decoded and if individual cells retain the memory of past exposure to inflammatory molecules. Here, we use live-cell analysis, ATAC sequencing, and an in vivo model of sepsis to show that sequential inflammatory signals induce memory in individual macrophages through reprogramming the nuclear factor κB (NF-κB) network and the chromatin accessibility landscape. We use transcriptomic profiling and deep learning to show that transcription factor and chromatin dynamics coordinate fine-tuned macrophage responses to new inflammatory signals. This work demonstrates how macrophages retain the memory of previous signals despite single-cell variability and elucidates the mechanisms of signal-induced memory in dynamic inflammatory conditions like sepsis.

Hormesis as an adaptive response to infection

Hormesis is a phenomenon whereby low-level stress can improve cellular, organ, or organismal fitness in response to a subsequent similar or other stress insult. Whereas hormesis is thought to contribute to the fitness benefits arising from symbiotic host-microbe interactions, the putative benefits of hormesis in host-pathogen interactions have yet to be explored. Hormetic responses have nonetheless been reported in experimental models of infection, a common feature of which is regulation of host mitochondrial function. We propose that these mitohormetic responses could be harnessed therapeutically to limit the severity of infectious diseases.

Epigenetic regulation of innate immune dynamics during inflammation

Innate immune cells play essential roles in modulating both immune defense and inflammation by expressing a diverse array of cytokines and inflammatory mediators, phagocytizing pathogens to promote immune clearance, and assisting with the adaptive immune processes through antigen presentation. Rudimentary innate immune "memory" states such as training, tolerance, and exhaustion develop based on the nature, strength, and duration of immune challenge, thereby enabling dynamic transcriptional reprogramming to alter present and future cell behavior. Underlying transcriptional reprogramming are broad changes to the epigenome, or chromatin alterations above the level of DNA sequence. These changes include direct modification of DNA through cytosine methylation as well as indirect modifications through alterations to histones that comprise the protein core of nucleosomes. In this review, we will discuss recent advances in our understanding of how these epigenetic changes influence the dynamic behavior of the innate immune system during both acute and chronic inflammation, as well as how stable changes to the epigenome result in long-term alterations of innate cell behavior related to pathophysiology.

Processing stimulus dynamics by the NF-κB network in single cells

Cells at the site of an infection experience numerous biochemical signals that vary in amplitude, space, and time. Despite the diversity of dynamic signals produced by pathogens and sentinel cells, information-processing pathways converge on a limited number of central signaling nodes to ultimately control cellular responses. In particular, the NF-κB pathway responds to dozens of signals from pathogens and self, and plays a vital role in processing proinflammatory inputs. Studies addressing the influence of stimulus dynamics on NF-κB signaling are rare due to technical limitations with live-cell measurements. However, recent advances in microfluidics, automation, and image analysis have enabled investigations that yield high temporal resolution at the single-cell level. Here, we summarize the recent research which measures and models the NF-κB response to pulsatile and fluctuating stimulus concentrations, as well as different combinations and sequences of signaling molecules. Collectively, these studies show that the NF-κB network integrates external inflammatory signals and translates these into downstream transcriptional responses.

Constructing maps between distinct cell fates and parametric conditions by systematic perturbations

MOTIVATION

Cell fate transitions are common in many developmental processes. Therefore, identifying the mechanisms behind them is crucial. Traditionally, due to complexity of networks and existence of plenty of kinetic parameters, dynamical analysis of biomolecular networks can only be performed by simultaneously perturbing a small number of parameters. Although many efforts have focused on how cell states change under specific perturbations, conversely, how to infer parametric conditions underlying distinct cell fates by systematic perturbations is less clear and needs to be further investigated.

RESULTS

In this article, we present a general computational method by integrating systematic perturbations, unsupervised clustering, principal component analysis, and fitting analysis. The method can be used to to construct maps between distinct cell fates and parametric conditions by systematic perturbations. In particular, there are no needs of accurate parameter measurements and occurrence of bifurcations to establish the maps. To validate feasibility and inference performance of the method, we use toggle switch, inner cell mass, and epithelial mesenchymal transition as model systems to show how the maps are constructed and how system parameters encode essential information on cell fates. The maps tell us how systematic perturbations drive cell fate decisions and transitions, and allow us to purposefully predict, manipulate, and even control cell states. The approach is especially helpful in understanding crucial roles of certain parameter combinations during fate transitions. We hope that the approach can provide us valuable information on parametric or perturbation conditions so some specific targets, e.g. directional differentiation, can be realized.

AVAILABILITY AND IMPLEMENTATION

No public data are used. The data we used are generated by randomly chosen values of model parameters in certain ranges, and the corresponding parameters are already attached in supplementary materials.

Modelling Microglial Innate Immune Memory In Vitro: Understanding the Role of Aerobic Glycolysis in Innate Immune Memory

Microglia, the resident macrophages of the central nervous system, play important roles in maintaining brain homeostasis and facilitating the brain's innate immune responses. Following immune challenges microglia also retain immune memories, which can alter responses to secondary inflammatory challenges. Microglia have two main memory states, training and tolerance, which are associated with increased and attenuated expression of inflammatory cytokines, respectively. However, the mechanisms differentiating these two distinct states are not well understood. We investigated mechanisms underlying training versus tolerance memory paradigms in vitro in BV2 cells using B-cell-activating factor (BAFF) or bacterial lipopolysaccharide (LPS) as a priming stimulus followed by LPS as a second stimulus. BAFF followed by LPS showed enhanced responses indicative of priming, whereas LPS followed by LPS as the second stimulus caused reduced responses suggestive of tolerance. The main difference between the BAFF versus the LPS stimulus was the induction of aerobic glycolysis by LPS. Inhibiting aerobic glycolysis during the priming stimulus using sodium oxamate prevented the establishment of the tolerized memory state. In addition, tolerized microglia were unable to induce aerobic glycolysis upon LPS restimulus. Therefore, we conclude that aerobic glycolysis triggered by the first LPS stimulus was a critical step in the induction of innate immune tolerance.

Low-Dose Lipopolysaccharide Protects from Lethal Paramyxovirus Infection in a Macrophage- and TLR4-Dependent Process

Respiratory diseases are a major public health burden and a leading cause of death and disability in the world. Understanding antiviral immune responses is crucial to alleviate morbidity and mortality associated with these respiratory viral infections. Previous data from human and animal studies suggested that pre-existing atopy may provide some protection against severe disease from a respiratory viral infection. However, the mechanism(s) of protection is not understood. Low-dose LPS has been shown to drive an atopic phenotype in mice. In addition, LPS has been shown in vitro to have an antiviral effect. We examined the effect of LPS treatment on mortality to the murine parainfluenza virus Sendai virus. Low-dose LPS treatment 24 h before inoculation with a normally lethal dose of Sendai virus greatly reduced death. This protection was associated with a reduced viral titer and reduced inflammatory cytokine production in the airways. The administration of LPS was associated with a marked increase in lung neutrophils and macrophages. Depletion of neutrophils failed to reverse the protective effect of LPS; however, depletion of macrophages reversed the protective effect of LPS. Further, we demonstrate that the protective effect of LPS depends on type I IFN and TLR4-MyD88 signaling. Together, these studies demonstrate pretreatment with low-dose LPS provides a survival advantage against a severe respiratory viral infection through a macrophage-, TLR4-, and MyD88-dependent pathway.

NF-κB memory coordinates transcriptional responses to dynamic inflammatory stimuli

Many scenarios in cellular communication require cells to interpret multiple dynamic signals. It is unclear how exposure to inflammatory stimuli alters transcriptional responses to subsequent stimulus. Using high-throughput microfluidic live-cell analysis, we systematically profile the NF-κB response to different signal sequences in single cells. We find that NF-κB dynamics store the short-term history of received signals: depending on the prior pathogenic or cytokine signal, the NF-κB response to subsequent stimuli varies from no response to full activation. Using information theory, we reveal that these stimulus-dependent changes in the NF-κB response encode and reflect information about the identity and dose of the prior stimulus. Small-molecule inhibition, computational modeling, and gene expression profiling show that this encoding is driven by stimulus-dependent engagement of negative feedback modules. These results provide a model for how signal transduction networks process sequences of inflammatory stimuli to coordinate cellular responses in complex dynamic environments.

Signal-Strength and History-Dependent Innate Immune Memory Dynamics in Health and Disease

Innate immunity exhibits memory characteristics, reflected not only in selective recognition of external microbial or internal damage signals, but more importantly in history and signal-strength dependent reprogramming of innate leukocytes characterized by priming, tolerance, and exhaustion. Key innate immune cells such as monocytes and neutrophils can finely discern and attune to the duration and intensity of external signals through rewiring of internal signaling circuitries, giving rise to a vast array of discreet memory phenotypes critically relevant to managing tissue homeostasis as well as diverse repertoires of inflammatory conditions. This review will highlight recent advances in this rapidly expanding field of innate immune programming and memory, as well as its translational implication in the pathophysiology of selected inflammatory diseases.

Two Faces of Macrophages: Training and Tolerance

Macrophages are present in almost all body tissues. They detect and quickly respond to “environmental signals” in the tissue. Macrophages have been associated with numerous beneficial roles, such as host defense, wound healing, and tissue regeneration; however, they have also been linked to the development of diverse illnesses, particularly cancers and autoimmune disorders. Complex signaling, epigenetic, and metabolic pathways drive macrophage training and tolerance. The induced intracellular program differs depending on the type of initial stimuli and the tissue microenvironment. Due to the essential roles of macrophages in homeostatic and their association with the pathogenesis of inflammatory diseases, recent studies have investigated the molecular mechanisms of macrophage training and tolerance. This review discusses the role of factors involved in macrophage training and tolerance, along with the current studies in human diseases.

Development of Exhausted Memory Monocytes and Underlying Mechanisms

Pathogenic inflammation and immuno-suppression are cardinal features of exhausted monocytes increasingly recognized in septic patients and murine models of sepsis. However, underlying mechanisms responsible for the generation of exhausted monocytes have not been addressed. In this report, we examined the generation of exhausted primary murine monocytes through prolonged and repetitive challenges with high dose bacterial endotoxin lipopolysaccharide (LPS). We demonstrated that repetitive LPS challenges skew monocytes into the classically exhausted Ly6Chi population, and deplete the homeostatic non-classical Ly6Clo population, reminiscent of monocyte exhaustion in septic patients. scRNAseq analyses confirmed the expansion of Ly6Chi monocyte cluster, with elevation of pathogenic inflammatory genes previously observed in human septic patients. Furthermore, we identified CD38 as an inflammatory mediator of exhausted monocytes, associated with a drastic depletion of cellular NAD+; elevation of ROS; and compromise of mitochondria respiration, representative of septic monocytes. Mechanistically, we revealed that STAT1 is robustly elevated and sustained in LPS-exhausted monocytes, dependent upon the TRAM adaptor of the TLR4 pathway. TRAM deficient monocytes are largely resistant to LPS-mediated exhaustion, and retain the non-classical homeostatic features. Together, our current study addresses an important yet less-examined area of monocyte exhaustion, by providing phenotypic and mechanistic insights regarding the generation of exhausted monocytes.

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

Mathematical Modeling of Plasticity and Heterogeneity in EMT

The epithelial-mesenchymal transition (EMT) and the corresponding reverse process, mesenchymal-epithelial transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at both biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: (a) multi-stability-how many phenotypes can cells attain during an EMT/MET?, (b) reversibility/irreversibility-what time and/or concentration of an EMT inducer marks the "tipping point" when cells induced to undergo EMT cannot revert?, (c) symmetry in EMT/MET-do cells take the same path when reverting as they took during the induction of EMT?, and (d) non-cell autonomous mechanisms-how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT? These dynamical traits may facilitate a heterogenous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics.

Trained immunity modulates inflammation-induced fibrosis

Chronic inflammation and fibrosis can result from inappropriately activated immune responses that are mediated by macrophages. Macrophages can acquire memory-like characteristics in response to antigen exposure. Here, we show the effect of BCG or low-dose LPS stimulation on macrophage phenotype, cytokine production, chromatin and metabolic modifications. Low-dose LPS training alleviates fibrosis and inflammation in a mouse model of systemic sclerosis (SSc), whereas BCG-training exacerbates disease in this model. Adoptive transfer of low-dose LPS-trained or BCG-trained macrophages also has beneficial or harmful effects, respectively. Furthermore, coculture with low-dose LPS trained macrophages reduces the fibro-inflammatory profile of fibroblasts from mice and patients with SSc, indicating that trained immunity might be a phenomenon that can be targeted to treat SSc and other autoimmune and inflammatory fibrotic disorders.

Innate immune cells can be trained by some stimuli or pathogen exposures to be metabolically and epigenetically altered such that they have different responses to subsequent exposures. Here the authors show that low-dose LPS trained macrophages and BCG-trained macrophages have opposing effects on fibrosis and inflammation in the context of systemic sclerosis.

Myalgic encephalomyelitis or chronic fatigue syndrome: how could the illness develop?

A model of the development and progression of chronic fatigue syndrome (myalgic encephalomyelitis), the aetiology of which is currently unknown, is put forward, starting with a consideration of the post-infection role of damage-associated molecular patterns and the development of chronic inflammatory, oxidative and nitrosative stress in genetically predisposed individuals. The consequences are detailed, including the role of increased intestinal permeability and the translocation of commensal antigens into the circulation, and the development of dysautonomia, neuroinflammation, and neurocognitive and neuroimaging abnormalities. Increasing levels of such stress and the switch to immune and metabolic downregulation are detailed next in relation to the advent of hypernitrosylation, impaired mitochondrial performance, immune suppression, cellular hibernation, endotoxin tolerance and sirtuin 1 activation. The role of chronic stress and the development of endotoxin tolerance via indoleamine 2,3-dioxygenase upregulation and the characteristics of neutrophils, monocytes, macrophages and T cells, including regulatory T cells, in endotoxin tolerance are detailed next. Finally, it is shown how the immune and metabolic abnormalities of chronic fatigue syndrome can be explained by endotoxin tolerance, thus completing the model.

An enriched network motif family regulates multistep cell fate transitions with restricted reversibility

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.

The functions of cells are dynamically controlled in many biological processes including development, regeneration and disease progression. Cell fate transition, or the switch of cellular functions, often involves multiple steps. The intermediate stages of the transition provide the biological systems with the opportunities to regulate the transitions in a precise manner. These transitions are controlled by key regulatory genes of which the expression shows stepwise patterns, but how the interactions of these genes can determine the multistep processes was unclear. Here, we present a comprehensive analysis on the design principles of gene circuits that govern multistep cell fate transition. We found a large network family with common structural features that can generate systems with the ability to control three consecutive steps of the transition. We found that this type of networks is enriched in a gene circuit controlling the development of T lymphocyte, a crucial type of immune cells. We performed mathematical modeling using this gene circuit and we recapitulated the stepwise and irreversible loss of stem cell properties of the developing T lymphocytes. Our findings can be useful to analyze a wide range of gene regulatory networks controlling multistep cell fate transitions.

Immunometabolic Crosstalk: An Ancestral Principle of Trained Immunity?

Memory was traditionally considered an exclusive hallmark of adaptive immunity. This dogma was challenged by recent reports that myeloid cells can retain 'memory' of earlier challenges, enabling them to respond strongly to a secondary stimulus. This process, designated 'trained immunity', is initiated by modulation of precursors of myeloid cells in the bone marrow. The ancestral innate immune system of lower organisms (e.g., Caenorhabditis elegans) can build long-lasting memory that modifies responses to secondary pathogen encounters. We posit that changes in cellular metabolism may be a common denominator of innate immune memory from lower animals to mammals. We discuss evidence from C. elegans and murine/human systems supporting the concept of an ancestral principle regulating innate immune memory by controlling cellular metabolism.

Modeling the dynamic behavior of biochemical regulatory networks

Strategies for modeling the complex dynamical behavior of gene/protein regulatory networks have evolved over the last 50 years as both the knowledge of these molecular control systems and the power of computing resources have increased. Here, we review a number of common modeling approaches, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches). We discuss the pro's and con's of each approach, to help novice modelers choose a modeling strategy suitable to their problem, based on the type and bounty of available experimental information. We illustrate different modeling strategies in terms of some abstract network motifs, and in the specific context of cell cycle regulation.

Modeling the Bistable Dynamics of the Innate Immune System

The size of primary challenge with lipopolysaccharide induces changes in the innate immune cells phenotype between pro-inflammatory and pro-tolerant states when facing a secondary lipopolysaccharide challenge. To determine the molecular mechanisms governing this differential response, we propose a mathematical model for the interaction between three proteins involved in the immune cell decision making: IRAK-1, PI3K, and RelB. The mutual inhibition of IRAK-1 and PI3K in the model leads to bistable dynamics. By using the levels of RelB as indicative of strength of the immune responses, we connect the size of different primary lipopolysaccharide doses to the differential phenotypical outcomes following a secondary challenge. We further predict under what circumstances the primary LPS dose does not influence the response to a secondary challenge. Our results can be used to guide treatments for patients with either autoimmune disease or compromised immune system.

Recent advances in endotoxin tolerance

Endotoxin tolerance is defined as a reduced capacity of a cell to respond endotoxin (lipopolysaccharide, LPS) challenge after an initial encounter with endotoxin in advance. The body becomes tolerant to subsequent challenge with a lethal dose of endotoxin and cytokines release and cell/tissue damage induced by inflammatory reaction are significantly reduced in the state of endotoxin tolerance. The main characteristics of endotoxin tolerance are downregulation of inflammatory mediators such as tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and C-X-C motif chemokine 10 (CXCL10) and upregulation of anti-inflammatory cytokines such as IL-10 and transforming growth factor β (TGF-β). Therefore, endotoxin tolerance is often regarded as the regulatory mechanism of the host against excessive inflammation. Endotoxin tolerance is a complex pathophysiological process and involved in multiple cellular signal pathways, receptor alterations, and biological molecules. However, the exact mechanism remains elusive up to date. To better understand the underlying cellular and molecular mechanisms of endotoxin tolerance, it is crucial to investigate the comprehensive cellular signal pathways, signaling proteins, cell surface molecules, proinflammatory and anti-inflammatory cytokines, and other mediators. Endotoxin tolerance plays an important role in reducing the mortality of sepsis, endotoxin shock, and other endotoxin-related diseases. Recent reports indicated that endotoxin tolerance is also related to other diseases such as cystic fibrosis, acute coronary syndrome, liver ischemia-reperfusion injury, and cancer. The aim of this review is to discuss the recent advances in endotoxin tolerance mainly based on the cellular and molecular mechanisms by outline the current state of the knowledge of the involvement of the toll-like receptor 4 (TLR4) signaling pathways, negative regulate factor, microRNAs, apoptosis, chromatin modification, and gene reprogramming of immune cells in endotoxin tolerance. We hope to provide a new idea and scientific basis for the rational treatment of endotoxin-related diseases such as endotoxemia, sepsis, and endotoxin shock clinically.

Inflammatory processes during acute respiratory distress syndrome: a complex system

PURPOSE OF REVIEW

ARDS is a severe pulmonary disease characterized by inflammation. However, inflammation-directed therapies have yet failed to improve the outcome in ARDS patients. One of the reasons may be the underestimated complexity of inflammation. Here, we summarize recent insights into the complex interrelations between inflammatory circuits.

RECENT FINDINGS

Gene expression analysis from animal models or from patients with ARDS, sepsis or trauma show an enormous number of differentially expressed genes with highly significant overlaps between the various conditions. These similarities, however, should not obscure the complexity of inflammation. We suggest to consider inflammation in ARDS as a system controlled by scale-free networks of genome-wide molecular interaction with hubs (e.g. NFκB, C/EBPβ, ATF3), exhibiting nonlinear emergence and the ability to adapt, meaning for instance that mild and life-threatening inflammation in ARDS are distinct processes. In order to comprehend this complex system, it seems necessary to combine model-driven simulations, data-driven modelling and hypothesis-driven experimental studies. Recent experimental studies have illustrated how several regulatory circuits interact during pulmonary inflammation, including the resolution of inflammation, the inflammasome, autophagy and apoptosis.

SUMMARY

We suggest that therapeutic interventions in ARDS should be based on a systems approach to inflammation.

Remembering Pathogen Dose: Long-Term Adaptation in Innate Immunity

Recent investigations reveal memory-like adaptive responses of the innate immune system to sequential pathogen challenge. Of note, opposing effects that include both sensitization ('training') and desensitization ('tolerance') have been reported. While hitherto the nature of the pathogen was thought to be of prime importance, we propose that pathogen dose plays a key role in determining these opposing effects. Within this concept, training and tolerance of innate immune cells emerge as adaptive responses to increasing pathogen load. Furthermore, environmental stressors significantly impact the pathogen-induced responses of these innate immune cells. Therefore, we hypothesize that pathogens, like other stressors, provoke hormetic responses of the affected cells. This concept could explain the tight interplay of dose-related effects of pathogens and other stressors in infectious diseases.

Difficulties in modelling ARDS (2017 Grover Conference Series)

Fifty years after the first description of acute respiratory distress syndrome (ARDS), none of the many positive drug studies in animal models have been confirmed in clinical trials and translated into clinical practice. This bleak outcome of so many animal experiments shows how difficult it is to model ARDS. Lungs from patients are characterized by hyperinflammation, permeability edema, and hypoxemia; accordingly, this is what most models aim to reproduce. However, in animal models it is very easy to cause inflammation in the lungs, but difficult to cause hypoxemia. Often - and not unlike in patients - models with hypoxemia are accompanied by cardiovascular failure that necessitates fluid support and ventilation, raising the question as to the role of intensive care measures in models of ARDS. In our opinion, there are two major arguments in favor of modelling intensive care medicine in models of ARDS: (1) preventing death from shock; and (2) modelling ventilation and other ICU measures as a second hit. The preferable predictive endpoints in any model of ARDS remain unclear. At present, the best recommendation is to use endpoints that can be compared across studies (i.e. PaO₂/FiO₂ ratio, compliance, wet-to-dry weight ratio) rather than percentage data. Another important and often overlooked issue is the fact that the thermoneutral environmental temperatures for mice and rats are 30℃ and 28℃, respectively; thus, at room temperature (20-22℃) they suffer from cold stress with the associated significant metabolic changes. While, by definition, any model is an abstraction, we suggest that clinically relevant models of ARDS will have to closer recapitulate important properties of the disease while taking into account species-specific confounders.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Complex diseases such as hypertension often involve maladaptive autonomic nervous system control over the cardiovascular, renal, hepatic, immune, and endocrine systems. We studied the pathogenesis of physiological homeostasis by examining the temporal dynamics of gene expression levels from multiple organs in an animal model of autonomic dysfunction characterized by cardiovascular disease, metabolic dysregulation, and immune system aberrations. We employed a data-driven modeling approach to jointly predict continuous gene expression dynamics and gene regulatory interactions across organs in the disease and control phenotypes. We combined our analyses of multi-organ gene regulatory network dynamics and connectivity with bioinformatic analyses of genetic mutations that could regulate gene expression. Our multi-organ modeling approach to investigate the mechanisms of complex disease pathogenesis revealed novel candidates for therapeutic interventions against the development and progression of complex diseases involving autonomic nervous system dysfunction.

Programming and memory dynamics of innate leukocytes during tissue homeostasis and inflammation

The field of innate immunity is witnessing a paradigm shift regarding "memory" and "programming" dynamics. Past studies of innate leukocytes characterized them as first responders to danger signals with no memory. However, recent findings suggest that innate leukocytes, such as monocytes and neutrophils, are capable of "memorizing" not only the chemical nature but also the history and dosages of external stimulants. As a consequence, innate leukocytes can be dynamically programmed or reprogrammed into complex inflammatory memory states. Key examples of innate leukocyte memory dynamics include the development of primed and tolerant monocytes when "programmed" with a variety of inflammatory stimulants at varying signal strengths. The development of innate leukocyte memory may have far-reaching translational implications, as programmed innate leukocytes may affect the pathogenesis of both acute and chronic inflammatory diseases. This review intends to critically discuss some of the recent studies that address this emerging concept and its implication in the pathogenesis of inflammatory diseases.

Modeling cytokine regulatory network dynamics driving neuroinflammation in central nervous system disorders

A central goal of pharmacological efforts to treat central nervous system (CNS) diseases is to develop systemic therapeutics that can restore CNS homeostasis. Achieving this goal requires a fundamental understanding of CNS function within the organismal context so as to leverage the mechanistic insights on the molecular basis of cellular and tissue functions towards novel drug target identification. The immune system constitutes a key link between the periphery and CNS, and many neurological disorders and neurodegenerative diseases are characterized by immune dysfunction. We review the salient opportunities for applying computational models to CNS disease research, and summarize relevant approaches from studies of immune function and neuroinflammation. While the accurate prediction of disease-related phenomena is often considered the central goal of modeling studies, we highlight the utility of computational modeling applications beyond making predictions, particularly for drawing counterintuitive insights from model-based analysis of multi-parametric and time series data sets.

Molecular Mechanisms That Underlie the Dynamic Adaptation of Innate Monocyte Memory to Varying Stimulant Strength of TLR Ligands

In adaptation to rising stimulant strength, innate monocytes can be dynamically programed to preferentially express either pro- or anti-inflammatory mediators. Such dynamic innate adaptation or programing may bear profound relevance in host health and disease. However, molecular mechanisms that govern innate adaptation to varying strength of stimulants are not well understood. Using lipopolysaccharide (LPS), the model stimulant of toll-like-receptor 4 (TLR4), we reported that the expressions of pro-inflammatory mediators are preferentially sustained in monocytes adapted by lower doses of LPS, and suppressed/tolerized in monocytes adapted by higher doses of LPS. Mechanistically, monocytes adapted by super-low dose LPS exhibited higher levels of transcription factor, interferon regulatory factor 5 (IRF5), and reduced levels of transcriptional modulator B lymphocyte-induced maturation protein-1 (Blimp-1). Intriguingly, the inflammatory monocyte adaptation by super-low dose LPS is dependent upon TRAM/TRIF but not MyD88. Similar to LPS, we also observed biphasic inflammatory adaptation and tolerance in monocytes challenged with varying dosages of TLR7 agonist. In sharp contrast, rising doses of TLR3 agonist preferentially caused inflammatory adaptation without inducing tolerance. At the molecular level, the differential regulation of IRF5 and Blimp-1 coincides with unique monocyte adaptation dynamics by TLR4/7 and TLR3 agonists. Our study provides novel clue toward the understanding of monocyte adaptation and memory toward distinct TLR ligands.

Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) and stimulate the innate immune response through the production of cytokines. The innate immune response depends on the timing of encountering PAMPs, suggesting a short-term "memory." In particular, activation of TLR3 appears to prime macrophages for the subsequent activation of TLR7, which leads to synergistically increased production of cytokines. By developing a calibrated mathematical model for the kinetics of TLR3 and TLR7 pathway crosstalk and providing experimental validation, we demonstrated the involvement of the Janus-activated kinase (JAK)-signal transducer and activator of transcription (STAT) pathway in controlling the synergistic production of cytokines. Signaling through this pathway played a dual role: It mediated the synergistic production of cytokines, thus boosting the immune response, and it also maintained homeostasis to avoid an excessive inflammatory response. Thus, we propose that the JAK-STAT pathway provides a cytokine rheostat mechanism, which enables macrophages to fine-tune their responses to multiple, temporally separated infection events involving the TLR3 and TLR7 pathways.

Low-grade inflammatory polarization of monocytes impairs wound healing

Impaired wound healing often accompanies low-grade inflammatory conditions, during which circulating levels of subclinical super-low-dose endotoxin may persist. Low-grade inflammatory monocyte polarization may occur during chronic inflammation and deter effective wound repair. However, little is understood about the potential mechanisms of monocyte polarization by sustained insult of subclinical super-low-dose endotoxin. We observed that super-low-dose endotoxin preferentially programmes a low-grade inflammatory monocyte state in vitro and in vivo, as represented by the elevated population of CD11b(+) Ly6C(high) monocytes and sustained expression of CCR5. Mechanistically, super-low-dose endotoxin caused cellular stress, altered lysosome function and increased the transcription factor IRF5. TUDCA, a potent inhibitor of cellular stress, effectively blocked monocyte polarization and improved wound healing in mice injected with super-low-dose endotoxin. Our data revealed the polarization of low-grade inflammatory monocytes by sustained endotoxin challenge, its underlying mechanisms and a potential intervention strategy. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation

A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.

Super-low dose endotoxin pre-conditioning exacerbates sepsis mortality

Sepsis mortality varies dramatically in individuals of variable immune conditions, with poorly defined mechanisms. This phenomenon complements the hypothesis that innate immunity may adopt rudimentary memory, as demonstrated in vitro with endotoxin priming and tolerance in cultured monocytes. However, previous in vivo studies only examined the protective effect of endotoxin tolerance in the context of sepsis. In sharp contrast, we report herein that pre-conditioning with super-low or low dose endotoxin lipopolysaccharide (LPS) cause strikingly opposite survival outcomes. Mice pre-conditioned with super-low dose LPS experienced severe tissue damage, inflammation, increased bacterial load in circulation, and elevated mortality when they were subjected to cecal-ligation and puncture (CLP). This is in contrast to the well-reported protective phenomenon with CLP mice pre-conditioned with low dose LPS. Mechanistically, we demonstrated that super-low and low dose LPS differentially modulate the formation of neutrophil extracellular trap (NET) in neutrophils. Instead of increased ERK activation and NET formation in neutrophils pre-conditioned with low dose LPS, we observed significantly reduced ERK activation and compromised NET generation in neutrophils pre-conditioned with super-low dose LPS. Collectively, our findings reveal a mechanism potentially responsible for the dynamic programming of innate immunity in vivo as it relates to sepsis risks.

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Super-low dose endotoxin pre-conditioning exacerbates, while higher dose endotoxin alleviates sepsis mortality.

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Super-low dose endotoxin reduces, while higher dose endotoxin facilitates neutrophil extracellular trap (NET) formation.

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Super-low dose endotoxin suppresses, while higher dose endotoxin induces ERK activation required for NET formation.

Myeloid cell-derived inducible nitric oxide synthase suppresses M1 macrophage polarization

Here we show that iNOS-deficient mice display enhanced classically activated M1 macrophage polarization without major effects on alternatively activated M2 macrophages. eNOS and nNOS mutant mice show comparable M1 macrophage polarization compared with wild-type control mice. Addition of N6-(1-iminoethyl)-L-lysine dihydrochloride, an iNOS inhibitor, significantly enhances M1 macrophage polarization while S-nitroso-N-acetylpenicillamine, a NO donor, suppresses M1 macrophage polarization. NO derived from iNOS mediates nitration of tyrosine residues in IRF5 protein, leading to the suppression of IRF5-targeted M1 macrophage signature gene activation. Computational analyses corroborate a circuit that fine-tunes the expression of IL-12 by iNOS in macrophages, potentially enabling versatile responses based on changing microenvironments. Finally, studies of an experimental model of endotoxin shock show that iNOS deficiency results in more severe inflammation with an enhanced M1 macrophage activation phenotype. These results suggest that NO derived from iNOS in activated macrophages suppresses M1 macrophage polarization.

In response to microbial ligands, IRF5 promotes pro-inflammatory M1 macrophage activation and production of nitrous oxide. Here the authors show that nitrous oxide modifies IRF5 tyrosine residues as a negative feedback, limiting the inflammatory response and protecting from endotoxin shock.

Esophageal cancer-related gene-4 (ECRG4) interactions with the innate immunity receptor complex

OBJECTIVE AND DESIGN

The human c2orf40 gene encodes a tumor suppressor gene called esophageal cancer-related gene-4 (ECRG4) with pro- and anti-inflammatory activities that depend on cell surface processing. Here, we investigated its physical and functional association with the innate immunity receptor complex.

METHODS

Interactions between ECRG4 and the innate immunity receptor complex were assessed by flow cytometry, immunohistochemistry, confocal microscopy, and co-immunoprecipitation. Phage display was used for ligand targeting to cells that overexpress the TLR4-MD2-CD14.

RESULTS

Immunoprecipitation and immunohistochemical studies demonstrate a physical interaction between ECRG4 and TLR4-MD2-CD14 on human granulocytes. Flow cytometry shows ECRG4 on the cell surface of a subset of CD14(+) and CD16(+) leukocytes. In a cohort of trauma patients, the C-terminal 16 amino acid domain of ECRG4 (ECRG4(133-148)) appears to be processed and shed, presumably at a thrombin-like consensus sequence. Phage targeting this putative ligand shows that this peptide sequence internalizes into cells through the TLR4/CD14/MD2 complex, but modulates inflammation through non-canonical, NFκB signal transduction.

CONCLUSIONS

ECRG4 is present on the surface of human monocytes and granulocytes. Its interaction with the human innate immunity receptor complex supports a role for cell surface activation of ECRG4 during inflammation and implicates this receptor in its mechanism of action.

Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses

A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

Activation of central muscarinic receptor type 1 prevents development of endotoxin tolerance in rat liver

Endotoxin tolerance is a mechanism in which cells receiving low doses of endotoxin, enter a transient phase with less inflammatory response to the next endotoxin challenges. Central nervous system is known to modulate systemic inflammation through activation of the cholinergic system; however, the role of central anti-inflammatory pathway in pathophysiology of hepatic endotoxin tolerance is unknown. Our study was designed to assess the effect central muscarinic type 1 receptor (M1) activation on development of endotoxin tolerance in rat liver. Endotoxin tolerance was induced by daily intraperitoneal injection of endotoxin (1 mg/kg) for 5 days. Animals were randomly divided into two groups which received intracerebroventricular injection of either MCNA-343 (an M1 agonist, 5 ng/kg) or saline 1h after intraperitoneal injection of saline or endotoxin. The responsiveness to endotoxin was assessed by measuring hepatic MCP-1 (monocyte chemotactic protein-1), iNOS (inducible nitric oxide synthase) and TNF-α (tumor necrosis-α) mRNA expression 3h after intraperitoneal administration of endotoxin using quantitative RT-PCR. A significant reduction in hepatic expression of MCP-1, iNOS and TNF-α was observed in rats with 5 days endotoxin challenge in comparison with rats given a single dose of endotoxin. There was no significant difference in hepatic expression of MCP-1, iNOS or TNF-α between acute and chronic LPS-treated groups in rats given MCNA-343. Central MCNA-343 stimulation could prevent the induction of hepatic endotoxin tolerance in animals receiving repeated doses of endotoxin. This indicates that M1 cholinergic receptor activation in the central nervous system can modulate endotoxin tolerance in rat liver.

Epigenetic state network approach for describing cell phenotypic transitions

Recent breakthroughs of cell phenotype reprogramming impose theoretical challenges on unravelling the complexity of large circuits maintaining cell phenotypes coupled at many different epigenetic and gene regulation levels, and quantitatively describing the phenotypic transition dynamics. A popular picture proposed by Waddington views cell differentiation as a ball sliding down a landscape with valleys corresponding to different cell types separated by ridges. Based on theories of dynamical systems, we establish a novel 'epigenetic state network' framework that captures the global architecture of cell phenotypes, which allows us to translate the metaphorical low-dimensional Waddington epigenetic landscape concept into a simple-yet-predictive rigorous mathematical framework of cell phenotypic transitions. Specifically, we simplify a high-dimensional epigenetic landscape into a collection of discrete states corresponding to stable cell phenotypes connected by optimal transition pathways among them. We then apply the approach to the phenotypic transition processes among fibroblasts (FBs), pluripotent stem cells (PSCs) and cardiomyocytes (CMs). The epigenetic state network for this case predicts three major transition pathways connecting FBs and CMs. One goes by way of PSCs. The other two pathways involve transdifferentiation either indirectly through cardiac progenitor cells or directly from FB to CM. The predicted pathways and multiple intermediate states are supported by existing microarray data and other experiments. Our approach provides a theoretical framework for studying cell phenotypic transitions. Future studies at single-cell levels can directly test the model predictions.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach

Aging is a biological process characterized by the progressive functional decline of many interrelated physiological systems. In particular, aging is associated with the development of a systemic state of low-grade chronic inflammation (inflammaging), and with progressive deterioration of metabolic function. Systems biology has helped in identifying the mediators and pathways involved in these phenomena, mainly through the application of high-throughput screening methods, valued for their molecular comprehensiveness. Nevertheless, inflammation and metabolic regulation are dynamical processes whose behavior must be understood at multiple levels of biological organization (molecular, cellular, organ, and system levels) and on multiple time scales. Mathematical modeling of such behavior, with incorporation of mechanistic knowledge on interactions between inflammatory and metabolic mediators, may help in devising nutritional interventions capable of preventing, or ameliorating, the age-associated functional decline of the corresponding systems.

Lung protective ventilation induces immunotolerance and nitric oxide metabolites in porcine experimental postoperative sepsis

Low tidal volume ventilation is beneficial in patients with severe pulmonary dysfunction and would, in theory, reduce postoperative complications if implemented during routine surgery. The study aimed to investigate whether low tidal volume ventilation and high positive end-expiratory pressure (PEEP) in a large animal model of postoperative sepsis would attenuate the systemic inflammatory response and organ dysfunction. Thirty healthy pigs were randomized to three groups: Group Prot-7h, i.e. protective ventilation for 7 h, was ventilated with a tidal volume of 6 mL x kg^-1 for 7 h; group Prot-5h, i.e. protective ventilation for 5 h, was ventilated with a tidal volume of 10 mL x kg^-1 for 2 h, after which the group was ventilated with a tidal volume of 6 mL x kg^-1; and a control group that was ventilated with a tidal volume of 10 mL x kg^-1 for 7 h. In groups Prot-7h and Prot-5h PEEP was 5 cmH₂O for 2 h and 10 cmH₂O for 5 h. In the control group PEEP was 5 cmH₂O for the entire experiment. After surgery for 2 h, postoperative sepsis was simulated with an endotoxin infusion for 5 h. Low tidal volume ventilation combined with higher PEEP led to lower levels of interleukin 6 and 10 in plasma, higher PaO₂/FiO2, better preserved functional residual capacity and lower plasma troponin I as compared with animals ventilated with a medium high tidal volume and lower PEEP. The beneficial effects of protective ventilation were seen despite greater reductions in cardiac index and oxygen delivery index. In the immediate postoperative phase low V_T ventilation with higher PEEP was associated with reduced ex vivo plasma capacity to produce TNF-α upon endotoxin stimulation and higher nitrite levels in urine. These findings might represent mechanistic explanations for the attenuation of systemic inflammation and inflammatory-induced organ dysfunction.

The effect of PPE-induced emphysema and chronic LPS-induced pulmonary inflammation on atherosclerosis development in APOE*3-LEIDEN mice

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is characterized by pulmonary inflammation, airways obstruction and emphysema, and is a risk factor for cardiovascular disease (CVD). However, the contribution of these individual COPD components to this increased risk is unknown. Therefore, the aim of this study was to determine the contribution of emphysema in the presence or absence of pulmonary inflammation to the increased risk of CVD, using a mouse model for atherosclerosis. Because smoke is a known risk factor for both COPD and CVD, emphysema was induced by intratracheal instillation of porcine pancreatic elastase (PPE).

METHODS

Hyperlipidemic APOE*3-Leiden mice were intratracheally instilled with vehicle, 15 or 30 µg PPE and after 4 weeks, mice received a Western-type diet (WTD). To study the effect of emphysema combined with pulmonary inflammation on atherosclerosis, mice received 30 µg PPE and during WTD feeding, mice were intranasally instilled with vehicle or low-dose lipopolysaccharide (LPS; 1 µg/mouse, twice weekly). After 20 weeks WTD, mice were sacrificed and emphysema, pulmonary inflammation and atherosclerosis were analysed.

RESULTS

Intratracheal PPE administration resulted in a dose-dependent increase in emphysema, whereas atherosclerotic lesion area was not affected by PPE treatment. Additional low-dose intranasal LPS administration induced a low-grade systemic IL-6 response, as compared to vehicle. Combining intratracheal PPE with intranasal LPS instillation significantly increased the number of pulmonary macrophages and neutrophils. Plasma lipids during the study were not different. LPS instillation caused a limited, but significant increase in the atherosclerotic lesion area. This increase was not further enhanced by PPE.

CONCLUSION

This study shows for the first time that PPE-induced emphysema both in the presence and absence of pulmonary inflammation does not affect atherosclerotic lesion development.

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.

Endotoxin tolerance variation over 24 h during porcine endotoxemia: association with changes in circulation and organ dysfunction

Endotoxin tolerance (ET), defined as reduced inflammatory responsiveness to endotoxin challenge following a first encounter with endotoxin, is an extensively studied phenomenon. Although reduced mortality and morbidity in the presence of ET has been demonstrated in animal studies, little is known about the temporal development of ET. Further, in acute respiratory distress syndrome ET correlates to the severity of the disease, suggesting a complicated relation between ET and organ dysfunction. Eighteen pigs were subjected to intensive care and a continuous endotoxin infusion for 24 h with the aim to study the time course of early ET and to relate ET to outcome in organ dysfunction. Three animals served as non-endotoxemic controls. Blood samples for cytokine analyses were taken and physiological variables registered every third hour. Production of TNF-α, IL-6, and IL-10 before and after endotoxin stimulation ex vivo was measured. The difference between cytokine values after and before ex vivo LPS stimulation (Δ-values) was calculated for all time points. ΔTNF-α was employed as the principal marker of ET and lower ΔTNF-α values were interpreted as higher levels of ET. During endotoxin infusion, there was suppression of ex vivo productions of TNF-α and IL-6 but not of IL-10 in comparison with that at 0 h. The ex vivo TNF-α values followed another time concentration curve than those in vivo. ΔTNF-α was at the lowest already at 6 h, followed by an increase during the ensuing hours. ΔTNF-α at 6 h correlated positively to blood pressure and systemic vascular resistance and negatively to cardiac index at 24 h. In this study a temporal variation of ET was demonstrated that did not follow changes in plasma TNF-α concentrations. Maximal ET occurred early in the course and the higher the ET, the more hyperdynamic the circulation 18 h later.

A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli

Background

Cells are subject to fluctuating and multiple stimuli in their natural environment. The signaling pathways often crosstalk to each other and give rise to complex nonlinear dynamics. Specifically repetitive exposure of a cell to a same stimulus sometime leads to augmented cellular responses. Examples are amplified proinflammatory responses of innate immune cells pretreated with a sub-threshold then a high dose of endotoxin or cytokine stimulation. This phenomenon, called priming effect in the literature, has important pathological and clinical significances.

Results

In a previous study, we enumerated possible mechanisms for priming using a three-node network model. The analysis uncovered three mechanisms. Based on the results, in this work we developed a straightforward procedure to identify molecular candidates contributing to the priming effect and the corresponding mechanisms. The procedure involves time course measurements, e.g., gene expression levels, or protein activities under low, high, and low + high dose of stimulant, then computational analysis of the dynamics patterns, and identification of functional roles in the context of the regulatory network. We applied the procedure to a set of published microarray data on interferon-γ-mediated priming effect of human macrophages. The analysis identified a number of network motifs possibly contributing to Interferon-γ priming. A further detailed mathematical model analysis further reveals how combination of different mechanisms leads to the priming effect.

Conclusions

One may perform systematic screening using the proposed procedure combining with high throughput measurements, at both transcriptome and proteome levels. It is applicable to various priming phenomena.

Molecular mechanisms responsible for the reduced expression of cholesterol transporters from macrophages by low-dose endotoxin

OBJECTIVE

Atherosclerosis is characterized as a chronic inflammatory condition that involves cholesterol deposition in arteries. Together with scavenger receptor B1 (SR-B1), the ATP-binding cassette transporters ABCA1 and ABCG1 are the major components of macrophage cholesterol efflux. Recent studies have shown that low-grade inflammation plays a distinct regulatory role in the expression of SR-B1 and ABCA1/ABCG1. However, the mechanisms linking low-grade inflammation and cholesterol accumulation are poorly understood.

METHODS AND RESULTS

Using primary bone-marrow-derived macrophages, we demonstrate that subclinical low-dose lipopolysaccharide potently reduces the expression of SR-B1 and ABCA1/ABCG1, as well as cholesterol efflux from macrophages through interleukin-1 receptor-associated kinase 1 and Toll-interacting-protein. Low-dose lipopolysaccharide downregulates the nuclear levels of retinoic acid receptor-α, leading to their reduced binding to the promoters of SR-B1 and ABCA1/ABCG1. We observe that glycogen synthase kinase 3β activation by low-dose lipopolysaccharide through interleukin-1 receptor-associated kinase 1 and Toll-interacting-protein is responsible for reduced levels of retinoic acid receptor-α, and reduced expression of SR-B1 and ABCA1/ABCG1. Interleukin-1 receptor-associated kinase M, however, counteracts the function of interleukin-1 receptor associated kinase 1.

CONCLUSIONS

Collectively, our data reveal a novel intracellular network regulated by low-dose endotoxemia that disrupts cholesterol efflux from macrophages and leads to the pathogenesis of atherosclerosis.