Synergy between individual TNF-dependent functions determines granuloma performance for controlling Mycobacterium tuberculosis infection

A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach

Tuberculosis (TB) is a life-threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB-related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well-studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

TNF in Human Tuberculosis: A Double-Edged Sword

TNF, a pleiotropic proinflammatory cytokine, is important for protective immunity and immunopathology during Mycobacterium tuberculosis (Mtb) infection, which causes tuberculosis (TB) in humans. TNF is produced primarily by phagocytes in the lungs during the early stages of Mtb infection and performs diverse physiological and pathological functions by binding to its receptors in a context-dependent manner. TNF is essential for granuloma formation, chronic infection prevention, and macrophage recruitment to and activation at the site of infection. In animal models, TNF, in cooperation with chemokines, contributes to the initiation, maintenance, and clearance of mycobacteria in granulomas. Although anti-TNF therapy is effective against immune diseases such as rheumatoid arthritis, it carries the risk of reactivating TB. Furthermore, TNF-associated inflammation contributes to cachexia in patients with TB. This review focuses on the multifaceted role of TNF in the pathogenesis and prevention of TB and underscores the importance of investigating the functions of TNF and its receptors in the establishment of protective immunity against and in the pathology of TB. Such investigations will facilitate the development of therapeutic strategies that target TNF signaling, which makes beneficial and detrimental contributions to the pathogenesis of TB.

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.

Cytokine Responses during Mycobacterium tuberculosis H37Rv and Ascaris lumbricoides Costimulation Using Human THP-1 and Jurkat Cells, and a Pilot Human Tuberculosis and Helminth Coinfection Study

BACKGROUND

Helminth infections are widespread in tuberculosis-endemic areas and are associated with an increased risk of active tuberculosis. In contrast to the pro-inflammatory Th1 responses elicited by Mycobacterium tuberculosis (Mtb) infection, helminth infections induce anti-inflammatory Th2/Treg responses. A robust Th2 response has been linked to reduced tuberculosis protection. Several studies show the effect of helminth infection on BCG vaccination and TB, but the mechanisms remain unclear.

AIM

To determine the cytokine response profiles during tuberculosis and intestinal helminth coinfection.

METHODS

For the in vitro study, lymphocytic Jurkat and monocytic THP-1 cell lines were stimulated with Mtb H37Rv and Ascaris lumbricoides (A. lumbricoides) excretory-secretory protein extracts for 24 and 48 h. The pilot human ex vivo study consisted of participants infected with Mtb, helminths, or coinfected with both Mtb and helminths. Thereafter, the gene transcription levels of IFN-γ, TNF-α, granzyme B, perforin, IL-2, IL-17, NFATC2, Eomesodermin, IL-4, IL-5, IL-10, TGF-β and FoxP3 in the unstimulated/uninfected controls, singly stimulated/infected and costimulated/coinfected groups were determined using RT-qPCR.

RESULTS

TB-stimulated Jurkat cells had significantly higher levels of IFN-γ, TNF-α, granzyme B, and perforin compared to unstimulated controls, LPS- and A. lumbricoides-stimulated cells, and A. lumbricoides plus TB-costimulated cells (p < 0.0001). IL-2, IL-17, Eomes, and NFATC2 levels were also higher in TB-stimulated Jurkat cells (p < 0.0001). Jurkat and THP-1 cells singly stimulated with TB had lower IL-5 and IL-4 levels compared to those singly stimulated with A. lumbricoides and those costimulated with TB plus A. lumbricoides (p < 0.0001). A. lumbricoides-singly stimulated cells had higher IL-4 levels compared to TB plus A. lumbricoides-costimulated Jurkat and THP-1 cells (p < 0.0001). TGF-β levels were also lower in TB-singly stimulated cells compared to TB plus A. lumbricoides-costimulated cells (p < 0.0001). IL-10 levels were lower in TB-stimulated Jurkat and THP-1 cells compared to TB plus A. lumbricoides-costimulated cells (p < 0.0001). Similar results were noted for the human ex vivo study, albeit with a smaller sample size.

CONCLUSIONS

Data suggest that helminths induce a predominant Th2/Treg response which may downregulate critical Th1 responses that are crucial for tuberculosis protection.

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.

Secondary immunodeficiencies and infectious considerations of biologic immunomodulatory therapies

Biologic immunomodulatory medications have rapidly expanded in the previous decades, providing new treatment options for individuals with a spectrum of oncologic, allergic, rheumatologic, and neurologic conditions. Biologic therapies alter immune function and can impair key host defense mechanisms, resulting in secondary immunodeficiency and increased infectious risks. Biologic medications can increase general risk for upper respiratory tract infections but can also be associated with unique infectious risks owing to distinct mechanisms of action. With the widespread use of these medications, providers in every area of medicine will likely care for individuals receiving biologic therapies and understanding their potential infectious complications can help mitigate these risks. This practical review discusses the infectious implications of biologics by class of medication and provides recommendations regarding the examination and screening both before therapy initiation and while the patient is receiving the medication. With this knowledge and background, providers can reduce risk whereas patients receive the treatment benefits of these biologic medications.

Protective effect of astaxanthin on tuberculosis-associated inflammatory lung injury

Mycobacterium tuberculosis (MTB) invades the lungs and is the key cause of tuberculosis (TB). MTB induces immune overreaction and inflammatory damage to lung tissue. There is a lack of protective drugs against pulmonary inflammatory damage. Herein, the protective roles and mechanisms of Astaxanthin (ASTA), a natural compound, in inflammatory injured lung epithelial cells were investigated. Lipopolysaccharide (LPS) was used to establish inflammatory injury model in the murine lung epithelial (MLE)-12 cells. Cell counting kit-8 was used for screening of compound concentrations. Cell proliferation was observed real-time with a high content analysis system. Flow cytometry assessed apoptosis. The changes of apoptotic proteins and key proteins in nuclear factor kappa-B (NF-κB) pathway were measured with the western blot. LPS was used to establish an animal model of pulmonary injury. The pathological changes and degree of inflammatory injury in lung tissue were observed with hematoxylin and eosin (HE) staining. The levels of inflammatory mediators were detected with enzyme-linked immunosorbent assay. The results showed that ASTA reduced lung inflammation and attenuated inflammatory damage in lung tissues. ASTA reduced apoptosis stimulated by LPS through suppressing the NF-κB pathway in MLE-12 cells. We believe that ASTA may have great potential for protection against inflammatory damage to lung tissue.

Engineering multicellular living systems-a Keystone Symposia report

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

Multiplexed microfluidic chip for cell co-culture

Paracrine signaling is challenging to study in vitro, as conventional culture tools dilute soluble factors and offer little to no spatiotemporal control over signaling. Microfluidic chips offer potential to address both of these issues. However, few solutions offer both control over onset and duration of cell-cell communication, and high throughput. We have developed a microfluidic chip designed to culture cells in adjacent chambers, separated by valves to selectively allow or prevent exchange of paracrine signals. The chip features 16 fluidic inputs and 128 individually-addressable chambers arranged in 32 sets of 4 chambers. Media can be continuously perfused or delivered by diffusion, which we model under different culture conditions to ensure normal cell viability. Immunocytochemistry assays can be performed in the chip, which we modeled and fine-tuned to reduce total assay time to 1 h. Finally, we validate the use of the chip for co-culture studies by showing that HEK293Ta cells respond to signals secreted by RAW 264.7 immune cells in adjacent chambers, only when the valve between the chambers is opened.

Vaccine-Induced Subcutaneous Granulomas in Goats Reflect Differences in Host–Mycobacterium Interactions between BCG- and Recombinant BCG-Derivative Vaccines

Tuberculous granulomas are highly dynamic structures reflecting the complex host–mycobacterium interactions. The objective of this study was to compare granuloma development at the site of vaccination with BCG and its recombinant derivatives in goats. To characterize the host response, epithelioid cells, multinucleated giant cells (MNGC), T cell subsets, B cells, plasma cells, dendritic cells and mycobacterial antigen were labelled by immunohistochemistry, and lipids and acid-fast bacteria (AFB) were labelled by specific staining. Granulomas with central caseous necrosis developed at the injection site of most goats though lesion size and extent of necrosis differed between vaccine strains. CD4⁺ T and B cells were more scarce and CD8⁺ cells were more numerous in granulomas induced by recombinant derivatives compared to their parental BCG strain. Further, the numbers of MNGCs and cells with lipid bodies were markedly lower in groups administered with recombinant BCG strains. Microscopic detection of AFB and mycobacterial antigen was rather frequent in the area of central necrosis, however, the isolation of bacteria in culture was rarely successful. In summary, BCG and its recombinant derivatives induced reproducibly subcutaneous caseous granulomas in goats that can be easily monitored and surgically removed for further studies. The granulomas reflected the genetic modifications of the recombinant BCG-derivatives and are therefore suitable models to compare reactions to different mycobacteria or TB vaccines.

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.

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.

Interferon-γ (+874 T/A) and interleukin-10 (-1082 G/A) genes polymorphisms are associated with active tuberculosis in the Algerian population of Oran's city

BACKGROUND AND AIMS

Polymorphisms within genes encoding the cytokines involved in anti-tuberculosis immunity have been widely studied and sometimes associated with an increased risk of developing the active form of tuberculosis (TB). This study analyzes for the first time the impact of two polymorphisms, namely IFNG+874 T/A and IL10-1082 G/A, in the Algerian population where tuberculosis is moderately endemic.

METHODS

This case-control study included 104 healthy controls and 141 active TB patients: 75 extrapulmonary (EPTB) and 66 pulmonary (PTB). They were all genotyped by refractory mutational system-PCR amplification. In order to measure the functional impact of IFNG+874 T/A on the production rate of IFN-γ, 43 patients performed a QuantiFERON®Gold In-tube test.

RESULTS

The IFNG+874 AA genotype was associated with a higher risk of developing EPTB (OR = 2.52; 95%CI = 1.23-5.18; p = 0.012) while the IFNG+874 TA genotype was associated with a greater protection (OR = 0.34, 95%CI = 0.16-0.74; p = 0.006) which was further characterized by a high production of IFN-γ (p = 0.001). Similarly, the allele A of SNP IL10-1082 G/A, especially in its homozygous form (AA), were overrepresented in PTB patients (p = 0.010 and 0.019, respectively). The combination of both susceptibility genotypes (AA/AA) was strongly associated with risk of development of active TB (OR = 8.58; 95% C.I = 1.95-37.70, p = 0.004). This susceptibility combination was only significant in men regarding PTB (OR = 11.05; 95% C.I = 1.32-92.72, p = 0.027). Additionally, IFNG+874 TA and IL10-1082G∗ genotypes combination was mostly encountered in men controls and conferred the highest protection rate against EPTB (OR = 0.25; 95% C.I = 0.08-0.76, p = 0.015).

CONCLUSION

These two cytokines genes polymorphisms are associated with active TB susceptibility in the Algerian population. They act synergistically in terms of protection and susceptibility regarding the two forms of the disease. Moreover, these associations were more marked among males suggesting a potential role of gender.

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.

An investigation of tuberculosis progression revealing the role of macrophages apoptosis via sensitivity and bifurcation analysis

Mycobacterium tuberculosis infection features various disease outcomes: clearance, latency, active disease, and latent tuberculosis infection (LTBI) reactivation. Identifying the decisive factors for disease outcomes and progression is crucial to elucidate the macrophages-tuberculosis interaction and provide insights into therapeutic strategies. To achieve this goal, we first model the disease progression as a dynamical shift among different disease outcomes, which are characterized by various steady states of bacterial concentration. The causal mechanisms of steady-state transitions can be the occurrence of transcritical and saddle-node bifurcations, which are induced by slowly changing parameters. Transcritical bifurcation, occurring when the basic reproduction number equals to one, determines whether the infection clears or spreads. Saddle-node bifurcation is the key mechanism to create and destroy steady states. Based on these two steady-state transition mechanisms, we carry out two sample-based sensitivity analyses on transcritical bifurcation conditions and saddle-node bifurcation conditions. The sensitivity analysis results suggest that the macrophage apoptosis rate is the most significant factor affecting the transition in disease outcomes. This result agrees with the discovery that the programmed cell death (apoptosis) plays a unique role in the complex microorganism-host interplay. Sensitivity analysis narrows down the parameters of interest, but cannot answer how these parameters influence the model outcomes. To do this, we employ bifurcation analysis and numerical simulation to unfold various disease outcomes induced by the variation of macrophage apoptosis rate. Our findings support the hypothesis that the regulation mechanism of macrophage apoptosis affects the host immunity against tuberculosis infection and tuberculosis virulence. Moreover, our mathematical results suggest that new treatments and/or vaccines that regulate macrophage apoptosis in combination with weakening bacillary viability and/or promoting adaptive immunity could have therapeutic value.

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

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.

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.

Role of secukinumab in ankylosing spondylitis with tubercular uveitis

We present the case of a 32-year-old Indian male one-eyed individual with a history of unilateral panuveitis with HLA B 27 positive spondyloarthropathy on systemic immunosuppressant (Adalimumab). He developed recurrent inflammation in the same eye in a span of 2 years, later complicated with retinal vasculitis. On evaluation, he was diagnosed with tubercular uveitis and started on antitubercular treatment along with systemic steroids. Inview of Increased IOP due to steroid response, Inj. Secukinumab ( IL 17 A inhibitor) was started and significant improvement was noted.

IL-10 Impairs Local Immune Response in Lung Granulomas and Lymph Nodes during Early Mycobacterium tuberculosis Infection

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.

Polymorphisms in TNF Receptor Superfamily 1B (TNFRSF1B:rs3397) are Linked to Mycobacterium avium paratuberculosis Infection and Osteoporosis in Rheumatoid Arthritis

We previously discovered that single nucleotide polymorphisms (SNPs) in PTPN2/22 (T-cell negative-regulators) occur in 78% of rheumatoid arthritis (RA), along with Mycobacterium avium paratuberculosis (MAP) infection in 33% of patients. In Crohn's disease, we reported that SNPs in TNFα and receptors (TNFRSF1A/TNFRSF1B) benefited intracellular MAP-survival, increased infection, and elevated inflammatory response mimicking the poor response to anti-TNFα treatment in some patients. Here, we studied the frequency and effects of SNPs in TNFα/TNFRSF1A/TNFRSF1B in RA including gene expression, MAP infection, and osteoporosis marker levels in blood (54 RA and 48 healthy controls). TNFα:rs1800629 (GA) was detected in 19/48 (40%) RA and 8/54 (15%) controls (p-value < 0.05, odds ratio (OR) = 3.6, 95% CI: 1.37-9.54). TNFRS1B:rs3397 (CT) was detected in 21/48 (44%) RA and 10/54 (19%) controls (p-value < 0.05, OR = 4.43, 95% CI: 1.73-11.33). In RA, rs3397 downregulated TNFRSF1B expression (CC > CT (0.34 ± 0.14) and CC > TT (0.27 ± 0.12)), compared to wildtype CC (0.51 ± 0.17), p-value < 0.05. MAP DNA was detected significantly in 17/48 (35.4%) RA compared to 11/54 (20.4%) controls (p-value < 0.05, OR = 2.14, 95% CI: 1.12-5.20). The average osteocalcin level was significantly lower (p-value < 0.05) in RA (2.70 ± 0.87 ng/mL), RA + MAP (0.60 ± 0.31 ng/mL), RA + TNFRSF1B:rs3397 (TT) (0.67 ± 0.35 ng/mL), compared to the healthy control (5.31 ± 1.39 ng/mL), and MAP-free RA (3.85 ± 1.31 ng/mL). Overall, rs3397 appears to downregulate TNFRSF1B, increase MAP infection, worsen inflammation, and cause osteocalcin deficiency and possibly osteoporosis in RA.

TNF-α inhibitors in the treatment of hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a complex disease with a dramatic impact on the quality of life of patients that it afflicts. Despite this, there are few treatment options offering long-term relief. The exact pathophysiology of HS is unclear, although the current theory involves follicular obstruction, rupture, and subsequent inflammation leading to fistula and abscess development in intertriginous skin. Several inflammatory modulators have been implicated in the development of HS, including tumor necrosis factor (TNF)-α as well as interleukin (IL)-1β, IL-10, and IL-17. Initial evidence for the use of TNF-α inhibitors in HS stemmed from recognition that inflammatory bowel disease patients treated with these medications saw a concurrent improvement in their HS symptoms. Early case reports and case series illustrated TNF-α inhibitors’ value in the treatment of HS. Later, two phase III clinical trials, PIONEER I and PIONEER II, demonstrated that adalimumab is an efficacious treatment for HS. Infliximab represents another effective HS treatment option with its main advantage being dosing flexibility. In contrast, clinical trials have failed to show evidence for application of etanercept in HS. There is limited data on other TNF-α inhibitors such as certolizumab-pegol and golimumab. This review outlines the history, dosing, response, and adverse effects of TNF-α inhibitors in the treatment of HS.

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.

A Multiscale Agent-Based Model for the Investigation of E. coli K12 Metabolic Response During Biofilm Formation

Bacterial biofilm formation is an organized collective response to biochemical cues that enables bacterial colonies to persist and withstand environmental insults. We developed a multiscale agent-based model that characterizes the intracellular, extracellular, and cellular scale interactions that modulate Escherichia coli MG1655 biofilm formation. Each bacterium's intracellular response and cellular state were represented as an outcome of interactions with the environment and neighboring bacteria. In the intracellular model, environment-driven gene expression and metabolism were captured using statistical regression and Michaelis-Menten kinetics, respectively. In the cellular model, growth, death, and type IV pili- and flagella-dependent movement were based on the bacteria's intracellular state. We implemented the extracellular model as a three-dimensional diffusion model used to describe glucose, oxygen, and autoinducer 2 gradients within the biofilm and bulk fluid. We validated the model by comparing simulation results to empirical quantitative biofilm profiles, gene expression, and metabolic concentrations. Using the model, we characterized and compared the temporal metabolic and gene expression profiles of sessile versus planktonic bacterial populations during biofilm formation and investigated correlations between gene expression and biofilm-associated metabolites and cellular scale phenotypes. Based on our in silico studies, planktonic bacteria had higher metabolite concentrations in the glycolysis and citric acid cycle pathways, with higher gene expression levels in flagella and lipopolysaccharide-associated genes. Conversely, sessile bacteria had higher metabolite concentrations in the autoinducer 2 pathway, with type IV pili, autoinducer 2 export, and cellular respiration genes upregulated in comparison with planktonic bacteria. Having demonstrated results consistent with in vitro static culture biofilm systems, our model enables examination of molecular phenomena within biofilms that are experimentally inaccessible and provides a framework for future exploration of how hypothesized molecular mechanisms impact bulk community behavior.

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.

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.

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.

Tuberculosis and lung damage: from epidemiology to pathophysiology

A past history of pulmonary tuberculosis (TB) is a risk factor for long-term respiratory impairment. Post-TB lung dysfunction often goes unrecognised, despite its relatively high prevalence and its association with reduced quality of life. Importantly, specific host and pathogen factors causing lung impairment remain unclear. Host immune responses probably play a dominant role in lung damage, as excessive inflammation and elevated expression of lung matrix-degrading proteases are common during TB. Variability in host genes that modulate these immune responses may determine the severity of lung impairment, but this hypothesis remains largely untested. In this review, we provide an overview of the epidemiological literature on post-TB lung impairment and link it to data on the pathogenesis of lung injury from the perspective of dysregulated immune responses and immunogenetics.

Host factors driving lung injury in TB likely contribute to variable patterns of pulmonary impairment after TBhttp://ow.ly/a3of30hBsxB

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.

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.

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.

Solving Immunology?

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

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

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

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

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

Mycobacterium indicus pranii as a booster vaccine enhances BCG induced immunity and confers higher protection in animal models of tuberculosis

BCG, the only approved vaccine protects against severe form of childhood tuberculosis but its protective efficacy wanes in adolescence. BCG has reduced the incidence of infant TB considerably in endemic areas; therefore prime-boost strategy is the most realistic measure for control of tuberculosis in near future. Mycobacterium indicus pranii (MIP) shares significant antigenic repertoire with Mtb and BCG and has been shown to impart significant protection in animal models of tuberculosis. In this study, MIP was given as a booster to BCG vaccine which enhanced the BCG mediated immune response, resulting in higher protection. MIP booster via aerosol route was found to be more effective in protection than subcutaneous route of booster immunization. Pro-inflammatory cytokines like IFN-γ, IL-12 and IL-17 were induced at higher level in infected lungs of 'BCG-MIP' group both at mRNA expression level and in secretory form when compared with 'only BCG' group. BCG-MIP groups had increased frequency of multifunctional T cells with high MFI for IFN-γ and TNF-α in Mtb infected mice. Our data demonstrate for the first time, potential application of MIP as a booster to BCG vaccine for efficient protection against tuberculosis. This could be very cost effective strategy for efficient control of tuberculosis.

Understanding the pathophysiology of the human TB lung granuloma using in vitro granuloma models

Tuberculosis remains a major human health threat that infects one in three individuals worldwide. Infection with Mycobacterium tuberculosis is a standoff between host and bacteria in the formation of a granuloma. This review will introduce a variety of bacterial and host factors that impact individual granuloma fates. The authors describe advances in the development of in vitro granuloma models, current evidence surrounding infection and granuloma development, and the applicability of existing in vitro models in the study of human disease. In vitro models of infection help improve our understanding of pathophysiology and allow for the discovery of other potential models of study.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis

Pulmonary fibrosis is pathologic remodeling of lung tissue that can result in difficulty breathing, reduced quality of life, and a poor prognosis for patients. Fibrosis occurs as a result of insult to lung tissue, though mechanisms of this response are not well-characterized. The disease is driven in part by dysregulation of fibroblast proliferation and differentiation into myofibroblast cells, as well as pro-fibrotic mediator-driven epithelial cell apoptosis. The most well-characterized pro-fibrotic mediator associated with pulmonary fibrosis is TGF-β1. Excessive synthesis of, and sensitivity to, pro-fibrotic mediators as well as insufficient production of and sensitivity to anti-fibrotic mediators has been credited with enabling fibroblast accumulation. Available treatments neither halt nor reverse lung damage. In this study we have two aims: to identify molecular and cellular scale mechanisms driving fibroblast proliferation and differentiation as well as epithelial cell survival in the context of fibrosis, and to predict therapeutic targets and strategies. We combine in vitro studies with a multi-scale hybrid agent-based computational model that describes fibroblasts and epithelial cells in co-culture. Within this model TGF-β1 represents a pro-fibrotic mediator and we include detailed dynamics of TGF-β1 receptor ligand signaling in fibroblasts. PGE2 represents an anti-fibrotic mediator. Using uncertainty and sensitivity analysis we identify TGF-β1 synthesis, TGF-β1 activation, and PGE2 synthesis among the key mechanisms contributing to fibrotic outcomes. We further demonstrate that intervention strategies combining potential therapeutics targeting both fibroblast regulation and epithelial cell survival can promote healthy tissue repair better than individual strategies. Combinations of existing drugs and compounds may provide significant improvements to the current standard of care for pulmonary fibrosis. Thus, a two-hit therapeutic intervention strategy may prove necessary to halt and reverse disease dynamics.

Isoniazid-induced control of Mycobacterium tuberculosis by primary human cells requires interleukin-1 receptor and tumor necrosis factor

Proinflammatory cytokines are critical mediators that control Mycobacterium tuberculosis (Mtb) growth during active tuberculosis (ATB). To further inhibit bacterial proliferation in diseased individuals, drug inhibitors of cell wall synthesis such as isoniazid (INH) are employed. However, whether INH presents an indirect effect on bacterial growth by regulating host cytokines during ATB is not well known. To examine this hypothesis, we used an in vitro human granuloma system generated with primary leukocytes from healthy donors adapted to model ATB. Intense Mtb proliferation in cell cultures was associated with monocyte/macrophage activation and secretion of IL-1β and TNF. Treatment with INH significantly reduced Mtb survival, but altered neither T-cell-mediated Mtb killing, nor production of IL-1β and TNF. However, blockade of both IL-1R1 and TNF signaling rescued INH-induced killing, suggesting synergistic roles of these cytokines in mediating control of Mtb proliferation. Additionally, mycobacterial killing by INH was highly dependent upon drug activation by the pathogen catalase-peroxidase KatG and involved a host PI3K-dependent pathway. Finally, experiments using coinfected (KatG-mutated and H37Rv strains) cells suggested that active INH does not directly enhance host-mediated killing of Mtb. Our results thus indicate that Mtb-stimulated host IL-1 and TNF have potential roles in TB chemotherapy.

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

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.

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome

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.

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.

Analysis of Plasma Cytokine and Chemokine Profiles in Patients with and without Tuberculosis by Liquid Array-Based Multiplexed Immunoassays

The aim of this study was to establish plasma cytokine/chemokine profiles in patients with 3 different presentations of active tuberculosis (TB), compared to the profiles observed in bacillus Calmette-Guérin (BCG)-vaccinated healthy individuals and patients with other pulmonary diseases (non-TB patients). To this end, plasma samples were collected from 151 TB patients including 68 pulmonary TB (PTB), 43 endobronchial TB, and 40 tuberculosis pleurisy (TP) patients, as well as 107 no-TB cases including 26 non-TB patients and 81 BCG-vaccinated healthy controls. A liquid array-based multiplexed immunoassay was used to screen plasma samples for 20 distinct cytokines and chemokines. Multinomial logistic regression was used to analyze associations between cytokines/chemokines and TB/non-TB patients. Compared to our findings with the no-TB donors, the median plasma levels of the proinflammatory cytokines/chemokines TNF-α, IL-6, IP-10, IFN-γ, and MIP-1β were significantly elevated in TB patients, suggesting their potential use as biomarkers for diagnosing TB patients. Further comparisons with healthy donors showed that only the median TNF-α plasma level was highly produced in the plasma of all 3 types of TB patients. Plasma IL-6 production was higher only in TP patients, while the plasma levels of IP-10, IFN-γ, and MIP-1β were markedly enhanced in both PTB and TP patients. Unexpectedly, among the above cytokines/chemokines, MIP-1β was also highly expressed in non-TB patients, compared with healthy donors. Our results suggested that TNF-α may be an ideal biomarker for diagnosing the 3 forms of TB presentation, while the other factors (IL-6, IP-10, MCP-1, and IFN-γ) can potentially facilitate differential diagnosis for the 3 TB presentation types. Further characterization of immune responses associated with different types of TB diseases will provide a basis for developing novel TB diagnostics.

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

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

Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies

The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

A novel role of Yin-Yang-1 in pulmonary tuberculosis through the regulation of the chemokine CCL4

Mycobacterium tuberculosis (M. tb) is the etiological agent of pulmonary tuberculosis (TB); this disease remains a worldwide health problem. Yin-Yang-1 (YY1) plays a major role in the maintenance and progression of some pulmonary diseases, including pulmonary fibrosis. However, the role of YY1 in TB remains unknown. The aim of this study was to elucidate the role of YY1 in the regulation of CCL4 and its implication in TB. We determined whether YY1 regulates CCL4 using reporter plasmids, ChIP and siRNA assays. Immunohistochemistry and digital pathology were used to measure the expression of YY1 and CCL4 in a mouse model of TB. A retrospective comparison of patients with TB and control subjects was used to measure the expression of YY1 and CCL4 using tissue microarrays. Our results showed that YY1 regulates the transcription of CCL4; moreover, YY1, CCL4 and TGF-β were overexpressed in the lung tissues of mice with TB during the late stages of the disease and the tissues of TB patients. The expression of CCL4 and TGF-β correlated with YY1 expression. In conclusion, YY1 regulates CCL4 transcription; moreover, YY1 is overexpressed in experimental and human TB and is positively correlated with CCL4 and TGF-β expression. Therefore, treatments that decrease YY1 expression may be a new therapeutic strategy against TB.