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Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a major global health problem. Lung granulomas are organized structures of host immune cells that function to contain the bacteria. Cytokine expression is a critical component of the protective immune response, but inappropriate cytokine expression can exacerbate TB. Although the importance of proinflammatory cytokines in controlling M. tuberculosis infection has been established, the effects of anti-inflammatory cytokines, such as IL-10, in TB are less well understood. To investigate the role of IL-10, we used an Ab to neutralize IL-10 in cynomolgus macaques during M. tuberculosis infection. Anti–IL-10–treated nonhuman primates had similar overall disease outcomes compared with untreated control nonhuman primates, but there were immunological changes in granulomas and lymph nodes from anti–IL-10–treated animals. There was less thoracic inflammation and increased cytokine production in lung granulomas and lymph nodes from IL-10–neutralized animals at 3–4 wk postinfection compared with control animals. At 8 wk postinfection, lung granulomas from IL-10–neutralized animals had reduced cytokine production but increased fibrosis relative to control animals. Although these immunological changes did not affect the overall disease burden during the first 8 wk of infection, we paired computational modeling to explore late infection dynamics. Our findings support that early changes occurring in the absence of IL-10 may lead to better bacterial control later during infection. These unique datasets provide insight into the contribution of IL-10 to the immunological balance necessary for granulomas to control bacterial burden and disease pathology in M. tuberculosis infection.

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world’s deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

Neutrophil infiltration into tuberculous granulomas is often associated with higher bacteria loads and severe disease but the basis for this relationship is not well understood. To better elucidate the connection between neutrophils and pathology in primate systems, we paired data from experimental studies with our next generation computational model GranSim to identify neutrophil-related factors, including neutrophil recruitment, lifespan, and intracellular bacteria numbers, that drive granuloma-level outcomes. We predict mechanisms underlying spatial organization of neutrophils within granulomas and identify how neutrophils contribute to granuloma dissemination. We also performed virtual deletion and depletion of neutrophils within granulomas and found that neutrophils play a nuanced role in determining granuloma outcome, promoting uncontrolled bacterial growth in some and working to contain bacterial growth in others. Here, we present three key results: We show that neutrophils can facilitate local dissemination of granulomas and thereby enable the spread of infection. We suggest that neutrophils influence CFU burden during both innate and adaptive immune responses, implying that they may be targets for therapeutic interventions during later stages of infection. Further, through the use of uncertainty and sensitivity analyses, we predict which neutrophil processes drive granuloma severity and structure.

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

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), kills more individuals worldwide per year than any other infectious agent. As the hallmark of TB, lung granulomas are complex structures composed of immune cells that interact with and surround bacteria, infected cells, and a necrotic core. This interaction leads to diverse granuloma outcomes across time, ranging from bacterial sterilization to uncontrolled bacterial growth, as well as diverse spatial structures. At this time, there are no systematic quantitative methods to classify the formation, function, and spatial characteristics of granulomas. This type of analysis would enable better understanding and prediction of granuloma behaviors that have known associations with poor clinical outcomes for TB patients. Herein, we develop a temporal and spatial analysis framework for TB granulomas using a systems biology approach combining in silico granuloma modeling, geographic information systems, topological data analysis, and machine learning. We apply this framework to simulated granulomas to understand temporal granuloma dynamics, quantify granuloma spatial structure, and predict the relationship between granuloma structure and bacterial growth. As a proof-of-concept, we apply our in silico predictions to in vivo derived data to test our framework for future applications and as a personalized medicine intervention.

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.

Mycobacterium tuberculosis (Mtb) infection causes tuberculosis (TB), a disease characterized by development of granulomas. Granulomas consist of activated immune cells that cluster together to limit bacterial growth and restrict dissemination. Control of the TB epidemic has been limited by lengthy drug regimens, antibiotic resistance, and lack of a robustly efficacious vaccine. Fibrosis commonly occurs during treatment and is associated with both positive and negative disease outcomes in TB but little is known about the processes that initiate fibrosis in granulomas. Human and nonhuman primate granulomas undergoing fibrosis can have spindle-shaped macrophages with fibroblast-like morphologies suggesting a relationship between macrophages, fibroblasts, and granuloma fibrosis. This relationship has been difficult to investigate because of the limited availability of human pathology samples, the time scale involved in human TB, and overlap between fibroblast and myeloid cell markers in tissues. To better understand the origins of fibrosis in TB, we used a computational model of TB granuloma biology to identify factors that drive fibrosis over the course of local disease progression. We validated the model with granulomas from nonhuman primates to delineate myeloid cells and lung-resident fibroblasts. Our results suggest that peripheral granuloma fibrosis, which is commonly observed, can arise through macrophage-to-myofibroblast transformation (MMT). Further, we hypothesize that MMT is induced in M1 macrophages through a sequential combination of inflammatory and anti-inflammatory signaling in granuloma macrophages. We predict that MMT may be a mechanism underlying granuloma-associated fibrosis and warrants further investigation into myeloid cells as drivers of fibrotic disease., Author summary Tuberculosis is a disease caused by Mycobacterium tuberculosis (Mtb), a bacterium that infects over a third of the world’s population. The only available vaccine for TB has limited efficacy and drug treatment involves several antibiotics that are taken for several months. These drugs can have significant side-effects and a lack of compliance can lead to drug resistance in Mtb. A hallmark of Mtb infection is the development of clusters of cells that form around infected macrophages to contain the infection called granulomas. Older granulomas, or granulomas in patients treated with antibiotic, often become fibrotic and this can cause chronic lung problems long after the Mtb infection has cleared. The process that drives a fibrotic outcome has been difficult to assess in vivo. Herein we combined wet-lab and computational experimentation to identify a novel mechanism leading to peripheral fibrosis in granulomas. We find that macrophages transforming into myofibroblast-like cells, may be a key pathway to granuloma-associated fibrosis, a phenomena that has not been well characterized in vivo. Further we identified factors that can inhibit fibrosis development during TB that could be therapeutic targets during TB treatment to limit the risk of long-term tissue damage in TB.

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination., Author summary Tuberculosis (TB) is caused by infection with Mycobacterium tuberculosis (Mtb) and kills 3 people per minute worldwide. Granulomas, spherical structures composed of immune cells surrounding bacteria, are the hallmark of Mtb infection and sometimes fail to contain the bacteria and disseminate, leading to further granuloma growth within the lung environment. To date, the mechanisms that determine granuloma dissemination events have not been characterized. We present a computational multi-scale model of granuloma formation and dissemination within primate lungs. Our computational model is calibrated to multiple experimental datasets across the cellular, granuloma, and whole-lung scales of non-human primates. We match to both individual granuloma and granuloma-population datasets, predict likelihood of dissemination events, and predict a critical role for multifunctional CD8+ T cells and macrophage-bacteria interactions to prevent infection dissemination.

Within the first 2&ndash, 3 months of a Mycobacterium tuberculosis (Mtb) infection, 2&ndash, 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.

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.

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 M 1 polarization early during infection and to improve outcome.

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.

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. Results: The model was calibrated using experimental data from the lungs and blood of NHPs. The addition of DCs allowed us to investigate in greater detail mechanisms of recruitment, trafficking and antigen presentation and their role in tuberculosis infection. Conclusion: The main conclusion of this study is that early events after Mtb infection are critical to establishing a timely and effective response. Manipulating CD8+ and CD4+ T cell proliferation rates, as well as DC migration early on during infection can determine the difference between bacterial clearance vs. uncontrolled bacterial growth and dissemination.

The effect of Mycobacterium tuberculosis inocula size on T cell priming in the lymph node and effector T cells in the lung remains controversial. In this study, we used a naive mouse model, without the transfer of transgenic T cells, in conjunction with mathematical model to test whether infection with higher aerosolized inocula would lead to increased priming of M. tuberculosis–specific T cells in the lung-draining lymph node. Our data do not support that inoculum size has a measurable influence on T cell priming in the lymph nodes but is associated with more cells overall in the lung, including T cells. To account for increased T cells in the lungs, we tested several possible mechanisms, and recruitment of T cells to the lungs was most influenced by inoculum dose. We also identified IL-10 as a possible mechanism to explain the lack of influence of inoculum dose on priming of T cells in the lymph node.

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery., Author Summary Tuberculosis (TB) is a disease that is caused by infection after inhaling the bacterium Mycobacterium tuberculosis. Not everyone infected with TB bacteria becomes sick. As a result, two TB-related conditions have been categorized: latent TB infection (not sick but still harboring the bacteria) and active TB disease. If not treated properly, active TB disease can be fatal. Almost 1.3 million die of TB worldwide each year, with ~8,6 million new infections in 2013. No effective vaccine is available to protect against TB and treatment of infection with multiple antibiotics is lengthy (6–9 months), with non-compliance being a major factor for the emergence of drug-resistant strains. A key step in developing effective vaccines and possibly shorter treatment regimens is the ability to identify biomarkers that correlate prognosis and progression to infection (similar to how cholesterol levels are a measure of heart health). In this study we show how pairing computer modeling, statistics and mathematics with datasets derived from non-human primate studies can accelerate biomarker discovery, and offer a new approach to identifying correlates of protection that will be useful in clinical practice, particularly in developing countries where TB is most prevalent.

Vibrio cholerae is a strict human pathogen that causes the disease cholera. It is an old-world pathogen that has re-emerged as a new threat since the early 1990s. V. cholerae colonizes the upper, small intestine where it produces a toxin that leads to watery diarrhea, characterizing the disease (Kahn et al., 1988). The dynamics of colonization by the bacteria of the intestines are largely unknown. Although a large initial infectious dose is required for infection, data suggests that only a smaller sub-population colonizes a portion of the small bowel leading to disease. There are many barriers to colonization in the intestines including peristalsis, fluid wash-out, viscosity of the mucus layer, and pH. We are interested in identifying the mechanisms that allow this sub-population of bacteria to survive and colonize the intestines when faced with these barriers. To elaborate the dynamics of V. cholerae infection, we have developed a mathematical model based on a convection-diffusion-reaction-swimming equation capturing bacterial dynamics coupled with Stokes equations governing fluid velocity where we developed a novel non-local boundary condition. Our results indicate that both host and bacterial factors contribute to bacterial density in the gut. Host factors include intestinal diffusion and convection rates while bacterial factors include adherence, motility and growth rates. This model can ultimately be used to test therapeutic strategies against V. cholerae.

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. Copyright © 2011 by The American Association of Immunologists, Inc.

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.

Mycobacterium tuberculosis is one of the world’s most deadly human pathogens; an integrated understanding of how it successfully survives in its host is crucial to developing new treatment strategies. One notable characteristic of infection with M. tuberculosis is the formation of granulomas, aggregates of immune cells whose structure and function may reflect success or failure of the host to contain infection. One central regulator of host responses to infection, including granuloma formation, is the pleiotropic cytokine TNF-α. Experimental work has characterized roles for TNF in macrophage activation; regulation of apoptosis; chemokine and cytokine production; and regulation of cellular recruitment via transendothelial migration. Separating the effects of these functions is presently difficult or impossible in vivo. To this end, we applied a computational model to understand specific roles of TNF in control of tuberculosis in a single granuloma. In the model, cells are represented as discrete entities on a spatial grid responding to environmental stimuli by following programmed rules determined from published experimental studies. Simulated granulomas emerge as a result of these rules. After confirming the importance of TNF in this model, we assessed the effects of individual TNF functions. The model predicts that multiple TNF activities contribute to control of infection within the granuloma, with macrophage activation as a key effector mechanism for controlling bacterial growth. Results suggest that bacterial numbers are a strong contributing factor to granuloma structure with TNF. Finally, TNF-dependent apoptosis may reduce inflammation at the cost of impairing mycobacterial clearance.

Tumor necrosis factor (TNF) is a prototypic proinflammatory cytokine that contributes significantly to the development of immunopathology in various disease states. A complication of TNF blockade therapy, which is used increasingly for the treatment of chronic inflammatory diseases, is the reactivation of latent tuberculosis. This study used a low-dose aerogenic model of murine tuberculosis to analyze the effect of TNF neutralization on disease progression in mice with chronic tuberculous infections. Histological, immunohistochemical, and flow cytometric analyses ofMycobacterium tuberculosis-infected lung tissues revealed that the neutralization of TNF results in marked disorganization of the tuberculous granuloma, as demonstrated by the dissolution of the previously described B-cell-macrophage unit in granulomatous tissues as well as by increased inflammatory cell infiltration. Quantitative gene expression studies using laser capture microdissected granulomatous lung tissues revealed that TNF blockade in mice chronically infected withM. tuberculosisleads to the enhanced expression of specific proinflammatory molecules. Collectively, these studies have provided evidence suggesting that in the chronic phase ofM. tuberculosisinfection, TNF is essential for maintaining the structure of the tuberculous granuloma and may regulate the granulomatous response by exerting an anti-inflammatory effect through modulation of the expression of proinflammatory mediators.

Tuberculosis is the leading cause of death due to infectious diseases in the world to- day, and it is increasing due to co-infection with HIV-1, the causative agent of AIDS. Here, we examine the impact that HIV-1 infection has on persons with latent tuberculosis. Based on previ- ous work, we develop a mathematical model of an adaptive immune response in the lung which considers relevant immune effectors such as macrophages, various sub-populations of T-cells, and key cytokines to predict which mechanisms are important to HIV-1 infection induced reactivation of tuberculosis. Our results indicate that persons latently infected with TB who are subsequently co-infected with HIV-1 will suffer reactive TB. The mechanisms that contribute to this are essen- tially related to a completely different cytokine environment at the onset of HIV-1 infection due to the presence of Mycobacterium tuberculosis. Our analysis suggests that macrophages play an important role during co-infection and decreases in macrophage counts are coupled to a decline in CD4 + T-cells and increased viral loads. These mechanisms are also coupled to lower recruitment of T-cells and macrophages, compromising protective immunity in the lung and eventually leading to TB reactivation. These results point to potential targets for drug and vaccine therapies.

Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo , we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ∼66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.

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 (c) the effect of vaccination.

Tuberculosis remains a significant global health burden. Little is understood regarding the critical pathogen-driven factors and host immune responses that result in latent infection and reactivation. The Wayne model is an in vitro model of latent infection in which Mycobacterium tuberculosis undergoes a hypoxia induced nonreplicating persistent state of metabolism. Using this model, important findings in genomic and proteomic factors involved in newly defined metabolic pathways and drug susceptibility have been identified. The mouse remains the most popular in vivo model latent infection. The Cornell model, reflecting chronic infection altered by antibiotic treatment, and the low to moderate dose chronic infection model have been used. Studies using this model have revealed important insights regarding TNF, IFN, reactive nitrogen intermediates, IL-10, CD4 and CD8 function during latent infection. However, the immune responses in the murine model most likely reflect chronic infection rather than true latency. The non-human primate model is the only animal model in which a true latent state occurs. However, its limited availability, high cost and support required are impractical for frequent use. Disparate data from multiple studies can be used to predict complex biologic interactions through mathematical simulations (in silico model). Mathematical modeling can be use to foresee important insights into host–pathogen interactions that can then be confirmed by in vivo experiments. Since no single perfect model of latent infection exists, the use of multiple models has and will continue to provide significant contributions in our understanding of latency and reactivation of tuberculosis.

Mathematical models are emerging as important tools in the study of microbiology. As an illustrative example, we present results from several models each generated to study the interaction of Mycobacterium tuberculosis and the immune system. Different mathematical models were formulated on the basis of assumptions regarding system-component interactions, enabling us to explore specific aspects at diverse biological scales (e.g. intracellular, cell-cell interactions, and cell population dynamics). In addition, we were able to examine both temporal and spatial aspects. At each scale, there were consistent themes that emerged as determinative in infection outcome. Factors we identified include both host and microbial characteristics. The use of the models lies in generating hypotheses that can then be tested experimentally. Here, we outline the primary host and bacterial factors that we have identified as key mechanisms that contribute to the success of M. tuberculosis as a human pathogen. Our goal is to stimulate experimentation and foster collaborations between theoretical and experimental scientists.

Previous experimental studies suggest thatMycobacterium tuberculosisinhibits a number of macrophage intracellular processes associated with antigen presentation, including antigen processing, MHC class II expression, trafficking of MHC class II molecules, and peptide-MHC class II binding. In this study, we investigate why multiple mechanisms have been observed. Specifically, we consider what purpose multiple mechanisms may serve, whether experimental protocols favor the detection of some mechanisms over others, and whether alternative mechanisms exist. By using a mathematical model of antigen presentation in macrophages that tracks levels of various molecules, including peptide–MHC class II complexes on the cell surface, we show that mechanisms targeting MHC class II expression are effective at inhibiting antigen presentation, but only after a delay of at least 10 h. By comparison, the effectiveness of mechanisms targeting other cellular processes is immediate, but may be attenuated under certain conditions. Therefore, targeting multiple cellular processes may represent an optimal strategy forM. tuberculosis(and other pathogens with relatively long doubling times) to maintain continuous inhibition of antigen presentation. In addition, based on a sensitivity analysis of the model, we identify other cellular processes that may be targeted by such pathogens to accomplish the same effect, representing potentially novel mechanisms.

The use of different mathematical tools to study biological processes is necessary to capture effects occurring at different scales. Here we study as an example the immune response to infection with the bacteria Mycobacterium tuberculosis, the causative agent of tuberculosis (TB). Immune responses are both global (lymph nodes, blood, and spleen) as well as local (site of infection) in nature. Interestingly, the immune response in TB at the site of infection results in the formation of spherical structures comprised of cells, bacteria, and effector molecules known as granulomas. In this work, we use four different mathematical tools to explore both the global immune response as well as the more local one (granuloma formation) and compare and contrast results obtained using these methods. Applying a range of approaches from continuous deterministic models to discrete stochastic ones allows us to make predictions and suggest hypotheses about the underlying biology that might otherwise go unnoticed. The tools developed and applied here are also applicable in other settings such as tumor modeling.

Infection with Mycobacterium tuberculosis is a major world health problem. An estimated 2 billion people are presently infected and the disease causes approximately 3 million deaths per year. After bacteria are inhaled into the lung, a complex immune response is triggered leading to the formation of multicellular structures termed granulomas. It is believed that the collection of host granulomas either contain bacteria resulting in a latent infection or are unable to do so, leading to active disease. Thus, understanding granuloma formation and function is essential for improving both diagnosis and treatment of tuberculosis. Granuloma formation is a complex spatio-temporal system involving interactions of bacteria, specific immune cells, including macrophages, CD4+ and CD8+ T cells, as well as immune effectors such as chemokine and cytokines. To study this complex dynamical system we have developed an agent-based model of granuloma formation in the lung. This model combines continuous representations of chemokines with discrete agent representations of macrophages and T cells in a cellular automata-like environment. Our results indicate that key host elements involved in granuloma formation are chemokine diffusion, prevention of macrophage overcrowding within the granuloma, arrival time, location and number of T cells within the granuloma, and an overall host ability to activate macrophages. Interestingly, a key bacterial factor is its intracellular growth rate, whereby slow growth actually facilitates survival.

There is significant disagreement in the epidemiological literature regarding the extent to which reinfection of latently infected individuals contributes to the dynamics of tuberculosis (TB) epidemics. In this study we present an epidemiological model of Mycobacterium tuberculosis infection that includes the process of reinfection. Using analysis and numerical simulations, we observe the effect that varying levels of reinfection has on the qualitative dynamics of the TB epidemic. We examine cases of the model both with and without treatment of actively infected individuals. Next, we consider a variation of the model describing a heterogeneous population, stratified by susceptibility to TB infection. Results show that a threshold level of reinfection exists in all cases of the model. Beyond this threshold, the dynamics of the model are described by a backward bifurcation. Uncertainty analysis of the parameters shows that this threshold is too high to be attained in a realistic epidemic. However, we show that even for sub-threshold levels of reinfection, including reinfection in the model changes dynamic behavior of the model. In particular, when reinfection is present the basic reproductive number, $R_0$, does not accurately describe the severity of an epidemic.

Lung granulomas are the pathologic hallmark of tuberculosis (TB). T cells are a major cellular component of TB lung granulomas and are known to play an important role in containment of Mycobacterium tuberculosis (Mtb) infection. We used cynomolgus macaques, a non-human primate model that recapitulates human TB with clinically active disease, latent infection or early infection, to understand functional characteristics and dynamics of T cells in individual granulomas. We sought to correlate T cell cytokine response and bacterial burden of each granuloma, as well as granuloma and systemic responses in individual animals. Our results support that each granuloma within an individual host is independent with respect to total cell numbers, proportion of T cells, pattern of cytokine response, and bacterial burden. The spectrum of these components overlaps greatly amongst animals with different clinical status, indicating that a diversity of granulomas exists within an individual host. On average only about 8% of T cells from granulomas respond with cytokine production after stimulation with Mtb specific antigens, and few “multi-functional” T cells were observed. However, granulomas were found to be “multi-functional” with respect to the combinations of functional T cells that were identified among lesions from individual animals. Although the responses generally overlapped, sterile granulomas had modestly higher frequencies of T cells making IL-17, TNF and any of T-1 (IFN-γ, IL-2, or TNF) and/or T-17 (IL-17) cytokines than non-sterile granulomas. An inverse correlation was observed between bacterial burden with TNF and T-1/T-17 responses in individual granulomas, and a combinatorial analysis of pair-wise cytokine responses indicated that granulomas with T cells producing both pro- and anti-inflammatory cytokines (e.g. IL-10 and IL-17) were associated with clearance of Mtb. Preliminary evaluation suggests that systemic responses in the blood do not accurately reflect local T cell responses within granulomas., Author Summary The characteristic feature of Mycobacterium tuberculosis (Mtb) infection is the formation of lesions, which are organized structures of immune cells in the lungs called granulomas, which contain the bacteria. When the granuloma functions effectively, it can kill the bacteria. T cells (a type of immune cell, also present in granulomas) are known to play an important role in control of tuberculosis. However, functions of T cells at individual granuloma levels are unknown. Here, we studied the functional characteristics of T cells, which are defined by the production of chemical messengers (cytokines) at the granuloma level in a non-human primate model. We compared the relationship between cytokine response and the number of bacteria (Mtb) in each granuloma. Each granuloma was found to be unique, suggesting different types exist within an animal. Only a small proportion of T cells produced any cytokine, but different types of cytokines were observed within each granuloma. A balance between different types of cytokine was associated with more killing of bacteria in granulomas. Understanding how to improve the T cell responses to obtain killing of bacteria in the granuloma will be important for vaccine development.

Studies of HIV-1-infected humans indicate that the thymus can be infected by HIV-1. In some of these patients, there is a significant CD4(+) T cell decline and a faster disease progression. This phenomenon is more evident in pediatric patients who depend heavily on their thymus for generation of new T cells. We hypothesize that HIV-1 causes T cell regenerative failure within the thymus, which has a profound impact on disease progression. Building on our established human thymopoiesis model, we include dynamic interactions between different HIV-1 strains (R5 and X4) and thymocytes. Our results predict that thymic infection with different HIV-1 strains induces thymic dysfunction to varying degrees, contributing to differences in disease progression as observed in both HIV-1-infected children and adults. Thymic infection in children is more severe than in adults, particularly during X4 infection. This outcome is likely due to both a higher viral load and a more active thymus in pediatric patients. Our results also indicate that a viral strain switch from R5 to X4 induces further deterioration in thymopoiesis. We predict that both viral and host factors play key roles in controlling thymic infection, including strain virulence and health status of the thymus.

Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB., Author summary Tuberculosis (TB) is caused by infection with the bacterium Mycobacterium tuberculosis (Mtb) and kills 1.5 million people each year. TB requires at least 6 months of treatment with up to four drugs, and is characterized by formation of granulomas in patient lungs. Granulomas are spherical collections of host cells and bacteria. Fluoroquinolones (FQs) are a class of drug that could help shorten TB treatment. Three FQs that are used to treat TB are: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, it is unclear if one FQ is better than the others at treating TB, in part because little is known about how these drugs distribute and work inside the lung granulomas. We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why. Our computer model is calibrated to multiple experimental data sets. We compare the three FQs by multiple metrics, and predict that MXF is better than LVX and GFX because it kills bacteria more quickly, and it works better when patients miss doses. However, all three FQs are unable to kill a part of the bacterial population living in the center of granulomas. Our results can now inform future experimental studies.

Although almost a third of the world’s population is infected with the bacterial pathogen Mycobacterium tuberculosis, our understanding of the functions of many immune factors involved in fighting infection is limited. Determining the role of the immunosuppressive cytokine IL-10 at the level of the granuloma has proven difficult because of lesional heterogeneity and the limitations of animal models. In this study, we take an in silico approach and, through a series of virtual experiments, we predict several novel roles for IL-10 in tuberculosis granulomas: 1) decreased levels of IL-10 lead to increased numbers of sterile lesions, but at the cost of early increased caseation; 2) small increases in early antimicrobial activity cause this increased lesion sterility; 3) IL-10 produced by activated macrophages is a major mediator of early antimicrobial activity and early host-induced caseation; and 4) increasing levels of infected macrophage derived IL-10 promotes bacterial persistence by limiting the early antimicrobial response and preventing lesion sterilization. Our findings, currently only accessible using an in silico approach, suggest that IL-10 at the individual granuloma scale is a critical regulator of lesion outcome. These predictions suggest IL-10–related mechanisms that could be used as adjunctive therapies during tuberculosis.

This chapter focuses on mathematical models of colonization and persistent bacterial infections. It reviews the modeling method and the state of the field and then focuses on three key areas where modeling has, and will continue to have, an impact: the ecology of the indigenous microflora and its plasmids, Helicobacter pylori colonization, and host-pathogen interactions with Mycobacterium tuberculosis. Mathematical models of host-pathogen dynamics are formulated on the basis of specific assumptions regarding the system's components and their interactions. Models of persistent viral infections, namely, human immunodeficiency virus (HlV)-host models, also have a successful recent history. H. pylori induces chronic gastric inflammation that results in peptic ulcer disease or gastric cancer in a small set of infected persons. This high ratio of mucus-living to adherent bacteria, although characteristic, is not necessary for colonization, as low concentrations of H. pylori may be present in the mucus during persistence. Incorporation of the dynamic host response into a model of H. pylori colonization is critical if one understands the initial features of the interactions between microbe and host, as well as the phenomena that permit persistence to develop. If we consider long-term associations between bacteria and humans a question of bacterial ecology, such as persistent infections or the homeostasis of an indigenous microflora, it becomes more logical to consider mathematical approaches to understanding these associations, as modeling has long been used by ecologists.

The dynamics of Helicobacter pylori colonization from its acquisition through the development of steady-state are examined through a mathematical model that includes the host response. The model encompasses both host and microbiological variation. The individual capacity of the host response is shown to be a key model parameter, leading to either transient or persistent colonization, whereas the growth rate of that response has little effect. Analyses of competing strains indicate that each must occupy a specific niche, otherwise exclusion occurs. The model implies that there exists a lower bound on the host response to the indigenous microflora that is consistent with current biological views of H. pylori . Parallel models may be useful in understanding other persistent host–microbial interactions.

Finding more effective vaccines against tuberculosis (TB) and improved preventive treatments against endogenous reactivation of latent TB is strategic to block transmission and reach the WHO goal of eliminating TB by 2050. Key related open questions in TB research include: i) what are the determinants of a strong memory response upon primary infection? ii) what is the role of cytokines towards protective memory response against a secondary infection? iii) what are the mechanisms responsible for the increased risk of reactivation in elderly individuals? To address these questions, we explored a computational model of the immune response to Mycobacterium tuberculosis including a mathematical description of immunosenescence and the generation and maintenance of immune memory. Sensitivity analysis techniques, together with extensive model characterization and in silico experiments, were applied to identify key mechanisms controlling TB reactivation and immunological memory. Key findings of this study are summarized by the following model predictions: i) increased strength and duration of memory protection is associated with higher levels of Tumor Necrosis Factor-[Formula: see text] (TNF) during primary infection; ii) production of TNF, but not of interferon-[Formula: see text], by memory T cells during secondary infection is a major determinant of effective protection; iii) impaired recruitment of CD4+ T cells may promote reactivation of latent TB infections in aging hosts. This study is a first attempt to consider the immune dynamics of a persistent infection throughout the lifetime of the host, taking into account immunosenescence and memory. While the model is TB specific, the results are applicable to other persistent bacterial infections and can aid in the development, evaluation and refinement of TB treatment and/or vaccine protocols.

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.

The pathologic hallmark of tuberculosis is the granuloma. A granuloma is a multifaceted cellular structure that serves to focus the host immune response, contain infection and pathology, and provide a niche for the bacillus to persist within the host. Granulomas form in response to Mycobacterium tuberculosis infection, and if a granuloma is capable of inhibiting or killing most of the M. tuberculosis present, humans develop a clinically latent infection. However, if a granuloma is impaired in function, infection progresses, granulomas enlarge, and bacteria seed new granulomas; this results in progressive pathology and disease, i.e., active tuberculosis. In clinical latency, immunologic perturbation at the level of the granuloma can result in reactivation of infection. In humans, there are a variety of granuloma types, even within the lungs of a single host.

In the last 12 years, different types of drugs that neutralize the proinflammatory molecule tumor necrosis factor alpha (TNFα or, simply, TNF) have been approved by the U.S. Food and Drug Administration (FDA) to treat patients with inflammatory diseases such as rheumatoid arthritis and Crohn’s disease [1–3]. Not surprisingly, interfering with TNF, an important cytokine that participates in many immune responses, could affect the immunogenesis of some infectious diseases and ultimately affect the susceptibility of these patients. The best example of an unfortunate side effect is the disease tuberculosis (TB). Following inhalation of Mycobacterium tuberculosis , the causative agent of TB, individuals either clear the infection, are able to control but not clear it (known as latent infection), or are unable to control infection and experience active primary disease. Those who have a latent infection have a 10% lifetime chance of reactivation to active disease, usually occurring

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

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., Author Summary 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.

The immune response to Mycobacterium tuberculosis infection (Mtb) is the formation of unique lesions, called granulomas. How well these granulomas form and function is a key issue that might explain why individuals experience different disease outcomes. The spatial structures of these granulomas are not well understood. In this paper, we use a metapopulation framework to develop a spatio-temporal model of the immune response to Mtb. Using this model, we are able to investigate the spatial organization of the immune response in the lungs to Mtb. We identify both host and pathogen factors that contribute to successful infection control. Additionally, we identify specific spatial interactions and mechanisms important for successful granuloma formation. These results can be further studied in the experimental setting.

Several molecules related to antigen presentation, including gamma interferon (IFN-γ) and the major histocompatibility complex (MHC), are encoded by polymorphic genes. Some polymorphisms were found to affect susceptibility to tuberculosis (TB) when they were considered singly in epidemiological studies, but how multiple polymorphisms interact to determine susceptibility to TB in an individual remains an open question. We hypothesized that polymorphisms in some genes may counteract or intensify the effects of polymorphisms in other genes. For example, an increase in IFN-γ expression may counteract the weak binding that a particular MHC variant displays for a peptide fromMycobacterium tuberculosisto establish the same T-cell response as another, more strongly binding MHC variant. To test this hypothesis, we developed a mathematical model of antigen presentation based on experimental data for the known effects of genetic polymorphisms and simulated time courses when multiple polymorphisms were present. We found that polymorphisms in different genes could affect antigen presentation to the same extent and therefore compensate for each other. Furthermore, we defined the conditions under which such relationships could exist. For example, increased IFN-γ expression compensated for decreased peptide-MHC affinity in the model only above a certain threshold of expression. Below this threshold, changes in IFN-γ expression were ineffectual compared to changes in peptide-MHC affinity. The finding that polymorphisms exhibit such relationships could explain discrepancies in the epidemiological literature, where some polymorphisms have been inconsistently associated with susceptibility to TB. Furthermore, the model allows polymorphisms to be ranked by effect, providing a new tool for designing association studies.

Summary: Lymphoid tissues are sites of soluble and cell-associated antigen sampling of peripheral tissues, and they are key compartments for the generation of cellular and humoral immune responses. Hilar lymph nodes (HiLNs), which drain the lungs, were examined to understand the effects of simian immunodeficiency virus (SIV) infection on this compartment of the immune system. Histologic and messenger RNA (mRNA) expression profiling approaches were used to determine the numbers, types, and distributions of SIV viral RNA + cells and to identify differentially expressed genes in HiLNs during SIVinfection. SIV RNA + cells were found to be primarily CD68 2 and localized to paracortical and medullary regions early in infection, whereas they resided mainly in paracortex during AIDS. As SIV infection progressed, CXCL9, CXCL10, interferon-g, and Toll-like receptor 3 levels all increased. In contrast, CCL19 increased early in infection but decreased during AIDS, whereas CCL21 decreased progressively throughout infection. Finally, local levels of cellular activation were increased throughout infection. Taken together, these findings indicate that SIV infection leads to an inflammatory environment in lung-draining lymph nodes that is characterized by type 1 cytokines and chemokines and likely has an impact on the nature and strength of immune responses to pulmonary pathogens.

Mycobacterium tuberculosis (Mtb) is an extraordinarily successful human pathogen, one of the major causes of death by infectious disease worldwide. A key issue for the study of tuberculosis is to understand why individuals infected with Mtb experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we coupled nonhuman primate experiments with a mathematical model we previously developed that qualitatively and quantitatively captures important processes of cellular priming and activation. These processes occur between the lung and the nearest draining lymph node where the key cells mediating this process are the dendritic cells (DC). The nonhuman primate experiments consist of bacteria and cell numbers from tissues of 17 adult cynomolgus macaques (Macaca fascicularis) that were infected with Mtb strain Erdman (∼25 CFU/animal via bronchoscope). The main result of this work is that delays in either DC migration to the draining lymph node or T cell trafficking to the site of infection can alter the outcome of Mtb infection, defining progression to primary disease or latent infection and reactivated tuberculosis. Our results also support the idea that the development of a new generation of treatment against Mtb should optimally elicit a fast DC turnover at the site of infection, as well as strong activation of DCs for maximal Ag presentation and production of key cytokines. This will induce the most protective T cell response.

We present a comprehensive mathematical model describing Helicobacter pylori interaction with the human gastric acid secretion system. We use the model to explore host and bacterial conditions that allow persistent infection to develop and be maintained. Our results show that upon colonization, there is a transient period (day 1-20 post-infection) prior to the establishment of persistence. During this period, changes to host gastric physiology occur including elevations in positive effectors of acid secretion (such as gastrin and histamine). This is promoted by reduced somatostatin levels, an inhibitor of acid release. We suggest that these changes comprise compensatory mechanisms aimed at restoring acid to pre-infection levels. We also show that ammonia produced by bacteria sufficiently buffers acid promoting bacteria survival and growth.

A key issue for the study of tuberculosis is to understand why individuals infected with Mycobacterium tuberculosis (Mtb) experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we have previously developed a temporal mathematical model that qualitatively and quantitatively characterizes the cellular and cytokine control network during infection. In this work we extend that model to a two compartmental model to capture the important processes of cellular activation and priming that occur between the lung and the nearest draining lymph node. We are able to reproduce typical disease progression scenarios including primary infection, latency or clearance. Then we use the model to predict key processes determining these different disease trajectories (i.e. identify bifurcation parameters), suggesting directions for further basic science study and potential new treatment strategies.

The extent to which highly active antiretroviral therapy can restore thymic function in HIV-1 infected patients is not known. We simulate treatment using a temporal model of thymopoiesis during HIV-1 infection, tracking thymic function by the number of recent thymic emigrants (RTE) exported to the periphery per day. Our results suggest that suppressing viral load in peripheral blood and improving inherent thymic function are necessary for the reconstitution of RTE levels in adult thymic infection with either R5 or X4 HIV-1 strains. This is also the case in pediatric thymic infection with R5 strains. However, recovery of RTE levels during pediatric infection with X4 strains also depends on high drug efficacy within the thymus. We further predict that protease inhibitors have high levels of efficacy directly suppressing viral replication within the thymus, while reverse transcriptase inhibitors have low efficacy.

The immune response to Mycobacterium tuberculosis (Mtb) infection is the for- mation of multicellular lesions, or granolomas, in the lung of the individual. However, the structure of the granulomas and the spatial distribution of the immune cells within is not well understood. In this paper we develop a mathematical model investigating the early and initial immune response to Mtb. The model consists of coupled reaction-diffusion-advection partial differen- tial equations governing the dynamics of the relevant macrophage and bacteria populations and a bacteria-produced chemokine. Our novel application of mathematical concepts of internal states and internal velocity allows us to begin to study this unique immunological structure. Volume changes resulting from proliferation and death terms generate a velocity field by which all cells are transported within the forming granuloma. We present numerical results for two distinct infection outcomes: controlled and uncontrolled granuloma growth. Using a simplified model we are able to analytically determine conditions under which the bacteria population decreases, representing early clearance of infection, or grows, represent- ing the initial stages of granuloma formation.

There is wide variation in endemic tuberculosis (TB) levels between countries and we seek to identify possible causes of these differences. In this study we present an epidemiological model of Mycobacterium tuberculosis infection to investigate the effects of host genetics and demographic factors on epidemic TB. We discuss the general framework for this approach and present analytical results to identify important parameters affecting steady-state prevalence and incidence rates of TB disease. We then use numerical simulations of our model to observe the effects of a genetically susceptible subpopulation on TB disease dynamics at the population level. Finally, we simulate infection within a genetically heterogeneous population in two demographic settings: India (a typical population with high TB prevalence) and the USA (a typical population with low TB prevalence). Results show that changes in transmission parameters, the fraction of the population genetically susceptible to infection, and demographic factors strongly affect TB prevalence and incidence rates.