The timing of TNF and IFN-gamma signaling affects macrophage activation strategies during Mycobacterium tuberculosis infection

Decreased IL-1 β Secretion as a Potential Predictor of Tuberculosis Recurrence in Individuals Diagnosed with HIV

Background: The mechanisms of the formation of immunological competence against tuberculosis (TB), and especially those associated with HIV co-infection, remain poorly understood. However, there is an urgent need for risk recurrence predictive biomarkers, as well as for predictors of successful treatment outcomes. The goal of the study was to identify possible immunological markers of TB recurrence in individuals with HIV/TB co-infection. Methods: The plasma levels of IFN-γ, TNF-α, IL-10, and IL-1β (cytokines which play important roles in the immune activation and protection against Mycobacterium tuberculosis) were measured using ELISA EIA-BEST kits. The cytokine concentrations were determined using a standard curve obtained with the standards provided by the manufacturer of each kit. Results: A total of 211 individuals were enrolled in the study as follows: 62 patients with HIV/TB co-infection, 52 with HIV monoinfection, 52 with TB monoinfection, and 45 healthy donors. Out of the 62 patients with HIV/TB, 75.8% (47) of patients were newly diagnosed with HIV and TB, and 24.2% (15) displayed recurrent TB and were newly diagnosed with HIV. Decreased levels of IFN-γ, TNF-α, and IL-10 were observed in patients with HIV/TB when compared with HIV and TB patients. However, there was no difference in IFN-γ, TNF-α, or IL-10 secretion between both HIV/TB groups. At the same time, an almost 4-fold decrease in Il-1β levels was detected in the HIV/TB group with TB recurrence when compared with the HIV/TB group (p = 0.0001); a 2.8-fold decrease when compared with HIV patients (p = 0.001); and a 2.2-fold decrease with newly diagnosed TB patients (p = 0.001). Conclusions: Significantly decreased Il-1β levels in HIV/TB patients' cohort with secondary TB indicate that this cytokine can be a potential biomarker of TB recurrence.

Immune-endocrine network in diabetes-tuberculosis nexus: does latent tuberculosis infection confer protection against meta-inflammation and insulin resistance?

Tuberculosis patients with diabetes, have higher sputum bacillary load, delayed sputum conversion, higher rates of drug resistance, higher lung cavitary involvement and extra-pulmonary TB infection, which is called as "Diabetes-Tuberculosis Nexus". However, recently we have shown a reciprocal relationship between latent tuberculosis infection and insulin resistance, which has not been reported before. In this review, we would first discuss about the immune-endocrine network, which operates during pre-diabetes and incipient diabetes and how it confers protection against LTBI. The ability of IR to augment anti-TB immunity and the immunomodulatory effect of LTBI to quench IR were discussed, under IR-LTB antagonism. The ability of diabetes to impair anti-TB immunity and ability of active TB to worsen glycemic control, were discussed under "Diabetes-Tuberculosis Synergy". The concept of "Fighter Genes" and how they confer protection against TB but susceptibility to IR was elaborated. Finally, we conclude with an evolutionary perspective about how IR and LTBI co-evolved in endemic zones, and have explained the molecular basis of "IR-LTB" Antagonism" and "DM-TB Synergy", from an evolutionary perspective.

Systemic and cerebrospinal fluid biomarkers for tuberculous meningitis identification and treatment monitoring

IMPORTANCE

Tuberculous meningitis is a life-threatening infection with high mortality and disability rates. Current diagnostic methods using cerebrospinal fluid (CSF) samples have limited sensitivity and lack predictive biomarkers for evaluating prognosis. This study's findings reveal excessive activation of the immune response during tuberculous meningitis (TBM) infection. Notably, a strong negative correlation was observed between CSF levels of monokine induced by interferon-γ (MIG) and the CSF/blood glucose ratio in TBM patients. MIG also exhibited the highest area under the curve with high sensitivity and specificity. This study suggests that MIG may serve as a novel biomarker for differentiating TBM infection in CSF or serum, potentially leading to improved diagnostic accuracy and better patient outcomes.

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.

Spatial Organization and Recruitment of Non-Specific T Cells May Limit T Cell-Macrophage Interactions Within Mycobacterium tuberculosis Granulomas

Tuberculosis (TB) is a worldwide health problem; successful interventions such as vaccines and treatment require a 2better understanding of the immune response to infection with Mycobacterium tuberculosis (Mtb). In many infectious diseases, pathogen-specific T cells that are recruited to infection sites are highly responsive and clear infection. Yet in the case of infection with Mtb, most individuals are unable to clear infection leading to either an asymptomatically controlled latent infection (the majority) or active disease (roughly 5%-10% of infections). The hallmark of Mtb infection is the recruitment of immune cells to lungs leading to development of multiple lung granulomas. Non-human primate models of TB indicate that on average <10% of T cells within granulomas are Mtb-responsive in terms of cytokine production. The reason for this reduced responsiveness is unknown and it may be at the core of why humans typically are unable to clear Mtb infection. There are a number of hypotheses as to why this reduced responsiveness may occur, including T cell exhaustion, direct downregulation of antigen presentation by Mtb within infected macrophages, the spatial organization of the granuloma itself, and/or recruitment of non-Mtb-specific T cells to lungs. We use a systems biology approach pairing data and modeling to dissect three of these hypotheses. We find that the structural organization of granulomas as well as recruitment of non-specific T cells likely contribute to reduced responsiveness.

Modeling immune cell behavior across scales in cancer

Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer.

This article is categorized under:

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Models of Systems Properties and Processes > Cellular Models

Vaccine-Specific Immune Responses against Mycobacterium ulcerans Infection in a Low-Dose Murine Challenge Model

The neglected tropical disease Buruli ulcer (BU) is an infection of subcutaneous tissue with Mycobacterium ulcerans There is no effective vaccine. Here, we assessed an experimental prime-boost vaccine in a low-dose murine tail infection model. We used the enoyl reductase (ER) domain of the M. ulcerans mycolactone polyketide synthases electrostatically coupled with a previously described Toll-like receptor 2 (TLR-2) agonist-based lipopeptide adjuvant, R₄Pam₂Cys. Mice were vaccinated and then challenged via tail inoculation with 14 to 20 CFU of a bioluminescent strain of M. ulcerans Mice receiving either the experimental ER vaccine or Mycobacterium bovis bacillus Calmette-Guérin (BCG) were equally protected, with both groups faring significantly better than nonvaccinated animals (P < 0.05). To explore potential correlates of protection, a suite of 29 immune parameters were assessed in the mice at the end of the experimental period. Multivariate statistical approaches were used to interrogate the immune response data to develop disease-prognostic models. High levels of interleukin 2 (IL-2) and low gamma interferon (IFN-γ) produced in the spleen best predicted control of infection across all vaccine groups. Univariate logistic regression revealed vaccine-specific profiles of protection. High titers of ER-specific IgG serum antibodies together with IL-2 and IL-4 in the draining lymph node (DLN) were associated with protection induced by the ER vaccine. In contrast, high titers of IL-6, tumor necrosis factor alpha (TNF-α), IFN-γ, and IL-10 in the DLN and low IFN-γ titers in the spleen were associated with protection following BCG vaccination. This study suggests that an effective BU vaccine must induce localized, tissue-specific immune profiles with controlled inflammatory responses at the site of infection.

The Role of Dimensionality in Understanding Granuloma Formation

Within the first 2-3 months of a Mycobacterium tuberculosis (Mtb) infection, 2-4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions.

Cytokine profiles in cerebrospinal fluid of patients with meningitis at a tertiary general hospital in China

BACKGROUND

There has been a great deal of evidence indicating that cytokines participate in meningeal inflammation. Different cytokine profiles may be presented in central nervous system (CNS) infection due to different pathogens. We have attempted to investigate cytokine profiles in cerebrospinal fluid (CSF) of patients with CNS infection.

METHODS

Forty-three patients with CNS infection including tuberculous meningitis, purulent meningitis and cryptococcal meningitis were enrolled and 11 patients with normal CSF were enrolled as control group. The concentrations of Th1-, Th2- and Th17-type cytokines in CSF were detected using multiplex cytokine assay. Furthermore, the correlation between CSF cytokines and CSF parameters in CNS infection was analyzed.

RESULTS

The CSF levels of IL-1β, IL-4, IL-6, IL-10, IL-17, IL-23, IL-33, IFN-γ, TNF-α and sCD40L among the patients with CNS infection were all higher than control group (all P < 0.05). A remarkable elevation of CSF IL-6 in the patients with CNS infection was observed with the least overlap of the CSF concentrations compared to controls. Moreover, CSF IL-6 levels were strongly negatively correlated with CSF glucose and the CSF/blood glucose ratio (r = -0.4375, P = 0.0042; r = -0.4991, P = 0.0009).

CONCLUSIONS

The excessive activation of immune response characterized by elevated levels of CSF Th1-, Th2- and Th17-type cytokines has been observed during CNS infection. Furthermore, we observed negative correlations between CSF IL-6 levels and CSF glucose and CSF/blood glucose ratio in CNS infection. And we suggested that combined CSF IL-6 levels with CSF glucose may serve as a novel biomarker pool for the differential of CNS infection.

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.

Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a pulmonary pathogen of major global concern. A key feature of Mtb infection in primates is the formation of granulomas, dense cellular structures surrounding infected lung tissue. These structures serve as the main site of host-pathogen interaction in TB, and thus to effectively treat TB we must clarify mechanisms of granuloma formation and their function in disease. Fibrotic granulomas are associated with both good and bad disease outcomes. Fibrosis can serve to isolate infected tissue from healthy tissue, but it can also cause difficulty breathing as it leaves scars. Little is known about fibrosis in TB, and data from non-human primates is just beginning to clarify the picture. This work focuses on constructing a hybrid multi-scale model of fibrotic granuloma formation, in order to identify mechanisms driving development of fibrosis in Mtb infected lungs. We combine dynamics of molecular, cellular, and tissue scale models from previously published studies to characterize the formation of two common sub-types of fibrotic granulomas: peripherally fibrotic, with a cuff of collagen surrounding granulomas, and centrally fibrotic, with collagen throughout granulomas. Uncertainty and sensitivity analysis, along with large simulation sets, enable us to identify mechanisms differentiating centrally versus peripherally fibrotic granulomas. These findings suggest that heterogeneous cytokine environments exist within granulomas and may be responsible for driving tissue scale morphologies. Using this model we are primed to better understand the complex structure of granulomas, a necessity for developing successful treatments for TB.

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.

Systems Medicine for Lung Diseases: Phenotypes and Precision Medicine in Cancer, Infection, and Allergy

Lung diseases cause an enormous socioeconomic burden. Four of them are among the ten most important causes of deaths worldwide: Pneumonia has the highest death toll of all infectious diseases, lung cancer kills the most people of all malignant proliferative disorders, chronic obstructive pulmonary disease (COPD) ranks third in mortality among the chronic noncommunicable diseases, and tuberculosis is still one of the most important chronic infectious diseases. Despite all efforts, for example, by the World Health Organization and clinical and experimental researchers, these diseases are still highly prevalent and harmful. This is in part due to the specific organization of tissue homeostasis, architecture, and immunity of the lung. Recently, several consortia have formed and aim to bring together clinical and molecular data from big cohorts of patients with lung diseases with novel experimental setups, biostatistics, bioinformatics, and mathematical modeling. This "systems medicine" concept will help to match the different disease modalities with adequate therapeutic and possibly preventive strategies for individual patients in the sense of precision medicine.

Computational modeling of cytokine signaling in microglia

Neuroinflammation due to glial activation has been linked to many CNS diseases. We developed a computational model of a microglial cytokine interaction network to study the regulatory mechanisms of microglia-mediated neuroinflammation. We established a literature-based cytokine network, including TNFα, TGFβ, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGFβ autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGFβ- and IL-10-mediated inhibition of TNFα was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNFα response profile to the initial TGFβ and IL-10 levels. The analysis showed two relatively shifted TNFα response profiles within separate domains of initial condition space. Further analysis revealed that TNFα exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGFβ and IL-10 on TNFα adaptation. Our analysis showed that TGFβ and IL-10 knockouts (TGFβ KO and IL-10 KO) exert divergent effects on adaptation. TFGβ KO attenuated TNFα adaptation whereas IL-10 KO enhanced TNFα adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNFα adaptation in murine macrophages and found supporting evidence. These opposing effects could be explained by differential kinetics of negative feedback. Inhibition of IL-10 reduced early negative feedback that results in enhanced TNFα-mediated TGFβ expression. We propose that differential kinetics in parallel negative feedback loops constitute a novel mechanism underlying the complex and non-intuitive pro- versus anti-inflammatory effects of individual cytokine perturbations.

Understanding PGE2, LXA4 and LTB4 balance during Mycobacterium tuberculosis infection through mathematical model

Infection of humans with Mycobacterium tuberculosis (Mtb) results in diverse outcomes that range from acute disease to establishment of persistence and to even clearance of the pathogen. These different outcomes represent the combined result of host heterogeneity on the one hand, and virulence properties of the infecting strain of pathogen on the other. From the standpoint of the host, the balance between PGE2, LXA4 and LTB4 represents at least one of the factors that dictates the eventual pathophysiology. We therefore built an ODE model to describe the host-pathogen interaction and studied the local stability properties of the system, to obtain the parametric conditions that lead to different disease outcomes. We then modulated levels of the pro- and anti-inflammatory lipid mediators to better understand the convergence between host phenotype and factors that relate to virulence properties of the pathogen. Global sensitivity analysis, using the variance-based method of extended Fourier Amplitude Sensitivity Test (eFAST), revealed that disease severity was indeed defined by combined effects of phenotypic variability at the level of both host and pathogen. Interestingly here, [PGE2] was found to act as a switch between bacterial clearance and acute disease. Our mathematical model suggests that development of more effective treatments for tuberculosis will be contingent upon a better understanding of how the intrinsic variability at the level of both host and pathogen contribute to influence the nature of interactions between these two entities.

A Petri net model of granulomatous inflammation: implications for IL-10 mediated control of Leishmania donovani infection

Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete “granulomas” within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma.

Granulomatous inflammation is a common feature of chronic infectious and non-infectious disease. In the parasitic disease visceral leishmaniasis, the formation of granulomas in the liver is a hallmark of effective cellular immunity and host resistance to infection. Conventional experimental models, however, have inherent limitations in their capacity to assess the dynamics of this complex inflammatory response and in their ability to discriminate the local contribution of different immune cells and mediators to the outcome of infection. To overcome these limitations and to provide a future platform for evaluating how novel drugs might be used to improve host resistance, we have developed a computational model of the Leishmania granuloma. Using this model, we show that conventional measures of parasite load potentially mask an underlying heterogeneity in the ability of individual granulomas to control parasite number. In addition, we have used our model to provide novel insights into the relative importance of IL-10 production by different immune cells found within the granuloma microenvironment. Our model thus provides a complementary tool to increase understanding of granulomatous inflammation in this and other important human diseases.

A mathematical model of the initial interaction between Mycobacterium tuberculosis and macrophages

There is a large body of literature describing molecular level interactions between Mycobacterium tuberculosis (Mtb) and macrophages. Macrophages initiate a range of anti-bacterial mechanisms in response to infection, and Mtb is capable of surviving and circumventing many of these responses. We apply a computational approach to ask: what are the effects on the cellular level of these opposing interactions? The model considers the interplay between bacterial killing and the pathogen's interference with macrophage function. The results reveal an oscillating balance between host and pathogen, but the balance is transient and varies in length, indicating that stochasticity in the bacterial population or host response could contribute to the diverse incubation periods observed in exposed individuals. The model captures host and strain variation and gives new insight into host-pathogen compatibility and co-evolution.

Latent Tuberculosis: Models, Computational Efforts and the Pathogen's Regulatory Mechanisms during Dormancy

Latent tuberculosis is a clinical syndrome that occurs after an individual has been exposed to the Mycobacterium tuberculosis (Mtb) Bacillus, the infection has been established and an immune response has been generated to control the pathogen and force it into a quiescent state. Mtb can exit this quiescent state where it is unresponsive to treatment and elusive to the immune response, and enter a rapid replicating state, hence causing infection reactivation. It remains a gray area to understand how the pathogen causes a persistent infection and it is unclear whether the organism will be in a slow replicating state or a dormant non-replicating state. The ability of the pathogen to adapt to changing host immune response mechanisms, in which it is exposed to hypoxia, low pH, nitric oxide (NO), nutrient starvation, and several other anti-microbial effectors, is associated with a high metabolic plasticity that enables it to metabolize under these different conditions. Adaptive gene regulatory mechanisms are thought to coordinate how the pathogen changes their metabolic pathways through mechanisms that sense changes in oxygen tension and other stress factors, hence stimulating the pathogen to make necessary adjustments to ensure survival. Here, we review studies that give insights into latency/dormancy regulatory mechanisms that enable infection persistence and pathogen adaptation to different stress conditions. We highlight what mathematical and computational models can do and what they should do to enhance our current understanding of TB latency.

Increased cytokines response in patients with tuberculosis complicated with chronic obstructive pulmonary disease

Objectives

To explore the change and its significance of cytokines in patients with pulmonary tuberculosis complicated with COPD.

Methods

The immune function of 152 cases of pulmonary tuberculosis with COPD was detected to compare with 150 cases of patients with pulmonary tuberculosis, 157 cases of patients with COPD and 50 cases of healthy volunteers who were in the hospital during the same period. T lymphocyte cell population in peripheral blood was detected by flow cytometry. The serum levels of sIL-2R, IL-6, IFN-γ, TNF-α were measured using ELISA.

Results

The percentage of CD4+ T cells in TB patients with or without COPD and COPD patients without TB was significantly lower than that in control group. The percentage of CD4+ T cells in patients with TB and COPD was significantly lower than that in the non-COPD TB patients. The percentage of CD8+ T cells was higher in the TB patients group than that in control group. The CD4+/CD8+ ratio in the TB patients group was significantly lower than that in control group. The concentrations of sIL-2R, IL-6, TNF-α, IFN-γ in TB patients with or without COPD and COPD patients without TB were significantly higher than those in control group. In addition, sIL-2R, IL-6, TNF-α concentrations in the patients with TB and COPD were higher than those in the non-COPD TB patients. The concentrations of sIL-2R, IL-6, TNF-α, IFN-γ in COPD patients with TB were significantly higher than those in COPD patients without TB. There was a significant negative correlation between serum levels of TNF-α, IL-6 and FEV1 (%, predicted) in COPD without TB group.

Conclusions

The patients with pulmonary tuberculosis complicated with COPD were impaired in cellular immunity, and its extent of immune impairment is more serious than those of the patients with pulmonary tuberculosis and the patients with COPD.

Computational modeling of tuberculous meningitis reveals an important role for tumor necrosis factor-α

Tuberculosis is a global health issue with annually about 1.5 million deaths and 2 billion infected people worldwide. Extra-pulmonary tuberculosis comprises 13% of all cases of which tuberculous meningitis is the most severe. It has a high mortality and is often diagnosed once irreversible neurological damage has already occurred. Development of diagnostic and treatment strategies requires a thorough understanding of the pathogenesis of tuberculous meningitis. This disease is characterized by the formation of a cerebral granuloma, which is a collection of immune cells that attempt to immunologically restrain, and physically contain bacteria. The cytokine tumor necrosis factor-α is known for its important role in granuloma formation. Because traditional experimental animal studies exploring tuberculous meningitis are difficult and expensive, another approach is needed to begin to address this important and significant disease outcome. Here, we present an in silico model capturing the unique immunological environment of the brain that allows us to study the key mechanisms driving granuloma formation in time. Uncertainty and sensitivity analysis reveals a dose-dependent effect of tumor necrosis factor-α on bacterial load and immune cell numbers thereby influencing the onset of tuberculous meningitis. Insufficient levels result in bacterial overgrowth, whereas high levels lead to uncontrolled inflammation being detrimental to the host. These findings have important implications for the development of immuno-modulating treatment strategies for tuberculous meningitis.

Fate of Mycobacterium tuberculosis in peroxidase-loaded resting murine macrophages

BACKGROUND

Myeloperoxidase (MPO), in the presence of hydrogen peroxide and a halide represent an efficient microbicidal mechanism of phagocytic cells. MPO is abundant in neutrophils which also respond to infection by producing large amounts of reactive oxygen species (ROS). MPO, ROS and halide constitute a very toxic antimicrobial system (called the Klebanoff system or KS). Resting mature macrophages do not contain granular MPO and thus are unable to kill pathogenic mycobacteria and some other microorganisms by this system.

EXPERIMENTAL

Under the hypothesis that transforming macrophages into peroxidase-positive (PO(+)) cells, these cells would be able to kill Mycobacterium tuberculosis, in this study, mature macrophages were loaded with exogenous peroxidase and were tested for their capacity to kill the Mycobacterium in the presence or in the absence of hydrogen peroxide.

RESULTS

It was found that PO-loaded macrophages eagerly ingest M. tuberculosis, but do not show a significant mycobactericidal activity on this microorganism despite that it is highly susceptible to the Klebanoff system in vitro. Failure of PO-loaded macrophages to kill M. tuberculosis may obey either to an inappropriate location of the exogenous PO in these cells or more likely, to the presence of efficient detoxifying mechanisms in the bacteria. On the contrary, MPO-loaded or unloaded macrophages efficiently killed Listeria monocytogenes.

CONCLUSION

The lack of granular MPO in mature macrophages, and the predilection of mycobacteria to infect these cells are two situations that favor the development of tuberculosis and related diseases, such as leprosy and Buruli ulcer.

Understanding TB latency using computational and dynamic modelling procedures

The Mycobacterium tuberculosis bacilli's potency to cause persistent latent infection that is unresponsive to the current cocktail of TB drugs is strongly associated with its ability to adapt to changing intracellular environments, and tolerating, evading and subverting host defence mechanisms. We applied a combination of bioinformatics and mathematical modelling methods to enhance the understanding of TB latency dynamics. Analysis of time course microarray gene expression data was carried out and gene profiles for bacilli adaptation and survival in latency, simulated by hypoxia were determined. Reverse network engineering techniques were used to predict gene dependencies and regulatory interactions. Biochemical systems theory was applied to mathematically model the inferred gene regulatory networks. Significant regulatory genes involved in latency were determined by a combination of systems biology procedures and mathematical modelling of the inferred regulatory networks. Analysis of gene clusters of the inferred networks in the stationary and non-replicating phases of the bacilli predicted probable functions of some of the latency genes to be associated with latency genes of known functions. The systems biology approach and mathematical computational deletion experiments predicted key genes in the TB latency/dormancy program that may be possible TB drug targets. However, these gene candidates require experimental testing and validation.

The timing of IFNβ production affects early innate responses to Listeria monocytogenes and determines the overall outcome of lethal infection

Dendritic cells (DCs) and natural killer (NK) cells are essential components of the innate immunity and play a crucial role in the first phase of host defense against infections and tumors. Listeria monocytogenes (Lm) is an intracellular pathogen that colonizes the cytosol of eukaryotic cells. Recent findings have shown Lm specifically in splenic CD8a⁺ DCs shortly after intravenous infection. We examined gene expression profiles of mouse DCs exposed to Lm to elucidate the molecular mechanisms underlying DCs interaction with Lm. Using a functional genomics approach, we found that Lm infection induced a cluster of late response genes including type I IFNs and interferon responsive genes (IRGs) in DCs. Type I INFs were produced at the maximal level only at 24 h post infection indicating that the regulation of IFNs in the context of Lm infection is delayed compared to the rapid response observed with viral pathogens. We showed that during Lm infection, IFNγ production and cytotoxic activity were severely impaired in NK cells compared to E. coli infection. These defects were restored by providing an exogenous source of IFNβ during the initial phase of bacterial challenge. Moreover, when treated with IFNβ during early infection, NK cells were able to reduce bacterial titer in the spleen and significantly improve survival of infected mice. These findings show that the timing of IFNβ production is fundamental to the efficient control of the bacterium during the early innate phase of Lm infection.

A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages

The majority of individuals infected with Mycobacterium tuberculosis (Mtb) bacilli develop latent infection. Mtb becomes dormant and phenotypically drug resistant when it encounters multiple stresses within the host, and expresses a set of genes, known as the dormancy regulon, in vivo. These genes are expressed in vitro in response to nitric oxide (NO), hypoxia (oxygen deprivation), and nutrient starvation. The occurrence and reactivation of latent tuberculosis (TB) is not clearly understood. The ability of the pathogen to enter and exit from different states is associated with its ability to cause persistent infection. During infection it is not known whether the organism is in a persistent slow replicating state or a dormant non-replicating state, with the latter ultimately causing a latent infection with the potential to reactivate to active disease. We collected gene expression data for Mtb bacilli under different stress conditions that simulate latency or dormancy. Time course experiments were selected and differentially expressed gene profiles were determined at each time point. A mathematical model was then developed to show the dynamics of Mtb latency based on the profile of differentially expressed genes. Analysis of the time course data show the dynamics of latency occurrence in vitro and the mathematical model reveals all possible scenarios of Mtb latency development with respect to the different conditions that may be produced by the immune response in vivo. The mathematical model provides a biological explanation of how Mtb latency occurs based on observed gene expression changes in in vitro latency models.

Mechanism of the biological effect of the tumor necrosis factor-аlpha at psoriasis

Among different cytokines, tumor necrosis factor-аlpha (tumor necrosis factor α, TNF-α) plays a special role in psoriatic immunopathogenesis. Data on this cytokine collected for the recent decades made it possible to create a number of biological drugs blocking TNF-α, which are successfully applied in clinical practice for treating medium to severe psoriasis and psoriatic arthritis. This review presents general information about the cytokine structure and its receptor apparatus, regulation mechanisms of TNF-α synthesis and ways of signal transmission as the basis needed to implement the biological effects of cytokine in the development of psoriatic skin affections.