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

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Automated quantitative assay of fibrosis characteristics in tuberculosis granulomas

Introduction

Granulomas, the pathological hallmark of Mycobacterium tuberculosis (Mtb) infection, are formed by different cell populations. Across various stages of tuberculosis conditions, most granulomas are classical caseous granulomas. They are composed of a necrotic center surrounded by multilayers of histocytes, with the outermost layer encircled by fibrosis. Although fibrosis characterizes the architecture of granulomas, little is known about the detailed parameters of fibrosis during this process.

Methods

In this study, samples were collected from patients with tuberculosis (spanning 16 organ types), and Mtb-infected marmosets and fibrotic collagen were characterized by second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy using a stain-free, fully automated analysis program.

Results

Histopathological examination revealed that most granulomas share common features, including necrosis, solitary and compact structure, and especially the presence of multinuclear giant cells. Masson's trichrome staining showed that different granuloma types have varying degrees of fibrosis. SHG imaging uncovered a higher proportion (4%~13%) of aggregated collagens than of disseminated type collagens (2%~5%) in granulomas from matched tissues. Furthermore, most of the aggregated collagen presented as short and thick clusters (200~620 µm), unlike the long and thick (200~300 µm) disseminated collagens within the matched tissues. Matrix metalloproteinase-9, which is involved in fibrosis and granuloma formation, was strongly expressed in the granulomas in different tissues.

Discussion

Our data illustrated that different tuberculosis granulomas have some degree of fibrosis in which collagen strings are short and thick. Moreover, this study revealed that the SHG imaging program could contribute to uncovering the fibrosis characteristics of tuberculosis granulomas.

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.

Active tuberculosis in inflammatory bowel disease patients: a case-control study

Background/Aims

Anti-tumor necrosis factor (anti-TNF) drugs have been the mainstay therapy for moderate to severe inflammatory bowel disease (IBD) over the past 25 years. Nevertheless, these drugs are associated with serious opportunistic infections like tuberculosis (TB). Brazil is ranked among the 30 countries with the highest incidence of TB in the world. This study aimed at identifying risk factors for the development of active TB and describing clinical characteristics and outcomes in IBD patients followed at a tertiary referral center in Brazil.

Methods

We conducted a retrospective, case-control study between January 2010 and December 2021. Active TB cases in IBD patients were randomly matched 1:3 to controls (IBD patients with no previous history of active TB) according to gender, age, and type of IBD.

Design

This was a retrospective, case-control study.

Results

A total of 38 (2.2%) cases of TB were identified from 1760 patients under regular follow-up at our outpatient clinics. Of the 152 patients included in the analysis (cases and controls), 96 (63.2%) were male, and 124 (81.6%) had Crohn's disease. Median age at TB diagnosis was 39.5 [interquartile range (IQR) 30.8-56.3]. Half of the active TB cases were disseminated (50%). Overall, 36 patients with TB (94.7%) were being treated with immunosuppressive medications. Of those, 31 (86.1%) were under anti-TNF drugs. Diagnosis of TB occurred at a median of 32 months after the first dose of anti-TNF (IQR 7-84). In multivariate analysis, IBD diagnosis older than 17 years and anti-TNF therapy were significantly associated with the development of TB (p < 0.05). After the TB treatment, 20 (52.7%) patients received anti-TNF therapy, and only one developed 'de novo' TB 10 years after the first infection.

Conclusions

TB remains a significant health problem in IBD patients from endemic regions, especially those treated with anti-TNFs. In addition, age at IBD diagnosis (>17 years old) was also a risk factor for active TB. Most cases occur after long-term therapy, suggesting a new infection. The reintroduction of anti-TNFs agents after the anti-TB treatment seems safe. These data highlight the importance of TB screening and monitoring in IBD patients living in endemic areas.

Alveolar deposition of inhaled fine particulate matter increased risk of severity of pulmonary tuberculosis in the upper and middle lobes

Inhaled PM_2.5 associated with pulmonary tuberculosis https://bit.ly/3VXAKfq.

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.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

An orally available small molecule that targets soluble TNF to deliver anti-TNF biologic-like efficacy in rheumatoid arthritis

Tumor necrosis factor (TNF) is a pleiotropic cytokine belonging to a family of trimeric proteins with both proinflammatory and immunoregulatory functions. TNF is a key mediator in autoimmune diseases and during the last couple of decades several biologic drugs have delivered new therapeutic options for patients suffering from chronic autoimmune diseases such as rheumatoid arthritis and chronic inflammatory bowel disease. Attempts to design small molecule therapies directed to this cytokine have not led to approved products yet. Here we report the discovery and development of a potent small molecule inhibitor of TNF that was recently moved into phase 1 clinical trials. The molecule, SAR441566, stabilizes an asymmetrical form of the soluble TNF trimer, compromises downstream signaling and inhibits the functions of TNF in vitro and in vivo. With SAR441566 being studied in healthy volunteers we hope to deliver a more convenient orally bioavailable and effective treatment option for patients suffering with chronic autoimmune diseases compared to established biologic drugs targeting TNF.

Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection

The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.

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.

Review article: latent tuberculosis in patients with inflammatory bowel diseases receiving immunosuppression-risks, screening, diagnosis and management

BACKGROUND

One quarter of the world's population has latent tuberculosis infection (LTBI). Systemic immunosuppression is a risk factor for LTBI reactivation and the development of active tuberculosis. Such reactivation carries a risk of significant morbidity and mortality. Despite the increasing global incidence of inflammatory bowel disease (IBD) and the use of immune-based therapies, current guidelines on the testing and treatment of LTBI in patients with IBD are haphazard with a paucity of evidence.

AIM

To review the screening, diagnostic practices and medical management of LTBI in patients with IBD.

METHODS

Published literature was reviewed, and recommendations for testing and treatment were synthesised by experts in both infectious diseases and IBD.

RESULTS

Screening for LTBI should be performed proactively and includes assessment of risk factors, an interferon-gamma releasing assay or tuberculin skin test and chest X-ray. LTBI treatment in patients with IBD is scenario-dependent, related to geographical endemicity, travel and other factors. Ideally, LTBI therapy should be used prior to immune suppression but can be applied concurrently where urgent IBD medical treatment is required. Management is best directed by a multidisciplinary team involving gastroenterologists, infectious diseases specialists and pharmacists. Ongoing surveillance is recommended during therapy. Newer LTBI therapies show promise, but medication interactions need to be considered. There are major gaps in evidence, particularly with specific newer therapeutic approaches to IBD.

CONCLUSIONS

Proactive screening for LTBI is essential in patients with IBD undergoing immune-suppressing therapy and several therapeutic strategies are available. Reporting of real-world experience is essential to refining current management recommendations.

Stringent screening strategy significantly reduces reactivation rates of tuberculosis in patients with inflammatory bowel disease on anti-TNF therapy in tuberculosis endemic region

BACKGROUND

Anti-tumor necrosis factor (anti-TNF) therapy use in patients with inflammatory bowel disease (IBD) leads to an increased risk of tuberculosis (TB) reactivation despite latent tuberculosis (LTB) screening, especially in TB endemic regions.

AIM

We evaluated the effect of stringent screening strategy and LTB prophylaxis on TB reactivation.

METHODS

We performed an ambispective comparison between patients who received anti-TNF therapy after January 2019 (late cohort) and between Jan 2005 and Jan 2019 (early cohort). Late cohort patients were subjected to stringent screening criteria which included all: history of past TB/recent contact with active TB, chest X-ray, CT (computed tomography) chest, IGRA (interferon-gamma release assay), TST (tuberculin skin test), and if any positive were given chemoprophylaxis. A cohort comparison was done to evaluate for risk reduction of TB following the stringent screening strategy.

RESULTS

One hundred seventy-one patients (63: ulcerative colitis/108: Crohn's disease, mean age diagnosis: 28.5 ± 13.4 years, 60% males, median follow-up duration after anti-TNF: 33 months [interquartile range: 23-57 months]) were included. Among the 112 in the early cohort, 29 (26%) underwent complete TB screening, 22 (19.6%) had LTB, 10 (9%) received chemoprophylaxis, and 19 (17%) developed TB. In comparison, in the late cohort, 100% of patients underwent complete TB screening, 26 (44%) had LTB, 23 (39%) received chemoprophylaxis, and only 1(1.7%) developed TB (p < 0.01). On survival analysis, patients in early cohort had a higher probability of TB reactivation compared with the late cohort (HR: 14.52 (95% CI: 1.90-110.61 [p = 0.01]) after adjusting for gender, age at anti-TNF initiation, concomitant immunosuppression, anti-TNF doses, and therapy escalation.

CONCLUSION

The high risk of TB reactivation with anti-TNF therapy in TB endemic regions can be significantly mitigated with stringent LTB screening and chemoprophylaxis.

Building digital twins of the human immune system: toward a roadmap

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.

Impact of Immunosuppressive Therapy on the Performance of Latent Tuberculosis Screening Tests in Patients with Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis

Screening for latent tuberculosis infection (LTBI) is mandatory before commencing tumor necrosis factor (TNF)-α inhibitor use. However, the impact of immunosuppressive therapy (IST), including corticosteroids and immunomodulators, on the performance of LTBI screening in patients with inflammatory bowel disease (IBD) has not been fully elucidated. We searched all relevant studies published before November 2021 that examined the performance of interferon γ release assays (IGRAs) and tuberculin skin tests (TSTs) in patients with IBD who received IST, using the Medline, EMBASE, and Cochrane Library databases. We performed meta-analyses of positive or indeterminate rates of IGRA or TST according to IST and calculated the concordance rates between IGRA and TST results. A total of 20 studies with 4045 patients were included in the meta-analysis. The IGRA-positive rate was lower in patients on IST than in those not on IST (odds ratio (OR) (95% confidence interval (CI)) = 0.55 (0.39–0.78)), whereas the IGRA-indeterminate rate was higher in patients on IST than in those not on IST (OR (95% CI) = 2.91 (1.36–6.24)). The TST-positive rate did not differ between the on-IST and not-on-IST groups (OR (95% CI) = 0.87 (0.51–1.50)). The concordance rate between IGRA and TST was 83.3% (95% CI, 78.5–88.1%). The IGRA-negative/TST-positive rate tended to be higher than that the IGRA-positive/TST-negative rate (9.5% vs. 5.8%, respectively), although the difference was not statistically significant. In conclusion, IGRA results were negatively affected by IST in patients with IBD, supporting requirements that IGRA should be performed before initiating IST. The use of both an IGRA and TST in patients with IBD on IST may improve the diagnosis rate of LTBI.

A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes

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

Biologics for Psoriasis During the COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19), a new form of acute infectious respiratory syndrome first reported in 2019, has rapidly spread worldwide and has been recognized as a pandemic by the WHO. It raised widespread concern about the treatment of psoriasis in this COVID-19 pandemic era, especially on the biologics use for patients with psoriasis. This review will summarize key information that is currently known about the relationship between psoriasis, biological treatments, and COVID-19, and vaccination-related issues. We also provide references for dermatologists and patients when they need to make clinical decisions. Currently, there is no consensus on whether biological agents increase the risk of coronavirus infection; however, current research shows that biological agents have no adverse effects on the prognosis of patients with COVID-19 with psoriasis. In short, it is not recommended to stop biological treatment in patients with psoriasis to prevent the infection risk, and for those patients who tested positive for SARS-CoV-2, the decision to pause biologic therapy should be considered on a case-by-case basis, and individual risk and benefit should be taken into account. Vaccine immunization against SARS-CoV-2 is strictly recommendable in patients with psoriasis without discontinuation of their biologics but evaluating the risk-benefit ratio of maintaining biologics before vaccination is mandatory at the moment.

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.

A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.

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.

Classical CD4 T cells as the cornerstone of antimycobacterial immunity

Tuberculosis is a significant health problem without an effective vaccine to combat it. A thorough understanding of the immune response and correlates of protection is needed to develop a more efficient vaccine. The immune response against Mycobacterium tuberculosis (Mtb) is complex and involves all aspects of the immune system, however, the optimal protective, non‐pathogenic T cell response against Mtb is still elusive. This review will focus on discussing CD4 T cell immunity against mycobacteria and its importance in Mtb infection with a primary focus on human studies. We will in particular discuss the large heterogeneity of immune cell subsets that have been revealed by recent immunological investigations at an unprecedented level of detail. These studies have identified specific classical CD4 T cell subsets important for immune responses against Mtb in various states of infection. We further discuss the functional attributes that have been linked to the various subsets such as upregulation of activation markers and cytokine production. Another important topic to be considered is the antigenic targets of Mtb‐specific immune responses, and how antigen reactivity is influenced by both disease state and environmental exposure(s). These are key points for both vaccines and immune diagnostics development. Ultimately, these factors are holistically considered in the definition and investigations of what are the correlates on protection and resolution of disease.

Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4rα and Socs1, validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.

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.

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.

Hesperidin methyl chalcone alleviates spinal tuberculosis in New Zealand white rabbits by suppressing immune responses

Objective: Spinal tuberculosis (ST) refers to tuberculosis resulted from infections of Mycobacterium tuberculosis (Mtb) in the spinal cord. Hesperidin methyl chalcone (HMC) is a flavonoid derivative from citrus fruits with anti-inflammatory properties. We aimed to investigate the efficacy of HMC in treating ST in New Zealand white rabbit model. Design and Setting: Rabbits were infected in the sixth lumbar vertebral bodies with or without Mtb strain H37Rv followed by treatments with HMC. Outcome Measures: 10 weeks post treatments, the adjacent vertebral tissues were examined by hematoxylin-eosin staining. The expression levels of transcription factor κB (NF-κB) p65 and monocyte chemoattractant protein-1 (MCP-1/CCL2) in lymphocytes were determined using reverse transcription quantitative real-time PCR (RT-qPCR), Western blot and enzyme-linked immunosorbent assays (ELISA). The serum levels of interleukin (IL)-2, IL-4, IL-10 as well as interferon (IFN)-γ were also assessed using ELISA. Western blot was used to determine the effects of HMC on the phosphorylation of IKKα/β, p65, and IκBα in the signal transduction of NF-κB pathways. Results: HMC significantly attenuated the granulation in adjacent vertebral bone tissues. The expression of p65, IL-4, IL-10, and MCP-1 was reduced. The NF-κB pathway was suppressed, in which the phosphorylation of IκBα, IKKα/β, and p65 was inhibited whereas the relative level of IκBα was increased. Conclusion: HMC could serve as a therapeutic option to effectively inhibit granulomas formation through downregulation of MCP-1, IL-4, IL-10, and NF-κB in the treatment of ST.

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.

Association Between Ambient Air Pollution and Elevated Risk of Tuberculosis Development

Background

Broad-scale evidence has shown the significant association between ambient air pollutants and the development of tuberculosis (TB). However, the impact of air quality on the risk of TB in Taiwan is still poorly understood.

Objective

To develop a probabilistic integrated population-level risk assessment approach for evaluating the contribution of ambient air pollution exposure to the risk of TB development among different regions of Taiwan.

Materials and methods

A Bayesian-based probabilistic risk assessment model was implemented to link exposure concentrations of various air pollutants quantified in a probabilistic manner with the population-based exposure-response models developed by using an epidemiological investigation.

Results

The increment of the risk of TB occurred in a region with a higher level of air pollution, indicating a strong relationship between ambient air pollution exposures and TB incidences. Carbon monoxide (CO) exposure showed the highest population attributable fraction (PAF), followed by nitrogen oxides (NO_X) and nitrogen dioxide (NO₂) exposures. In a region with higher ambient air pollution, it is most likely (80% risk probability) that the contributions of CO exposure to development of TB were 1.6–12.2% (range of median PAFs), whereas NO_X and NO₂ exposures contributed 1.2–9.8% to developing TB.

Conclusion

Our findings provide strong empirical support for the hypothesis and observations from the literature that poor air quality is highly likely to link aetiologically to the risk of TB. Therefore, substantial reductions in CO, NO_X, and NO₂ exposures are predicted to have health benefits to susceptible and latently infected individuals that provide complementary mitigation efforts in reducing the burden of TB. Considering that people continue to be exposed to both TB bacilli and ambient air pollutants, our approach can be applied for different countries/regions to identify which air pollutants contribute to a higher risk of TB in order to develop potential mitigation programs.

Global sensitivity analysis of biological multi-scale models

Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.

Heterogeneous responses to low level death receptor activation are explained by random molecular assembly of the Caspase-8 activation platform

Ligand binding to death receptors activates apoptosis in cancer cells. Stimulation of death receptors results in the formation of intracellular multiprotein platforms that either activate the apoptotic initiator Caspase-8 to trigger cell death, or signal through kinases to initiate inflammatory and cell survival signalling. Two of these platforms, the Death-Inducing Signalling Complex (DISC) and the RIPoptosome, also initiate necroptosis by building filamentous scaffolds that lead to the activation of mixed lineage kinase domain-like pseudokinase. To explain cell decision making downstream of death receptor activation, we developed a semi-stochastic model of DISC/RIPoptosome formation. The model is a hybrid of a direct Gillespie stochastic simulation algorithm for slow assembly of the RIPoptosome and a deterministic model of downstream caspase activation. The model explains how alterations in the level of death receptor-ligand complexes, their clustering properties and intrinsic molecular fluctuations in RIPoptosome assembly drive heterogeneous dynamics of Caspase-8 activation. The model highlights how kinetic proofreading leads to heterogeneous cell responses and results in fractional cell killing at low levels of receptor stimulation. It reveals that the noise in Caspase-8 activation-exclusively caused by the stochastic molecular assembly of the DISC/RIPoptosome platform-has a key function in extrinsic apoptotic stimuli recognition.

Inhibition of the PD-1/PD-L1 signaling pathway enhances innate immune response of alveolar macrophages to mycobacterium tuberculosis in mice

BACKGROUND

Mycobacterium tuberculosis (TB) is a pathogen that consequently leads to TB infection, which remains a significant global health concern. Programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) signaling pathway is critical for terminating immune responses. The present study aimed to elucidate the regulatory role of the PD-1/PD-L1 signaling pathway in alveolar macrophages against Mycobacterium TB in mice.

METHODS

Specific pathogen free mice were initially prepared for Mycobacterium TB model establishment. The alveolar macrophages of the successfully modeled rats were evaluated to determine the levels of PD-1, PD-L1, AKT, mTOR, TNF-α, NF-κB, IL-2, IL-4, IL-6, IL-10, IL-17, IL-17A, and IFN-γ. The surface makers of macrophages (CD11c, CD16, CD86, CD163, CD206, CX3CR-1 and CSF-1R), level of ROS, apoptosis and cell cycle, were all assessed.

RESULTS

Elevated levels of PD-1 and PD-L1, decreased levels of AKT and mTOR, along with elevated levels of TNF-α, NF-κB, IL-17, IL-2, IL-6, IL-17A and IFN-γ were identified in the alveolar macrophages infected with Mycobacterium TB, while an opposite trend was observed when PD-1/PD-L1 signaling pathway was inhibited. Additionally, elevated protein levels of CD11c, CD16 and CD86, as well as an increased rate of positive ROS and cell apoptosis, levels of Bax, and a diminished percentage of alveolar macrophages at the S and G2/M stages were detected in the event of Mycobacterium TB infection. A contrasting trend to the aforementioned findings was detected when the PD-1/PD-L1 signaling pathway was inhibited.

CONCLUSION

Taken together, these results suggested that inhibition of the PD-1/PD-L1 signaling pathway enhanced the innate immune response of alveolar macrophages to Mycobacterium TB in mice.

Data-Driven Model Validation Across Dimensions

Data-driven model validation across dimensions in mathematical and computational biology assumptions are often made (e.g., symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purpose of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.

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.

High risk of tuberculosis during infliximab therapy despite tuberculosis screening in inflammatory bowel disease patients in India

Background/Aims

The data on the risk of tuberculosis (TB) reactivation with infliximab (IFX) in patients with inflammatory bowel disease (IBD) from TB endemic countries, like India, is limited. The risk of TB reactivation on IFX and its predictors in patients with IBD was assessed.

Methods

This retrospective review included consecutive patients with IBD who received IFX, and were on follow-up from January 2005 to November 2017. The data was recorded on age/disease duration, indications for IFX, screening for latent tuberculosis (LTB) before IFX, response to IFX, incidence and duration when TB developed after IFX, and type of TB (pulmonary [PTB]/extra-pulmonary [EPTB]/disseminated).

Results

Of 69 patients (22 ulcerative colitis/47 Crohn's disease; mean age, 35.6±14.5 years; 50.7% males; median follow-up duration after IFX, 19 months [interquartile range, 5.5-48.7 months]), primary non-response at 8 weeks and secondary loss of response at 26 and 52 weeks were seen in 14.5%, 6% and 15% patients respectively. Prior to IFX, all patients were screened for LTB, 8 (11.6%) developed active TB (disseminated, 62.5%; EPTB, 25%; PTB, 12.5%) after a median of 19 weeks (interquartile range, 14.0-84.5 weeks) of IFX. Of these 8 patients' none had LTB, even when 7 of 8 were additionally screened with contrast-enhanced chest tomography. Though not statistically significant, more patients with Crohn's disease than ulcerative colitis (14.9% vs. 4.5%, P=0.21), and those with past history of TB (25% vs. 9.8%, P=0.21), developed TB. Age, gender, disease duration, or extraintestinal manifestations could not predict TB reactivation.

Conclusions

There is an extremely high rate of TB with IFX in Indian patients with IBD. Current screening techniques are ineffective and it is difficult to predict TB after IFX.

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 usefulness of routine chest radiograph examinations in patients treated with TNF inhibitors for inflammatory arthritis in South Korea

OBJECTIVES

We aimed to investigate the usefulness of routine chest radiograph (CXR) examinations for patients with inflammatory arthritis treated with a tumor necrosis factor (TNF) inhibitor in terms of (i) the role of CXR in baseline latent tuberculosis infection (LTBI) screening and (ii) detecting asymptomatic active tuberculosis after TNF inhibitor initiation.

METHODS

From January 2011 to June 2017, 469 patients with inflammatory arthritis were enrolled in the study at a tertiary referral center in South Korea. At our institution, CXR was performed for all patients undergoing a tuberculin skin test (TST) and/or an interferon-gamma release assay (IGRA) at the LTBI screening visit. LTBI treatment was determined by (i) positive TST or IGRA or (ii) CXR findings suggestive of spontaneously healed tuberculosis. After TNF inhibitor initiation, patients were recommended to undergo CXR at a specified interval.

RESULTS

Of 469 patients, 187 were treated for LTBI. Among them, 181 patients were treated for LTBI because of a positive TST or IGRA result. TST was considered positive if induration size was ≥10 mm. The remaining six patients were considered positive on the basis of CXR findings compatible with spontaneously healed tuberculosis, such as noncalcified nodules with distinct margins and fibrotic linear opacity, despite demonstrating negative results for TST and IGRA. Thus, CXR had a diagnostic value as a baseline LTBI test in 6 (1.3%) patients. After TNF inhibitor initiation, 2 patients who had respiratory symptoms were diagnosed with active tuberculosis. For asymptomatic patients, routine CXR follow-up could not detect any case of active pulmonary tuberculosis within 1 year (n = 219) or after 1 year (n = 217).

CONCLUSIONS

CXR should be performed as one of the LTBI screening tests for patients with inflammatory arthritis in a tuberculosis-prevalent country. However, after TNF inhibitor treatment, routine CXR follow-up was not advantageous.

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.

Isoliquiritigenin attenuates spinal tuberculosis through inhibiting immune response in a New Zealand white rabbit model

Spinal tuberculosis (ST) is the tuberculosis caused by Mycobacterium tuberculosis (Mtb) infections in spinal curds. Isoliquiritigenin 4,2′,4′-trihydroxychalcone, ISL) is an anti-inflammatory flavonoid derived from licorice (Glycyrrhiza uralensis), a Chinese traditional medicine. In this study, we evaluated the potential of ISL in treating ST in New Zealand white rabbit models. In the model, rabbits (n=40) were infected with Mtb strain H37Rv or not in their 6^th lumbar vertebral bodies. Since the day of infection, rabbits were treated with 20 mg/kg and 100 mg/kg of ISL respectively. After 10 weeks of treatments, the adjacent vertebral bone tissues of rabbits were analyzed through Hematoxylin-Eosin staining. The relative expression of Monocyte chemoattractant protein-1 (MCP-1/CCL2), transcription factor κB (NF-κB) p65 in lymphocytes were verified through reverse transcription quantitative real-time PCR (RT-qPCR), western blotting and enzyme-linked immunosorbent assays (ELISA). The serum level of interleukin (IL)-2, IL-4, IL-10 and interferon γ (IFN-γ) were evaluated through ELISA. The effects of ISL on the phosphorylation of IκBα, IKKα/β and p65 in NF-κB signaling pathways were assessed through western blotting. In the results, ISL has been shown to effectively attenuate the granulation inside adjacent vertebral tissues. The relative level of MCP-1, p65 and IL-4 and IL-10 were retrieved. NF-κB signaling was inhibited, in which the phosphorylation of p65, IκBα and IKKα/β were suppressed whereas the level of IκBα were elevated. In conclusion, ISL might be an effective drug that inhibited the formation of granulomas through downregulating MCP-1, NF-κB, IL-4 and IL-10 in treating ST.

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.

Asian Organization for Crohn's and Colitis and Asia Pacific Association of Gastroenterology consensus on tuberculosis infection in patients with inflammatory bowel disease receiving anti-tumor necrosis factor treatment. Part 1: risk assessment

Because anti-tumor necrosis factor (anti-TNF) therapy has become increasingly popular in many Asian countries, the risk of developing active tuberculosis (TB) among anti-TNF users may raise serious health problems in this region. Thus, the Asian Organization for Crohn's and Colitis and the Asia Pacific Association of Gastroenterology have developed a set of consensus statements about risk assessment, detection and prevention of latent TB infection, and management of active TB infection in patients with inflammatory bowel disease (IBD) receiving anti-TNF treatment. Twenty-three consensus statements were initially drafted and then discussed by the committee members. The quality of evidence and the strength of recommendations were assessed by using the Grading of Recommendations Assessment, Development, and Evaluation methodology. Web-based consensus voting was performed by 211 IBD specialists from 9 Asian countries concerning each statement. A consensus statement was accepted if at least 75% of the participants agreed. Part 1 of the statements comprised 2 parts: risk of TB infection Recommendaduring anti-TNF therapy, and screening for TB infection prior to commencing anti-TNF therapy. These consensus statements will help clinicians optimize patient outcomes by reducing the morbidity and mortality related to TB infections in patients with IBD receiving anti-TNF treatment.

Asian Organization for Crohn's and Colitis and Asian Pacific Association of Gastroenterology consensus on tuberculosis infection in patients with inflammatory bowel disease receiving anti-tumor necrosis factor treatment. Part 1: Risk assessment

Because anti-tumor necrosis factor (anti-TNF) therapy has become increasingly popular in many Asian countries, the risk of developing active tuberculosis (TB) among anti-TNF users may raise serious health problems in this region. Thus, the Asian Organization for Crohn's and Colitis and the Asian Pacific Association of Gastroenterology have developed a set of consensus statements about risk assessment, detection, and prevention of latent TB infection and management of active TB infection in patients with inflammatory bowel disease (IBD) receiving anti-TNF treatment. Twenty-three consensus statements were initially drafted and then discussed by the committee members. The quality of evidence and the strength of recommendations were assessed by using the Grading of Recommendations Assessment, Development, and Evaluation methodology. Web-based consensus voting was performed by 211 IBD specialists from nine Asian countries concerning each statement. A consensus statement was accepted if at least 75% of the participants agreed. Part 1 of the statements comprised two parts: (i) risk of TB infection during anti-TNF therapy and (ii) screening for TB infection prior to commencing anti-TNF therapy. These consensus statements will help clinicians optimize patient outcomes by reducing the morbidity and mortality related to TB infections in patients with IBD receiving anti-TNF treatment.

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.