Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection

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.

Mathematical models disentangle the role of IL-10 feedbacks in human monocytes upon proinflammatory activation

Inflammation is one of the vital mechanisms through which the immune system responds to harmful stimuli. During inflammation, proinflammatory and anti-inflammatory cytokines interplay to orchestrate fine-tuned and dynamic immune responses. The cytokine interplay governs switches in the inflammatory response and dictates the propagation and development of the inflammatory response. Molecular pathways underlying the interplay are complex, and time-resolved monitoring of mediators and cytokines is necessary as a basis to study them in detail. Our understanding can be advanced by mathematical models that enable to analyze the system of interactions and their dynamical interplay in detail. We, therefore, used a mathematical modeling approach to study the interplay between prominent proinflammatory and anti-inflammatory cytokines with a focus on tumor necrosis factor and interleukin 10 (IL-10) in lipopolysaccharide-primed primary human monocytes. Relevant time-resolved data were generated by experimentally adding or blocking IL-10 at different time points. The model was successfully trained and could predict independent validation data and was further used to perform simulations to disentangle the role of IL-10 feedbacks during an acute inflammatory event. We used the insight to obtain a reduced predictive model including only the necessary IL-10-mediated feedbacks. Finally, the validated reduced model was used to predict early IL-10-tumor necrosis factor switches in the inflammatory response. Overall, we gained detailed insights into fine-tuning of inflammatory responses in human monocytes and present a model for further use in studying the complex and dynamic process of cytokine-regulated acute inflammation.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

The effect of mesenchymal stem cell-derived supernatant nasal administration on lung inflammation and immune response in BCG-vaccinated BALB/c mice

Mesenchymal stem cells (MSCs) are among the known cells that can control and modulate immune responses in different circumstances, including autoimmune diseases. Also, various studies have shown that they can prevent and reduces the pulmonary inflammation caused by infectious agents. In the case of tuberculosis and inflammation caused by BCG, the granuloma has destructive effects and improper orientation of the immune response. Therefore, it is possible to prevent airway damage by preventing harmful inflammatory responses and guiding the immune system responses. This study investigates the role of nasal administration of MSCs supernatant by designing an inflammatory model in the BALB/c mice lung with BCG. MSCs are isolated from mice adipose tissue in this study and evaluated for their phenotypic and differentiation properties. After the third passage, these cells' condition medium (CM) was collected. 20 mice were divided into four groups. Group 1 receive BCG (10⁷ CFU in 5 ml volume for 15 min) nasal administration. Group 2 treated with CM, and group 3 initially were treated with CM (in 5 ml volume for 15 min) and, after 24 h, treated with BCG nasal administration. CM treatment was continued every five days for one month. The fourth group of mice was treated with PBS nasal administration of CM and BCG. One week after the last administration, the lung tissue of mice in each group was pathologically examined. In addition, secretion of IL1-β, IL-6, TNF-α, TGF-β, and IL-10 in the alveolar fluid and secretion of IL-4 and IFN-γ cytokines in the supernatant of splenocytes was evaluated by ELISA. The TNF-α/IL-10 ratio in the alveolar lung fluid of the BCG received group is 2/9 and decreased to 0.58 after successive CM treatment. Therefore, it can be concluded that inflammatory responses to BCG infection in the presence of CM are balanced and pave the way for the induction of effective immune responses by reducing lung tissue damage.

A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion

Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.

In vitro two-step granuloma formation model for testing innate immune response to implants and coatings

The extensive innate immune response to implanted biomaterials contributes significantly to their sub-par performance and failure. Granuloma formation is one of such reactions which results in multi-cell type clusters in line with the immune reaction to implanted materials. However, currently no in vitro model of granuloma formation exists that takes into account the arrival of multiple cell types (immune cells and connective tissue cells) to the implant insertion site. In this study, we developed a two-step model based on stimulated macrophage seeding followed by fibroblast introduction after a physiologically relevant time period for mimicking initial steps of immune reaction to biomaterials and inducing granuloma like behavior. Both LPS and TNF-α induction resulted in granuloma like formations which persisted longer than the control conditions. Introduction of human fibroblasts resulted in the colonization of the surfaces where the cell numbers and the collagen secretion were dependent on the microenvironment. In order to demonstrate the capacity of our model system to monitor the reaction to a given coating, a validated antimicrobial coating (Polyarginine (PAR)/Hyaluronic acid (HA)) was used as a testing bed. The coating prevented the adhesion of macrophages while allowing the adhesion of the fibroblast at the time of their arrival. Similar to its antimicrobial activity, macrophage metabolic activity and M2 differentiation in the presence of PAR was dependent to its chain length. The incorporation of fibroblasts resulted in decreased TNF-α and increased IL-1RA secretion especially in stimulation conditions. The pro- and anti-inflammatory cytokine secretions were low for PAR/HA coatings in line with the decreased number of macrophage presence. In the presence of complex PBMC population, the coating resulted in slightly less cellular attachment, without any significant cytokine secretion; the absence of inflammatory reaction was also demonstrated in vivo in a mouse model. The described in vitro granuloma testing system can control the macrophage reaction as a function of stimulation. It can also be used for testing new biomaterials for the potential innate immune responses and also for validation of implant coatings beyond their primary function from the immune response point of view.

A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues

Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.

Neutrophil Dynamics Affect Mycobacterium tuberculosis Granuloma Outcomes and Dissemination

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.

QSPcc reduces bottlenecks in computational model simulations

Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.

Lombardo and colleagues present QSPcc, a computational code compiler designed to convert code from popular scientific programming languages, such as MATLAB or R, into fast-running C code. This reduces the computational load required for complex modelling approaches and reduces user investment learning additional complex languages.

To Sobol or not to Sobol? The effects of sampling schemes in systems biology applications

Computational and mathematical models in biology rely heavily on the parameters that characterize them. However, robust estimates for their values are typically elusive and thus a large parameter space becomes necessary for model study, particularly to make translationally impactful predictions. Sampling schemes exploring parameter spaces for models are used for a variety of purposes in systems biology, including model calibration and sensitivity analysis. Typically, random sampling is used; however, when models have a high number of unknown parameters or the models are highly complex, computational cost becomes an important factor. This issue can be reduced through the use of efficient sampling schemes such as Latin hypercube sampling (LHS) and Sobol sequences. In this work, we compare and contrast three sampling schemes - random sampling, LHS, and Sobol sequences - for the purposes of performing both parameter sensitivity analysis and model calibration. In addition, we apply these analyses to different types of computational and mathematical models of varying complexity: a simple ODE model, a complex ODE model, and an agent-based model. In general, the sampling scheme had little effect when used for calibration efforts, but when applied to sensitivity analyses, Sobol sequences exhibited faster convergence. While the observed benefit to convergence is relatively small, Sobol sequences are computationally less expensive to compute than LHS samples and also have the benefit of being deterministic, which allows for better reproducibility of results.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

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.

Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle

•

In silico model of TB in the lung incorporating environmental heterogeneity.

•

Preferential conditions at the apex of lung localise post-primary disease there.

•

Analysis of the key influences driving disease at different regions of the lung.

Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease.

Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors. The alveolar tissue in the lung is divided into distinct patches, with each patch representing a portion of the total tissue and containing environmental attributes that reflect the internal conditions at that location. We include populations of bacteria and immune cells in various states, and events are included which determine how the members of the model interact with each other and the environment. By allowing some of these events to be dependent on environmental attributes, we create a set of heterogeneous dynamics, whereby the location of the tissue within the lung determines the disease pathological events that occur there.

Our results show that the environmental heterogeneity within the lung is a plausible driving force behind the apical localisation during post-primary disease. After initial infection, bacterial levels will grow in the initial infection location at the base of the lung until an adaptive immune response is initiated. During this period, bacteria are able to disseminate and create new lesions throughout the lung. During the latent stage, the lesions that are situated towards the apex are the largest in size, and once a post-primary immune-suppressing event occurs, it is the uppermost lesions that reach the highest levels of bacterial proliferation. Our sensitivity analysis also shows that it is the differential in blood perfusion, causing reduced immune activity towards the apex, which has the biggest influence of disease outputs.

CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models

Introduction

Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.

Methods

Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.

Results

We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.

Conclusions

We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Integrating mathematical models with experimental data to investigate the within-host dynamics of bacterial infections

Bacterial infections still constitute a major cause of mortality and morbidity worldwide. The unavailability of therapeutics, antimicrobial resistance and the chronicity of infections due to incomplete clearance contribute to this phenomenon. Despite the progress in antimicrobial and vaccine development, knowledge about the effect that therapeutics have on the host–bacteria interactions remains incomplete. Insights into the characteristics of bacterial colonization and migration between tissues and the relationship between replication and host- or therapeutically induced killing can enable efficient design of treatment approaches. Recently, innovative experimental techniques have generated data enabling the qualitative characterization of aspects of bacterial dynamics. Here, we argue that mathematical modeling as an adjunct to experimental data can enrich the biological insight that these data provide. However, due to limited interdisciplinary training, efforts to combine the two remain limited. To promote this dialogue, we provide a categorization of modeling approaches highlighting their relationship to data generated by a range of experimental techniques in the area of in vivo bacterial dynamics. We outline common biological themes explored using mathematical models with case studies across all pathogen classes. Finally, this review advocates multidisciplinary integration to improve our mechanistic understanding of bacterial infections and guide the use of existing or new therapies.

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

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.

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.

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies

Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines.

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

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

Metabolic Programming of Macrophages: Implications in the Pathogenesis of Granulomatous Disease

Metabolic reprogramming is rapidly gaining appreciation in the etiology of immune cell dysfunction in a variety of diseases. Tuberculosis, schistosomiasis, and sarcoidosis represent an important class of diseases characterized by the formation of granulomas, where macrophages are causatively implicated in disease pathogenesis. Recent studies support the incidence of macrophage metabolic reprogramming in granulomas of both infectious and non-infectious origin. These publications identify the mechanistic target of rapamycin (mTOR), as well as the major regulators of lipid metabolism and cellular energy balance, peroxisome proliferator receptor gamma (PPAR-γ) and adenosine monophosphate-activated protein kinase (AMPK), respectively, as key players in the pathological progression of granulomas. In this review, we present a comprehensive breakdown of emerging research on the link between macrophage cell metabolism and granulomas of different etiology, and how parallels can be drawn between different forms of granulomatous disease. In particular, we discuss the role of PPAR-γ signaling and lipid metabolism, which are currently the best-represented metabolic pathways in this context, and we highlight dysregulated lipid metabolism as a common denominator in granulomatous disease progression. This review therefore aims to highlight metabolic mechanisms of granuloma immune cell fate and open up research questions for the identification of potential therapeutic targets in the future.

Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of “universal” drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone.

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of “universal” drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone. In the absence of clinical data comparing their side-by-side efficacies in controlled MDR-TB trials, a pharmacological rationale is needed to guide the selection of the most efficacious fluoroquinolone. The present studies were designed to test the hypothesis that fluoroquinolone concentrations (pharmacokinetics) and activity (pharmacodynamics) at the site of infection are better predictors of efficacy than the plasma concentrations and potency measured in standard growth inhibition assays and are better suited to determinations of whether one of the fluoroquinolones outperforms the others in rabbits with active TB. We first measured the penetration of these fluoroquinolones in lung lesion compartments, and their potency against bacterial populations that reside in each compartment, to compute lesion-centric pharmacokinetic-pharmacodynamic (PK/PD) parameters. PK modeling methods were used to quantify drug penetration from plasma to tissues at human-equivalent doses. On the basis of these metrics, moxifloxacin emerged with a clear advantage, whereas plasma-based PK/PD favored levofloxacin (the ranges of the plasma AUC/MIC ratio [i.e., the area under the concentration-time curve over 24 h in the steady state divided by the MIC] are 46 to 86 for moxifloxacin and 74 to 258 for levofloxacin). A comparative efficacy trial in the rabbit model of active TB demonstrated the superiority of moxifloxacin in reducing bacterial burden at the lesion level and in sterilizing cellular and necrotic lesions. Collectively, these results show that PK/PD data obtained at the site of infection represent an adequate predictor of drug efficacy against TB and constitute the baseline required to explore synergies, antagonism, and drug-drug interactions in fluoroquinolone-containing regimens.

Review of the systems biology of the immune system using agent-based models

The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.

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.

Host Directed Therapy for Chronic Tuberculosis via Intrapulmonary Delivery of Aerosolized Peptide Inhibitors Targeting the IL-10-STAT3 Pathway

Here we demonstrate that aerosols of host directed therapies [HDT] administered during a chronic Mycobacterium tuberculosis (Mtb) infection have bactericidal effect. The pulmonary bacterial load of C57BL/6 mice chronically infected with Mtb was reduced by 1.7 and 0.6 log₁₀CFU after two weeks of treatment via aerosol delivery with ST3-H2A2, [a selective peptide inhibitor of the STAT3 N-terminal domain] or IL10R1-7 [selective peptide inhibitor for the IL-10Ra] respectively and when compared to control mice treated with IL10R1-14 [peptide inhibitor used as negative control] or untreated mice infected with Mtb. Accordingly, when compared to control mice, the bactericidal capacity in mice was enhanced upon treatment with peptide inhibitors ST3-H2A2 and IL10R1-7 as evidenced by higher pulmonary activities of nitric oxide synthase, NADPH oxidase and lysozyme enzymes and decreased arginase enzyme activity. This therapy also modulated important checkpoints [Bcl2, Beclin-1, Atg 5, bax] in the apoptosis-autophagy pathways. Thus, even in the absence of antibiotics, targeting of the host pulmonary IL-10-STAT3 pathway can significantly reduce the Mtb bacilli load in the lungs, modulate the host own bactericidal capacity and apoptosis and autophagy pathways. Our approach here also allows targeting checkpoints of the lungs to determine their specific contribution in pulmonary immunity or pathogenesis.

PPE11 of Mycobacterium tuberculosis can alter host inflammatory response and trigger cell death

Tuberculosis (TB), which is caused by Mycobacterium tuberculosis (Mtb), remains a serious global health problem. The PE/PPE family, featuring unique sequences, structures and expression in Mtb, is reported to interfere with the macrophage response to the pathogen and facilitate its infection. PPE11 (Rv0453) existed in pathogenic mycobacteria and was persistently expressed in the infected guinea pig lungs. However, the role it played in the pathogenesis remains unclear. Here, to investigate the interaction and potential mechanism of PPE11 between pathogens and hosts, we heterologously expressed PPE11 in non-pathogenic, rapidly growing Mycobacterium smegmatis strains. We found that the overexpression of the cell wall-associated protein, PPE11, can improve the viability of bacteria in the presence of lysozyme, hydrogen peroxide and acid stress. Expression of PPE11 enhanced the early survival of M. smegmatis in macrophages and sustained a higher bacterial load in mouse tissues that showed exacerbated organ pathology. Macrophages infected with recombinant M. smegmatis produced significantly greater amounts of interleukin (IL)-1β, IL-6, tumour necrosis factor (TNF)-α and an early decrease in IL-10 along with higher levels of host cell death. Similar cytokines changes were observed in the sera of infected mice. Accordingly, PPE11 protein causes histopathological changes by disrupting the dynamic balance of the inflammatory factors and promoting host-cell death, indicating a potential role in the virulence of Mtb.

A review of inflammatory mechanism in airway diseases

BACKGROUND

Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF).

FINDINGS

In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-β (TGF-β), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases.

CONCLUSION

The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.

The Synergistic Effects of the Glutathione Precursor, NAC and First-Line Antibiotics in the Granulomatous Response Against Mycobacterium tuberculosis

Mycobacterium tuberculosis (M. tb), the causative bacterial agent responsible for tuberculosis (TB) continues to afflict millions of people worldwide. Although the human immune system plays a critical role in containing M. tb infection, elimination proves immensely more challenging. Consequently, there has been a worldwide effort to eradicate, and limit the spread of M. tb through the conventional use of first-line antibiotics. Unfortunately, with the emergence of drug resistant and multi-drug resistant strains of M. tb the archetypical antibiotics no longer provide the same ascendancy as they once did. Furthermore, when administered, these first-line antibiotics commonly present severe complications and side effects. The biological antioxidant glutathione (GSH) however, has been demonstrated to have a profound mycobactericidal effect with no reported adverse consequences. Therefore, we examined if N-Acetyl Cysteine (NAC), the molecular precursor to GSH, when supplemented in combination with suboptimal levels of standalone first-line antibiotics would be sufficient to completely clear M. tb infection within in vitro derived granulomas from healthy subjects and individuals with type 2 diabetes (T2DM). Our results revealed that by virtue of immune modulation, the addition of NAC to subprime levels of isoniazid (INH) and rifampicin (RIF) was indeed capable of inducing complete clearance of M. tb among healthy individuals.

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

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

A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection

Tuberculosis (TB) is an ancient disease that, although curable, still accounts for over 1 million deaths worldwide. Shortening treatment time is an important area of research but is hampered by the lack of models that mimic the full range of human pathology. TB shows distinct localisations during different stages of infection, the reasons for which are poorly understood. Greater understanding of how heterogeneity within the human lung influences disease progression may hold the key to improving treatment efficiency and reducing treatment times. In this work, we present a novel in silico software model which uses a networked metapopulation incorporating both spatial heterogeneity and dissemination possibilities to simulate a TB infection over the whole lung and associated lymphatics. The entire population of bacteria and immune cells is split into a network of patches: members interact within patches and are able to move between them. Patches and edges of the lung network include their own environmental attributes which influence the dynamics of interactions between the members of the subpopulations of the patches and the translocation of members along edges. In this work, we detail the initial findings of a whole-organ model that incorporates distinct spatial heterogeneity features which are not present in standard differential equation approaches to tuberculosis modelling. We show that the inclusion of heterogeneity within the lung landscape when modelling TB disease progression has significant outcomes on the bacterial load present: a greater differential of oxygen, perfusion and ventilation between the apices and the basal regions of the lungs creates micro-environments at the apex that are more preferential for bacteria, due to increased oxygen availability and reduced immune activity, leading to a greater overall bacterial load present once latency is established. These findings suggest that further whole-organ modelling incorporating more sophisticated heterogeneities within the environment and complex lung topologies will provide more insight into the environments in which TB bacteria persist and thus help develop new treatments which are factored towards these environmental conditions.

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.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

Interleukin 10 mutant zebrafish have an enhanced interferon gamma response and improved survival against a Mycobacterium marinum infection

Tuberculosis ranks as one of the world’s deadliest infectious diseases causing more than a million casualties annually. IL10 inhibits the function of Th1 type cells, and IL10 deficiency has been associated with an improved resistance against Mycobacterium tuberculosis infection in a mouse model. Here, we utilized M. marinum infection in the zebrafish (Danio rerio) as a model for studying Il10 in the host response against mycobacteria. Unchallenged, nonsense il10^e46/e46 mutant zebrafish were fertile and phenotypically normal. Following a chronic mycobacterial infection, il10^e46/e46 mutants showed enhanced survival compared to the controls. This was associated with an increased expression of the Th cell marker cd4-1 and a shift towards a Th1 type immune response, which was demonstrated by the upregulated expression of tbx21 and ifng1, as well as the down-regulation of gata3. In addition, at 8 weeks post infection il10^e46/e46 mutant zebrafish had reduced expression levels of proinflammatory cytokines tnfb and il1b, presumably indicating slower progress of the infection. Altogether, our data show that Il10 can weaken the immune defense against M. marinum infection in zebrafish by restricting ifng1 response. Importantly, our findings support the relevance of M. marinum infection in zebrafish as a model for tuberculosis.

Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model

If improvements are to be made in tuberculosis (TB) treatment, an increased understanding of disease in the lung is needed. Studies have shown that bacteria in a less metabolically active state, associated with the presence of lipid bodies, are less susceptible to antibiotics, and recent results have highlighted the disparity in concentration of different compounds into lesions. Treatment success therefore depends critically on the responses of the individual bacteria that constitute the infection. We propose a hybrid, individual-based approach that analyses spatio-temporal dynamics at the cellular level, linking the behaviour of individual bacteria and host cells with the macroscopic behaviour of the microenvironment. The individual elements (bacteria, macrophages and T cells) are modelled using cellular automaton (CA) rules, and the evolution of oxygen, drugs and chemokine dynamics are incorporated in order to study the effects of the microenvironment in the pathological lesion. We allow bacteria to switch states depending on oxygen concentration, which affects how they respond to treatment. This is the first multiscale model of its type to consider both oxygen-driven phenotypic switching of the Mycobacterium tuberculosis and antibiotic treatment. Using this model, we investigate the role of bacterial cell state and of initial bacterial location on treatment outcome. We demonstrate that when bacteria are located further away from blood vessels, less favourable outcomes are more likely, i.e. longer time before infection is contained/cleared, treatment failure or later relapse. We also show that in cases where bacteria remain at the end of simulations, the organisms tend to be slower-growing and are often located within granulomas, surrounded by caseous material.

Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By “multi-level” we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization.

Immunological recovery in patients with pulmonary tuberculosis after intensive phase treatment

Objectives

This study aimed to examine the change and significance of immune parameters in patients with sputum smear-positive pulmonary tuberculosis (TB) after 2 months of intensive phase anti-TB treatment.

Methods

The immune parameters of 232 cases of sputum smear-positive pulmonary TB were detected before and after 2 months of intensive phase anti-TB treatment and compared with 50 cases from healthy volunteers (controls). The T lymphocyte cell population in peripheral blood was detected using flow cytometry. Serum levels of interleukin (IL)-1β, soluble interleukin-2 receptor, IL-6, and tumour necrosis factor-α were measured by ELISA.

Results

After 2 months of intensive phase anti-TB treatment, a reduction in the percentage of CD4+ T cells showed a significant restoration similar to that of controls. Moreover, after intensive anti-TB treatment, serum levels of IL-1β, soluble interleukin-2 receptor, IL-6, and tumour necrosis factor-α were significantly decreased compared with before treatment. Additionally, serum levels of IL-1β and IL-6 showed a diminished recovery compared with controls.

Conclusions

Our findings suggest immunological recovery in patients with pulmonary TB after intensive phase treatment. Therefore, serum cytokine levels are considered potential host biomarkers for monitoring the response of treatment for pulmonary TB.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.

Immune response patterns in non-communicable inflammatory skin diseases

Non-communicable inflammatory skin diseases (ncISD) such as psoriasis or atopic eczema are a major cause of global disease burden. Due to their impact and complexity, ncISD represent a major challenge of modern medicine. Dermatology textbooks describe more than 100 different ncISD based on clinical phenotype and histological architecture. In the last decades, this historical description was complemented by increasing molecular knowledge - and this knowledge is now being translated into specific therapeutics. Combining the enormous advances made in lymphocyte immunology and molecular genetics with clinical and histological phenotyping reveals six immune response patterns of the skin - type I immune cells cause the lichenoid pattern characterized by immune-mediated cell death of keratinocytes; type II immune cells underlie the eczematous pattern with impaired epidermal barrier, infection and eosinophils as well as the bullous pattern with loss of epithelial integrity; Th17 cells and ILC3 mediate the psoriatic pattern characterized by acanthosis, high metabolic activity and neutrophils; dysbalance of regulatory T cells causes either the fibrogenic pattern with rarefication of cells and dermal thickening or the granulomatous pattern defined by formation of granulomas. With more and more specific therapeutic agents approved, classifying ncISD also according to their immune response pattern will become highly relevant. This review defines the six immune response patterns of ncISD and highlights therapeutic strategies targeting key lymphocyte mediators.

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.

A complete categorization of multiscale models of infectious disease systems

Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.

Mathematical Modeling of Tuberculosis Granuloma Activation

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

Restoring Cytokine Balance in HIV-Positive Individuals with Low CD4 T Cell Counts

HIV infects and destroys CD4+ T cells leading to a compromised immune system. In a double-blinded study, a group of HIV-infected individuals with CD4+ T cell counts below 350 cells/mm³ were given either an empty liposomal supplement or a liposomal glutathione (L-GSH) supplement to take over a 3-month period. Baseline measurements in HIV-positive subjects show a significant decrease in levels of interleukin (IL)-12, IL-2, and interferon (IFN)-γ, along with a substantial increase in the levels of IL-6, IL-10, transforming growth factor (TGF)-β, and free radicals, compared to healthy individuals. Supplementation of HIV-positive subjects with L-GSH for 3 months resulted in a notable increase in the levels of IL-12, IL-2, and IFN-γ, with a concomitant decrease in the levels of IL-6, IL-10, and free radicals, and stabilization in the levels of TGF-β, IL-1, and IL-17, compared to their placebo counterparts. Levels of free radicals in CD4+ T cells stabilized, while GSH levels increased in the treatment group. Those in the placebo group showed no significant difference throughout the study. In summary, supplementation with L-GSH in HIV-infected individuals with CD4+ T cell counts below 350 cells/mm³ can help restore redox homeostasis and cytokine balance, therefore aiding the immune system to control opportunistic infections.

Applying optimization algorithms to tuberculosis antibiotic treatment regimens

Introduction

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

Methods

We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments.

Results

We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens.

Conclusions

Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.

Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach

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.

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.

Heterogeneity in tuberculosis

Infection with Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), results in a range of clinical presentations in humans. Most infections manifest as a clinically asymptomatic, contained state that is termed latent TB infection (LTBI); a smaller subset of infected individuals present with symptomatic, active TB. Within these two seemingly binary states, there is a spectrum of host outcomes that have varying symptoms, microbiologies, immune responses and pathologies. Recently, it has become apparent that there is diversity of infection even within a single individual. A good understanding of the heterogeneity that is intrinsic to TB - at both the population level and the individual level - is crucial to inform the development of intervention strategies that account for and target the unique, complex and independent nature of the local host-pathogen interactions that occur in this infection. In this Review, we draw on model systems and human data to discuss multiple facets of TB biology and their relationship to the overall heterogeneity observed in the human disease.

Striking the right immunological balance prevents progression of tuberculosis

INTRODUCTION

Tuberculosis (TB) caused by infection with Mycobacterium tuberculosis (Mtb) is a major burden for human health worldwide. Current standard treatments for TB require prolonged administration of antimycobacterial drugs leading to exaggerated inflammation and tissue damage. This can result in the reactivation of latent TB culminating in TB progression. Thus, there is an unmet need to develop therapies that would shorten the duration of anti-TB treatment and to induce optimal protective immune responses to control the spread of mycobacterial infection with minimal lung pathology.

FINDINGS

Granulomata is the hallmark structure formed by the organized accumulation of immune cells including macrophages, natural killer cells, dendritic cells, neutrophils, T cells, and B cells to the site of Mtb infection. It safeguards the host by containing Mtb in latent form. However, granulomata can undergo caseation and contribute to the reactivation of latent TB, if the immune responses developed to fight mycobacterial infection are not properly controlled. Thus, an optimal balance between innate and adaptive immune cells might play a vital role in containing mycobacteria in latent form for prolonged periods and prevent the spread of Mtb infection from one individual to another.

CONCLUSION

Optimal and well-regulated immune responses against Mycobacterium tuberculosis may help to prevent the reactivation of latent TB. Moreover, therapies targeting balanced immune responses could help to improve treatment outcomes among latently infected TB patients and thereby limit the dissemination of mycobacterial infection.

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.