Differences in reactivation of tuberculosis induced from anti-TNF treatments are based on bioavailability in granulomatous tissue

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.

NF-κB-mediated TET2-dependent TNF promoter demethylation drives Mtb-upregulation TNF expression in macrophages

Tumor necrosis factor (TNF) is essential for the host defense against tuberculosis (TB). However, scarcity or excessive TNF production in macrophages can also increase susceptibility to TB. The precise mechanisms underlying how Mycobacterium tuberculosis (Mtb) induces TNF over-expression are unclear. Here, we show that Mtb infection significantly increases 5-hydroxylmethylocytosine (5hmC) levels in the TNF promoter. Luciferase reporter assays identify the precise methylated CpG sites that are essential to regulating TNF promoter activity. Infection simultaneously promotes the expression of the TET2 demethylase in macrophages. After inhibiting NF-κB or knocking down TET2, we found that TNF promoter demethylation levels is increased while Mtb-induced TNF expression decrease. Here, NF-κB binds to TET2 and mediates its recruitment to the TNF promoter to induce TNF demethylation. Finally, we show that TLR2 activation during Mtb infection promotes NF-κB translocation into the nucleus which is important for NF-κB-mediated TET2-dependent TNF promoter demethylation thus helps drive Mtb-induced TNF expression. Targeting this axis might be a novel strategy for host-directed therapy against TB.

Intranasal immunization with peptide-based immunogenic complex enhances BCG vaccine efficacy in a murine model of tuberculosis

Prime-boost immunization strategies are required to control the global tuberculosis (TB) pandemic, which claims approximately 3 lives every minute. Here, we have generated an immunogenic complex against Mycobacterium tuberculosis (M.tb), consisting of promiscuous T cell epitopes (M.tb peptides) and TLR ligands assembled in liposomes. Interestingly, this complex (peptide–TLR agonist–liposomes; PTL) induced significant activation of CD4⁺ T cells and IFN-γ production in the PBMCs derived from PPD⁺ healthy individuals as compared with PPD^– controls. Furthermore, intranasal delivery of PTL significantly reduced the bacterial burden in the infected mice by inducing M.tb-specific polyfunctional (IFN-γ⁺IL-17⁺TNF-α⁺IL-2⁺) immune responses and long-lasting central memory responses, thereby reducing the risk of TB recurrence in DOTS-treated infected animals. The transcriptome analysis of peptide-stimulated immune cells unveiled the molecular basis of enhanced protection. Furthermore, PTL immunization significantly boosted the Bacillus Calmette-Guerin–primed (BCG-primed) immune responses against TB. The greatly enhanced efficacy of the BCG-PTL vaccine model in controlling pulmonary TB projects PTL as an adjunct vaccine against TB.

TNFRp75-dependent immune regulation of alveolar macrophages and neutrophils during early Mycobacterium tuberculosis and Mycobacterium bovis BCG infection

TNF signalling through TNFRp55 and TNFRp75, and receptor shedding is important for immune activation and regulation. TNFRp75 deficiency leads to improved control of Mycobacterium tuberculosis (M. tuberculosis) infection, but the effects of early innate immune events in this process are unclear. We investigated the role of TNFRp75 on cell activation and apoptosis of alveolar macrophages and neutrophils during M. tuberculosis and M. bovis BCG infection. We found increased microbicidal activity against M. tuberculosis occurred independently of IFNy and NO generation, and displayed an inverse correlation with alveolar macrophages (AMs) apoptosis. Both M. tuberculosis and M. bovis BCG induced higher expression of MHC-II in TNFRp75^-/- AMs; however, M bovis BCG infection did not alter AM apoptosis in the absence of TNFRp75. Pulmonary concentrations of CCL2, CCL3 and IL-1β were increased in TNFRp75^-/- mice during M, bovis BCG infection, but had no effect on neutrophil responses. Thus, TNFRp75-dependent regulation of mycobacterial replication is virulence dependent and occurs independently of early alveolar macrophage apoptosis and neutrophil responses.

Neutralization of TNF-α and IL-1β Regulates CXCL8 Production through CXCL8/CXCR1 Axis in Macrophages during Staphylococcus aureus Infection

Anti-cytokine therapy is widely acknowledged as an anti-inflammatory technique to treat varied infectious diseases. TNF-α and IL-1β are major cytokines that regulate every aspect of the inflammatory process. However, the effects of single or dual cytokine neutralization on S. aureus mediated CXCL8 secretion and CXCR1 expression in murine peritoneal macrophages remained noninvestigated. Thus we aimed to explore the effects of kinetic-dose dependent neutralization of TNF-α and IL-1β using specific anti-cytokine antibodies and its influential impact on the CXCL8/CXCR1 axis at different stages of S. aureus (30, 60, and 90 min) infection. The murine peritoneal macrophages were isolated and infected with viable S. aureus followed by subsequent addition of anti-TNF-α and anti-IL-1β into the medium. The treated cells were centrifuged and lysate and supernatant collected for various experiments. The ROS generation was measured and cytokine production was estimated by ELISA. The expression of TNFR1, IL-1R, CXCR1, signaling molecules (NF-κB and JNK) were evaluated by Western blot. The role of single or dual cytokine neutralization on intracellular bacterial phagocytosis had also been analyzed by confocal microscopy. Dual cytokine neutralization significantly suppressed ROS, cytokines, CXCL8 secretion, and intracellular bacterial count compared to single cytokine neutralization and it was more apparent at 90 min post S. aureus infection. There was a drastic reduction in TNFR1, IL-1R, and CXCR1 expression on macrophage surface due to reduced expression of downstream signaling molecules, NF-κB and JNK. Hence dual cytokine neutralization was more effectual compared to single cytokine neutralization in the downregulation of S. aureus induced CXCR1 expression.

TNF-α antagonists differentially induce TGF-β1-dependent resuscitation of dormant-like Mycobacterium tuberculosis

TNF-α- as well as non-TNF-α-targeting biologics are prescribed to treat a variety of immune-mediated inflammatory disorders. The well-documented risk of tuberculosis progression associated with anti-TNF-α treatment highlighted the central role of TNF-α for the maintenance of protective immunity, although the rate of tuberculosis detected among patients varies with the nature of the drug. Using a human, in-vitro granuloma model, we reproduce the increased reactivation rate of tuberculosis following exposure to Adalimumab compared to Etanercept, two TNF-α-neutralizing biologics. We show that Adalimumab, because of its bivalence, specifically induces TGF-β1-dependent Mycobacterium tuberculosis (Mtb) resuscitation which can be prevented by concomitant TGF-β1 neutralization. Moreover, our data suggest an additional role of lymphotoxin-α–neutralized by Etanercept but not Adalimumab–in the control of latent tuberculosis infection. Furthermore, we show that, while Secukinumab, an anti-IL-17A antibody, does not revert Mtb dormancy, the anti-IL-12-p40 antibody Ustekinumab and the recombinant IL-1RA Anakinra promote Mtb resuscitation, in line with the importance of these pathways in tuberculosis immunity.

Mycobacterium tuberculosis (Mtb) is the world’s leading infectious killer. Multi-cellular immune structures called granulomas may constitute a latent form of Mtb infection and a potential reservoir for future cases. Post-marketing surveillance data suggested that Mtb protective immunity is unequally impacted by different TNF-α-targeting drugs used to treat inflammatory disorders. We used an in-vitro granuloma model to reproduce these clinical observations and gain mechanistic insights and, in addition, to assess the risk of tuberculosis reactivation associated with the use of other immunomodulatory drugs. These results may inspire pharmacologists to design future drug-development strategies of biologics in particular, while immunologists and microbiologists will find a relevant experimental approach to disentangle the complex interactions involved in Mtb protective immunity and immunopathogenesis.

Agent-based models of inflammation in translational systems biology: A decade later

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

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

Low Levels of T Cell Exhaustion in Tuberculous Lung Granulomas

The hallmarks of pulmonary Mycobacterium tuberculosis infection are lung granulomas. These organized structures are composed of host immune cells whose purpose is to contain or clear infection, creating a complex hub of immune and bacterial cell activity, as well as limiting pathology in the lungs. Yet, given cellular activity and the potential for frequent interactions between host immune cells and M. tuberculosis-infected cells, we observed a surprisingly low quantity of cytokine-producing T cells (<10% of granuloma T cells) in our recent study of M. tuberculosis infection within nonhuman primate (NHP) granulomas. Various mechanisms could limit T cell function, and one hypothesis is T cell exhaustion. T cell exhaustion is proposed to result from continual antigen stimulation, inducing them to enter a state characterized by low cytokine production, low proliferation, and expression of a series of inhibitory receptors, the most common being PD-1, LAG-3, and CTLA-4. In this work, we characterized the expression of inhibitory receptors on T cells and the functionality of these cells in tuberculosis (TB) lung granulomas. We then used these experimental data to calibrate and inform an agent-based computational model that captures environmental, cellular, and bacterial dynamics within granulomas in lungs during M. tuberculosis infection. Together, the results of the modeling and the experimental work suggest that T cell exhaustion alone is not responsible for the low quantity of M. tuberculosis-responsive T cells observed within TB granulomas and that the lack of exhaustion is likely an intrinsic property of granuloma structure.

Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model

Mycobacterium tuberculosis is the pathogenic bacterium that causes tuberculosis (TB), one of the most lethal infectious diseases in the world. The only vaccine against TB is minimally protective, and multi-drug resistant TB necessitates new therapeutics to treat infection. Developing new therapies requires a better understanding of the complex host immune response to infection, including dissecting the processes leading to formation of granulomas, the dense cellular lesions associated with TB. In this work, we pair experimental and computational modeling studies to explore cytokine regulation in the context of TB. We use our next-generation hybrid multi-scale model of granuloma formation (GranSim) to capture molecular, cellular, and tissue scale dynamics of granuloma formation. We identify TGF-β1 as a major inhibitor of cytotoxic T-cell effector function in granulomas. Deletion of TGF-β1 from the system results in improved bacterial clearance and lesion sterilization. We also identify a novel dichotomous regulation of cytotoxic T cells and macrophages by TGF-β1 and IL-10, respectively. These findings suggest that increasing cytotoxic T-cell effector functions may increase bacterial clearance in granulomas and highlight potential new therapeutic targets for treating TB.

Mathematical Modeling of Tuberculosis Granuloma Activation

Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood. Animal and human studies currently cannot probe the dynamics of activation, so a computational model is developed to fill this gap. This dynamic mathematical model focuses specifically on the latent to active transition after the initial immune response has successfully formed a granuloma. Bacterial leakage from latent granulomas is successfully simulated in response to the MMP-1 dynamics under several scenarios for granuloma activation.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.

Myeloid and T Cell-Derived TNF Protects against Central Nervous System Tuberculosis

Tuberculosis of the central nervous system (CNS-TB) is a devastating complication of tuberculosis, and tumor necrosis factor (TNF) is crucial for innate immunity and controlling the infection. TNF is produced by many cell types upon activation, in particularly the myeloid and T cells during neuroinflammation. Here we used mice with TNF ablation targeted to myeloid and T cell (MT-TNF^-/-) to assess the contribution of myeloid and T cell-derived TNF in immune responses during CNS-TB. These mice exhibited impaired innate immunity and high susceptibility to cerebral Mycobacterium tuberculosis infection, a similar phenotype to complete TNF-deficient mice. Further, MT-TNF^-/- mice were not able to control T cell responses and cytokine/chemokine production. Thus, our data suggested that collective TNF production by both myeloid and T cells are required to provide overall protective immunity against CNS-TB infection.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection

Tuberculosis (TB) is a world-wide health problem with approximately 2 billion people infected with Mycobacterium tuberculosis (Mtb, the causative bacterium of TB). The pathologic hallmark of Mtb infection in humans and Non-Human Primates (NHPs) is the formation of spherical structures, primarily in lungs, called granulomas. Infection occurs after inhalation of bacteria into lungs, where resident antigen-presenting cells (APCs), take up bacteria and initiate the immune response to Mtb infection. APCs traffic from the site of infection (lung) to lung-draining lymph nodes (LNs) where they prime T cells to recognize Mtb. These T cells, circulating back through blood, migrate back to lungs to perform their immune effector functions. We have previously developed a hybrid agent-based model (ABM, labeled GranSim) describing in silico immune cell, bacterial (Mtb) and molecular behaviors during tuberculosis infection and recently linked that model to operate across three physiological compartments: lung (infection site where granulomas form), lung draining lymph node (LN, site of generation of adaptive immunity) and blood (a measurable compartment). Granuloma formation and function is captured by a spatio-temporal model (i.e., ABM), while LN and blood compartments represent temporal dynamics of the whole body in response to infection and are captured with ordinary differential equations (ODEs). In order to have a more mechanistic representation of APC trafficking from the lung to the lymph node, and to better capture antigen presentation in a draining LN, this current study incorporates the role of dendritic cells (DCs) in a computational fashion into GranSim.

Tuberculosis and other opportunistic infections in tofacitinib-treated patients with rheumatoid arthritis

OBJECTIVES

To evaluate the risk of opportunistic infections (OIs) in patients with rheumatoid arthritis (RA) treated with tofacitinib.

METHODS

Phase II, III and long-term extension clinical trial data (April 2013 data-cut) from the tofacitinib RA programme were reviewed. OIs defined a priori included mycobacterial and fungal infections, multidermatomal herpes zoster and other viral infections associated with immunosuppression. For OIs, we calculated crude incidence rates (IRs; per 100 patient-years (95% CI)); for tuberculosis (TB) specifically, we calculated rates stratified by patient enrolment region according to background TB IR (per 100 patient-years): low (≤0.01), medium (>0.01 to ≤0.05) and high (>0.05).

RESULTS

We identified 60 OIs among 5671 subjects; all occurred among tofacitinib-treated patients. TB (crude IR 0.21, 95% CI of (0.14 to 0.30)) was the most common OI (n=26); median time between drug start and diagnosis was 64 weeks (range 15-161 weeks). Twenty-one cases (81%) occurred in countries with high background TB IR, and the rate varied with regional background TB IR: low 0.02 (0.003 to 0.15), medium 0.08 (0.03 to 0.21) and high 0.75 (0.49 to 1.15). In Phase III studies, 263 patients diagnosed with latent TB infection were treated with isoniazid and tofacitinib concurrently; none developed TB. For OIs other than TB, 34 events were reported (crude IR 0.25 (95% CI 0.18 to 0.36)).

CONCLUSIONS

Within the global tofacitinib RA development programme, TB was the most common OI reported but was rare in regions of low and medium TB incidence. Patients who screen positive for latent TB can be treated with isoniazid during tofacitinib therapy.

Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach

Mycobacterium tuberculosis associated granuloma formation can be viewed as a structural immune response that can contain and halt the spread of the pathogen. In several mammalian hosts, including non-human primates, Mtb granulomas are often hypoxic, although this has not been observed in wild type murine infection models. While a presumed consequence, the structural contribution of the granuloma to oxygen limitation and the concomitant impact on Mtb metabolic viability and persistence remains to be fully explored. We develop a multiscale computational model to test to what extent in vivo Mtb granulomas become hypoxic, and investigate the effects of hypoxia on host immune response efficacy and mycobacterial persistence. Our study integrates a physiological model of oxygen dynamics in the extracellular space of alveolar tissue, an agent-based model of cellular immune response, and a systems biology-based model of Mtb metabolic dynamics. Our theoretical studies suggest that the dynamics of granuloma organization mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome. Furthermore, our integrated model demonstrates the link between structural immune response and mechanistic drivers influencing Mtbs adaptation to its changing microenvironment and the qualitative infection outcome scenarios of clearance, containment, dissemination, and a newly observed theoretical outcome of transient containment. We observed hypoxic regions in the containment granuloma similar in size to granulomas found in mammalian in vivo models of Mtb infection. In the case of the containment outcome, our model uniquely demonstrates that immune response mediated hypoxic conditions help foster the shift down of bacteria through two stages of adaptation similar to thein vitro non-replicating persistence (NRP) observed in the Wayne model of Mtb dormancy. The adaptation in part contributes to the ability of Mtb to remain dormant for years after initial infection.

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.

A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices. The values of the inflammation indices may reflect the differences between normal and pathological inflammation, and may be used to gauge the effects of therapeutic interventions aimed to control inflammation. Yet, the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown. Here, we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices. We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios. We then subjected the corresponding inflammation index values to sensitivity and correlation analysis. We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates, as well as by the degradation rates of three specific molecular mediators. These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators, which we confirmed by computational experiments. Taken together, our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response.

Adventures within the speckled band: heterogeneity, angiogenesis, and balanced inflammation in the tuberculous granuloma

Recent work in a variety of animal models, including mice, zebrafish, and macaques, as well as in humans, has led to a reassessment of several tenets of mycobacterial infection. In this review, we describe new findings about the composition and dynamics of the tuberculous granuloma, the central host structure in mycobacterial infection, as well as inflammatory mediators that drive a successful anti-microbial response on one hand and pathological inflammation on the other. We highlight granuloma heterogeneity that emerges in the context of infection, the functional consequences of angiogenesis in tuberculous granulomas, and data that balanced inflammation in humans, with a central role for tumor necrosis factor, appears to play a key role in optimal defense against mycobacterial infection. These findings have suggested new and specific host-directed therapies that await further clinical exploration.

Control of Mycobacterial Infections in Mice Expressing Human Tumor Necrosis Factor (TNF) but Not Mouse TNF

Tumor necrosis factor (TNF) is an important cytokine for host defense against pathogens but is also associated with the development of human immunopathologies. TNF blockade effectively ameliorates many chronic inflammatory conditions but compromises host immunity to tuberculosis. The search for novel, more specific human TNF blockers requires the development of a reliable animal model. We used a novel mouse model with complete replacement of the mouse TNF gene by its human ortholog (human TNF [huTNF] knock-in [KI] mice) to determine resistance to Mycobacterium bovis BCG and M. tuberculosis infections and to investigate whether TNF inhibitors in clinical use reduce host immunity. Our results show that macrophages from huTNF KI mice responded to BCG and lipopolysaccharide similarly to wild-type macrophages by NF-κB activation and cytokine production. While TNF-deficient mice rapidly succumbed to mycobacterial infection, huTNF KI mice survived, controlling the bacterial burden and activating bactericidal mechanisms. Administration of TNF-neutralizing biologics disrupted the control of mycobacterial infection in huTNF KI mice, leading to an increased bacterial burden and hyperinflammation. Thus, our findings demonstrate that human TNF can functionally replace murine TNF in vivo, providing mycobacterial resistance that could be compromised by TNF neutralization. This new animal model will be helpful for the testing of specific biologics neutralizing human TNF.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), induces formation of granulomas, structures in which immune cells and bacteria colocalize. Macrophages are among the most abundant cell types in granulomas and have been shown to serve as both critical bactericidal cells and targets for M. tuberculosis infection and proliferation throughout the course of infection. Very little is known about how these processes are regulated, what controls macrophage microenvironment-specific polarization and plasticity, or why some granulomas control bacteria and others permit bacterial dissemination. We take a computational-biology approach to investigate mechanisms that drive macrophage polarization, function, and bacterial control in granulomas. We define a "macrophage polarization ratio" as a metric to understand how cytokine signaling translates into polarization of single macrophages in a granuloma, which in turn modulates cellular functions, including antimicrobial activity and cytokine production. Ultimately, we extend this macrophage ratio to the tissue scale and define a "granuloma polarization ratio" describing mean polarization measures for entire granulomas. Here we coupled experimental data from nonhuman primate TB granulomas to our computational model, and we predict two novel and testable hypotheses regarding macrophage profiles in TB outcomes. First, the temporal dynamics of granuloma polarization ratios are predictive of granuloma outcome. Second, stable necrotic granulomas with low CFU counts and limited inflammation are characterized by short NF-κB signal activation intervals. These results suggest that the dynamics of NF-κB signaling is a viable therapeutic target to promote M1 polarization early during infection and to improve outcome.

Three-dimensional in vitro models of granuloma to study bacteria-host interactions, drug-susceptibility, and resuscitation of dormant mycobacteria

Mycobacterium tuberculosis, Mycobacterium leprae, Mycobacterium bovis, and Mycobacterium avium subsp. paratuberculosis can survive within host macrophages in a dormant state, encased within an organized aggregate of immune host cells called granuloma. Granulomas consist of uninfected macrophages, foamy macrophages, epithelioid cells, and T lymphocytes accumulated around infected macrophages. Within granulomas, activated macrophages can fuse to form multinucleated giant cells, also called giant Langhans cells. A rim of T lymphocytes surrounds the core, and a tight coat of fibroblast closes the structure. Several in vivo models have been used to study granuloma's structure and function, but recently developed in vitro models of granuloma show potential for closer observation of the early stages of host's responses to live mycobacteria. This paper reviews culture conditions that resulted in three-dimensional granulomas, formed by the adhesion of cell populations in peripheral blood mononuclear cells infected with mycobacteria. The similarities of these models to granulomas encountered in clinical specimens include cellular composition, granulomas' cytokine production, and cell surface antigens. A reliable in vitro dormancy model may serve as a useful platform to test whether drug candidates can kill dormant mycobacteria. Novel drugs that target dormancy-specific pathways may shorten the current long, difficult treatments necessary to cure mycobacterial diseases.

In silico models of M. tuberculosis infection provide a route to new therapies

Tuberculosis (TB) is a global health problem responsible for ~2 million deaths per year. Current antibiotic treatments are lengthy and fraught with compliance and resistance issues. There is a crucial need for additional approaches to provide a cost-effective means of exploring the design space for potential therapies. We discuss the use of mathematical and computational models in virtual experiments and virtual clinical trials both to develop new hypotheses regarding the disease and to provide a cost-effective means of discovering new treatment strategies.

Monocyte-derived IL-5 reduces TNF production by Mycobacterium tuberculosis-specific CD4 T cells during SIV/M. tuberculosis coinfection

HIV-infected individuals are significantly more susceptible to tuberculosis (TB) than uninfected individuals. Although it is established that HIV reduces Mycobacterium tuberculosis-specific T cell responses, the causes of this dysfunction are not known. We used the cynomolgus macaque model of TB to demonstrate that ex vivo SIV reduces the frequency of M. tuberculosis-specific TNF and IFN-γ-producing T cells within 24 h after infection. In vivo, T cell IFN-γ responses in granulomas from animals with SIV/M. tuberculosis coinfection were lower than SIV-negative animals with active TB. The SIV effects on the inhibition of T cell responses were primarily on APCs and not the T cells directly. Specifically, reductions in the frequency of TNF-producing M. tuberculosis-specific CD4 T cells were caused, at least in part, by SIV-induced production of monocyte derived IL-5.

Application of a stochastic modeling to assess the evolution of tuberculous and non-tuberculous mycobacterial infection in patients treated with tumor necrosis factor inhibitors

In this manuscript we apply stochastic modeling to investigate the risk of reactivation of latent mycobacterial infections in patients undergoing treatment with tumor necrosis factor inhibitors. First, we review the perspective proposed by one of the authors in a previous work and which consists in predicting the occurrence of reactivation of latent tuberculosis infection or newly acquired tuberculosis during treatment; this is based on variational procedures on a simple set of parameters (e.g. rate of reactivation of a latent infection). Then, we develop a full analytical study of this approach through a Markov chain analysis and we find an exact solution for the temporal evolution of the number of cases of tuberculosis infection (re)activation. The analytical solution is compared with Monte Carlo simulations and with experimental data, showing overall excellent agreement. The generality of this theoretical framework allows to investigate also the case of non-tuberculous mycobacteria infections; in particular, we show that reactivation in that context plays a minor role. This may suggest that, while the screening for tuberculous is necessary prior to initiating biologics, when considering non-tuberculous mycobacteria only a watchful monitoring during the treatment is recommended. The framework outlined in this paper is quite general and could be extremely promising in further researches on drug-related adverse events.

Human granuloma in vitro model, for TB dormancy and resuscitation

Tuberculosis (TB) is responsible for death of nearly two million people in the world annually. Upon infection, Mycobacterium tuberculosis (Mtb) causes formation of granuloma where the pathogen goes into dormant state and can live for decades before resuscitation to develop active disease when the immune system of the host is weakened and/or suppressed. In an attempt to better understand host-pathogen interactions, several groups have been developing in vitro models of human tuberculosis granuloma. However, to date, an in vitro granuloma model in which Mtb goes into dormancy and can subsequently resuscitate under conditions that mimic weakening of the immune system has not been reported. We describe the development of a biomimetic in vitro model of human tuberculosis granuloma using human primary leukocytes, in which the Mtb exhibited characteristics of dormant mycobacteria as demonstrated by (1) loss of acid-fastness, (2) accumulation of lipid bodies (3) development of rifampicin-tolerance and (4) gene expression changes. Further, when these micro granulomas were treated with immunosuppressant anti-tumor necrosis factor-alpha monoclonal antibodies (anti-TNFα mAbs), resuscitation of Mtb was observed as has been found in humans. In this human in vitro granuloma model triacylglycerol synthase 1deletion mutant (Δtgs1) with impaired ability to accumulate triacylglycerides (TG), but not the complemented mutant, could not go into dormancy. Deletion mutant of lipY, with compromised ability to mobilize the stored TG, but not the complemented mutant, was unable to come out of dormancy upon treatment with anti-TNFα mAbs. In conclusion, we have developed an in vitro human tuberculosis granuloma model that largely exhibits functional features of dormancy and resuscitation observed in human tuberculosis.

Modelling HIV and MTB co-infection including combined treatment strategies

A new host-pathogen model is described that simulates HIV-MTB co-infection and treatment, with the objective of testing treatment strategies. The model includes CD4+ and CD8+ T cells, resting and activated macrophages, HIV and Mycobacterium tuberculosis (MTB). For TB presentation at various stages of HIV disease in a co-infected individual, combined treatment strategies were tested with different relative timings of treatment for each infection. The stages were early HIV disease, late HIV disease and AIDS. The main strategies were TB treatment followed by anti-retroviral therapy (ART) after delays of 15 days, 2 months and 6 months. ART followed by TB treatment was an additional strategy that was tested. Treatment was simulated with and without drug interaction. Simulation results were that TB treatment first followed by ART after a stage-dependent delay has the best outcome. During early HIV disease a 6 month delay is acceptable. During late HIV disease, a 2 month delay is best. During AIDS it is better to start ART after 15 days. However, drug interaction works against the benefits of early ART. These results agree with expert reviews and clinical trials.

NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis

The NF-κB signaling pathway is central to the body’s response to many pathogens. Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this pathway have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung. NF-κB signaling is triggered via tumor necrosis factor-α (TNF) binding to receptors on macrophages; TNF has been shown to play a key role in infection dynamics in humans and multiple animal systems. Using our multi-scale model, we predict the impact of TNF-induced NF-κB-mediated responses on the outcome of infection at the level of a granuloma, an aggregate of immune cells and bacteria that forms in response to infection and is key to containment of infection and clinical latency. We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size. Because we incorporate intracellular signaling pathways explicitly, our analysis also elucidates NF-κB-associated signaling molecules and processes that may be new targets for infection control.

Comparative safety of infliximab and etanercept on the risk of serious infections: does the association vary by patient characteristics?

PURPOSE

Infliximab, a chimeric monoclonal anti-TNFα antibody, has been found to increase the risk of serious infections compared with the TNF receptor fusion protein etanercept in some studies. It is unclear whether the risk varies by patient characteristics. We conducted a study to address this question.

METHODS

We identified members of Kaiser Permanente Northern California who initiated infliximab (n = 793) or etanercept (n = 2692) in 1997-2007. Using a Cox model, we estimated the propensity-score-adjusted hazard ratio (HR) and 95% confidence interval (CI) of serious infections requiring hospitalization or opportunistic infections comparing infliximab initiators to etanercept initiators. We tested whether the adjusted HR differed by age, sex, race/ethnicity, body mass index, and smoking status.

RESULTS

The crude incidence rate of serious infections per 100 person-years was 5.4 (95%CI: 3.8, 7.5) in patients <65 years and 16.0 (95%CI: 10.4, 23.4) in patients ≥ 65 years during the first 3 months following treatment initiation. Compared with etanercept, the adjusted HR during this period was elevated for infliximab in patients <65 years (HR: 3.01; 95%CI: 1.49, 6.07), but not in those ≥ 65 years (HR 0.94; 95%CI: 0.41, 2.13). Findings did not suggest that the HR varied by the other patient characteristics examined.

CONCLUSIONS

An increased risk of serious infections associated with infliximab relative to etanercept did not appear to be modified by patients' sex, race/ethnicity, body mass index, or smoking status. There was an indication that the increased risk might be limited to patients <65 years. Additional studies are warranted to verify or refute this finding.

Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability

Increased rates of tuberculosis (TB) reactivation have been reported in humans treated with TNF-α (TNF)-neutralizing drugs, and higher rates are observed with anti-TNF Abs (e.g., infliximab) as compared with TNF receptor fusion protein (etanercept). Mechanisms driving differential reactivation rates and differences in drug action are not known. We use a computational model of a TB granuloma formation that includes TNF/TNF receptor dynamics to elucidate these mechanisms. Our analyses yield three important insights. First, drug binding to membrane-bound TNF critically impairs granuloma function. Second, a higher risk of reactivation induced from Ab-type treatments is primarily due to differences in TNF/drug binding kinetics and permeability. Apoptotic and cytolytic activities of Abs and pharmacokinetic fluctuations in blood concentration of drug are not essential to inducing TB reactivation. Third, we predict specific host factors that, if augmented, would improve granuloma function during anti-TNF therapy. Our findings have implications for the development of safer anti-TNF drugs to treat inflammatory diseases.

Detection of immune cell response to M. tuberculosis-specific antigens by quantitative polymerase chain reaction

One third of the world's population is latently infected with Mycobacterium tuberculosis (Mtb) and up to 10% of infected individuals develop active tuberculosis (TB) in their lifetime. Among the major challenges in the control of TB is the implementation of sensitive methods for detection of latent tuberculosis infection (LTBI). Currently, in vitro interferon gamma release assays, yielding single value readout, are used as an alternative to the traditional tuberculin skin test for the diagnosis of LTBI. More complex characterization of immune status of LTBI individuals, however, is desirable for indication of LTBI subjects for preventative chemotherapy. Here we describe a quantitative polymerase chain reaction (qPCR) for determination of expression levels of 14 genes, additional to interferon gamma, which was applied for comparison of the specific Mtb-antigen immune response of blood cells from healthy, latently infected, and TB individuals. With the use of principal component analysis and discriminant analysis, a pattern of mRNA levels of 6 genes was identified, allowing discrimination of healthy individuals from active TB and LTBI subjects. These results open the way to development of multimarker qPCR for the detection of LTBI.

A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis

Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host-mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can be regulated by key immune cell migration rates. Our hybrid multi-organ implementation suggests that effector CD4+ T cells can rescue the system from a persistent infection and lead to clearance once a granuloma is fully formed. This could be effective as an immunotherapy strategy for latently infected individuals.

Phosphodiesterase 4 inhibition reduces innate immunity and improves isoniazid clearance of Mycobacterium tuberculosis in the lungs of infected mice

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is one of the leading infectious disease causes of morbidity and mortality worldwide. Though current antibiotic regimens can cure the disease, treatment requires at least six months of drug therapy. One reason for the long duration of therapy is that the currently available TB drugs were selected for their ability to kill replicating organisms and are less effective against subpopulations of non-replicating persistent bacilli. Evidence from in vitro models of Mtb growth and mouse infection studies suggests that host immunity may provide some of the environmental cues that drive Mtb towards non-replicating persistence. We hypothesized that selective modulation of the host immune response to modify the environmental pressure on the bacilli may result in better bacterial clearance during TB treatment. For this proof of principal study, we compared bacillary clearance from the lungs of Mtb-infected mice treated with the anti-TB drug isoniazid (INH) in the presence and absence of an immunomodulatory phosphodiesterase 4 inhibitor (PDE4i), CC-3052. The effects of CC-3052 on host global gene expression, induction of cytokines, and T cell activation in the lungs of infected mice were evaluated. We show that CC-3052 modulates the innate immune response without causing generalized immune suppression. Immune modulation combined with INH treatment improved bacillary clearance and resulted in smaller granulomas and less lung pathology, compared to treatment with INH alone. This novel strategy of combining anti-TB drugs with an immune modulating molecule, if applied appropriately to patients, may shorten the duration of TB treatment and improve clinical outcome.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.

Choice of Biologic Therapy for Patients with Rheumatoid Arthritis: The Infection Perspective

Biologicals revolutionized the treatment of Rheumatoid Arthritis (RA). The targeted suppression of key inflammatory pathways involved in joint inflammation and destruction allows better disease control, which, however, comes at the price of an elevated infection risk due to relative immunosuppression. The disease-related infection risk and the infection risk associated with the use of TNF-α inhibitors (infliximab, adalimumab, etanercept, golimumab and certolizumab pegol), rituximab, abatacept and tocilizumab are discussed. Risk factors clinicians need to take into account when selecting the most appropriate biologic therapy for RA patients, as well as precautions and screening concerning a number of specific infections, such as tuberculosis, intracellular bacterial infections, reactivation of chronic viral infections and HIV are reviewed.

A multifaceted approach to modeling the immune response in tuberculosis

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self-organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479-489 DOI: 10.1002/wsbm.131

TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: a next-generation two-compartmental model

Tuberculosis (TB) is one of the earliest recorded human diseases and still one of the deadliest worldwide. Its causative agent is the bacteria Mycobacterium tuberculosis (Mtb). Cytokine-mediated macrophage activation is a necessary step in control of bacterial growth, and early immunologic events in lymph node and lung are crucial to the outcome of infection, although the factors that influence these environments and the immune response are poorly understood. Our goal is to build the next-generation two-compartmental model of the immune response to provide a gateway to more spatial and mechanistic investigations of M. tuberculosis infection in the LN and lung. Crucial immune factors emerge that affect macrophage populations and inflammation, namely TNF-dependent recruitment and apoptosis, and IL-10 levels. Surprisingly, bacterial load plays a less important role than TNF in increasing the population of infected macrophages and inflammation. Using a mathematical model, it is possible to distinguish the effects of pro-inflammatory (TNF) and anti-inflammatory (IL-10) cytokines on the spectrum of phagocyte populations (macrophages and dendritic cells) in the lung and lymph node. Our results suggest that TNF is a major mediator of recruitment of phagocytes to the lungs. In contrast, IL-10 plays a role in balancing the dominant macrophage phenotype in LN and lung.

Tuberculosis: global approaches to a global disease

Mycobacterium tuberculosis is a remarkably successful human pathogen. The interaction with the human host is complex and much remains unknown. Recent advances in systems biology have allowed the integration of data from humans and animal models into computational approaches. For example, mathematical models provide a platform for in silico manipulation of host-pathogen interactions to gain insight into this infection across temporal and biologic scales. Here, we review recent studies on global approaches toward identifying comprehensive responses of both host and bacillus during infection, and the potential for incorporation of these data into many types of useful computational systems. Systems biology approaches provide a unique opportunity to study interventions that may improve therapy and vaccines against this major killer.

[TNFα blocking agents and sarcoidosis: an update]

Increased production of TNFα by alveolar macrophages and involvement of TNFα in granuloma formation suggest that this cytokine is involved in the pathophysiology of sarcoidosis. The three available TNFα blocking agents have been tested in sarcoidosis refractory to corticosteroids or immunosuppressive drugs. Data are available from isolated case reports or limited series of patients treated in open label trials with favourable issue with anti-TNFα monoclonal antibodies. Two randomized placebo controlled studies evaluated the efficacy of infliximab in pulmonary and extra-pulmonary sarcoidosis, showing that infliximab improves significantly extra-pulmonary disease. There is no significant difference between infliximab and placebo in the treatment of pulmonary manifestations. Etanercept showed no efficacy for treating ocular sarcoidosis in a controlled trial and for pulmonary disease in an open label trial. Paradoxical cases of proven sarcoidosis have been reported in patients receiving anti-TNFα agents for chronic inflammatory rheumatic diseases. A literature review identified 28 cases, including 16 with etanercept, eight with infliximab and four with adalimumab. Although these cases were mainly reported with etanercept, paradoxical sarcoidosis has been reported with the three available anti-TNFα agents, suggesting a class effect. Changes in the cytokine balance may be involved in these cases of induced sarcoidosis, which must be known by the clinician.

Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma

Tuberculosis (TB) granulomas are organized collections of immune cells comprised of macrophages, lymphocytes and other cells that form in the lung as a result of immune response to Mycobacterium tuberculosis (Mtb) infection. Formation and maintenance of granulomas are essential for control of Mtb infection and are regulated in part by a pro-inflammatory cytokine, tumor necrosis factor-α (TNF). To characterize mechanisms that control TNF availability within a TB granuloma, we developed a multi-scale two compartment partial differential equation model that describes a granuloma as a collection of immune cells forming concentric layers and includes TNF/TNF receptor binding and trafficking processes. We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using our model, we then demonstrated that the organization of immune cells within a TB granuloma as well as TNF/TNF receptor binding and intracellular trafficking are two important factors that control TNF availability and may spatially coordinate TNF-induced immunological functions within a granuloma. Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry. To further elucidate the role of TNF in the process of granuloma development, our modeling and experimental findings on TNF-associated molecular scale aspects of the granuloma can be incorporated into larger scale models describing the immune response to TB infection. Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

Tuberculosis is a common and deadly infectious disease caused by a highly successful bacterium, Mycobacterium tuberculosis (Mtb). Multiple host immune factors control the formation of a self-organizing aggregate of immune cells termed a granuloma in the lungs after inhalation of Mtb. One such factor, tumor necrosis factor-α (TNF), is a protein that regulates inflammatory immune responses. Availability of TNF within a TB granuloma has been proposed to have a critical role in the protective immunity against TB. However, direct measurement of the level of TNF in a granuloma is not experimentally feasible. Therefore, we develop a mathematical model based on an experimental model of granuloma developed in mice to predict TNF availability in a granuloma. We measure values of critical model parameters and explore mechanisms that influence TNF availability in the granuloma. We find that cellular organization in a granuloma and intracellular trafficking of TNF control TNF availability in a granuloma. Further, our model analysis also highlights anti-TNF drug properties that determine their TNF neutralization power. Our findings complement and extend those of recent studies on the role of TNF in the immune response against TB.

A variant of TNFR2-Fc fusion protein exhibits improved efficacy in treating experimental rheumatoid arthritis

Etanercept, a TNF receptor 2-Fc fusion protein, is currently being used for the treatment of rheumatoid arthritis (RA). However, 25% to 38% of patients show no response which is suspected to be partially due to insufficient affinity of this protein to TNFα. By using computational protein design, we found that residue W89 and E92 of TNFR2 were critical for ligand binding. Among several mutants tested, W89Y/E92N displayed 1.49-fold higher neutralizing activity to TNFα, as compared to that of Etanercept. Surface plasmon resonance (SPR) based binding assay revealed that the equilibrium dissociation constant of W89Y/E92N to TNFα was 3.65-fold higher than that of Etanercept. In a rat model of collagen-induced arthritis (CIA), W89Y/E92N showed a significantly better ability than Etanercept in reducing paw swelling and improvement of arthritic joint histopathologically. These data demonstrate that W89Y/E92N is potentially a better candidate with improved efficacy in treating RA and other autoimmune diseases.

Rheumatoid arthritis (RA) is a frequently occurring, chronic, debilitating disease. TNFα plays a pivotal role in regulating its inflammatory response. TNFα inhibition with TNF receptor 2-Fc fusion protein (TNFR2-Fc) was effective in the treatment of RA. However, quite a few patients may not achieve good clinical outcomes after TNFR2-Fc therapy, and the high dosage of TNFR2-Fc in clinical treatment usually causes some side effects, such as injection site reactions. Enhancing the affinity of TNFR2-Fc to TNFα would be of benefit to its therapeutic effect on RA, and may reduce the clinical dosage. We modeled the interactions of TNFα and TNFR2, and figured out the sites that may be critical for ligand-receptor binding. A high affinity TNFR2-Fc variant (E92N/W89Y) was obtained by rational mutagenesis at residue 89 and 92. It shows significant improvements compared with wild type TNFR2-Fc in suppressing rat arthritis induced by collagen. This variant is more potent in neutralizing TNFα, and thereafter may offer a higher degree of RA symptom relief, and be in a much lower dosage.

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.

The spectrum of latent tuberculosis: rethinking the biology and intervention strategies

Immunological tests provide evidence of latent tuberculosis in one third of the global population, which corresponds to more than two billion individuals. Latent tuberculosis is defined by the absence of clinical symptoms but carries a risk of subsequent progression to clinical disease, particularly in the context of co-infection with HIV. In this Review we discuss the biology of latent tuberculosis as part of a broad range of responses that occur following infection with Mycobacterium tuberculosis, which result in the formation of physiologically distinct granulomatous lesions that provide microenvironments with differential ability to support or suppress the persistence of viable bacteria. We then show how this model can be used to develop a rational programme to discover effective drugs for the eradication of M. tuberculosis infection.

Therapeutic targets in rheumatoid arthritis: the interleukin-6 receptor

RA is a chronic, debilitating disease in which articular inflammation and joint destruction are accompanied by systemic manifestations including anaemia, fatigue and osteoporosis. IL-6 is expressed abundantly in the SF of RA patients and is thought to mediate many of the local and systemic effects of this disease. Unlike a number of other cytokines, IL-6 can activate cells through both membrane-bound (IL-6R) and soluble receptors (sIL-6R), thus widening the number of cell types responsive to this cytokine. Indeed, trans-signalling, where IL-6 binds to the sIL-6R, homodimerizes with glycoprotein 130 subunits and induces signal transduction, has been found to play a key role in acute and chronic inflammation. Elevated levels of IL-6 and sIL-6R in the SF of RA patients can increase the risk of joint destruction and, at the joint level, IL-6/sIL-6R can stimulate pannus development through increased VEGF expression and increase bone resorption as a result of osteoclastogenesis. Systemic effects of IL-6, albeit through conventional or trans-signalling, include regulation of acute-phase protein synthesis, as well as hepcidin production and stimulation of the hypothalamo-pituitary-adrenal axis, the latter two actions potentially leading to anaemia and fatigue, respectively. This review aims to provide an insight into the biological effects of IL-6 in RA, examining how IL-6 can induce the articular and systemic effects of this disease.