Modeling Granulomas in Response to Infection in the Lung

Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas

Physiological abnormalities in pulmonary granulomas-pathological hallmarks of tuberculosis (TB)-compromise the transport of oxygen, nutrients, and drugs. In prior studies, we demonstrated mathematically and experimentally that hypoxia and necrosis emerge in the granuloma microenvironment (GME) as a direct result of limited oxygen availability. Building on our initial model of avascular oxygen diffusion, here we explore additional aspects of oxygen transport, including the roles of granuloma vasculature, transcapillary transport, plasma dilution, and interstitial convection, followed by cellular metabolism. Approximate analytical solutions are provided for oxygen and glucose concentration, interstitial fluid velocity, interstitial fluid pressure, and the thickness of the convective zone. These predictions are in agreement with prior experimental results from rabbit TB granulomas and from rat carcinoma models, which share similar transport limitations. Additional drug delivery predictions for anti-TB-agents (rifampicin and clofazimine) strikingly match recent spatially-resolved experimental results from a mouse model of TB. Finally, an approach to improve molecular transport in granulomas by modulating interstitial hydraulic conductivity is tested in silico.

Within-Host Mathematical Models of Antibiotic Resistance

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Examining pathogen DNA recovery across the remains of a 14th century Italian friar (Blessed Sante) infected with Brucella melitensis

OBJECTIVE

To investigate variation in ancient DNA recovery of Brucella melitensis, the causative agent of brucellosis, from multiple tissues belonging to one individual MATERIALS: 14 samples were analyzed from the mummified remains of the Blessed Sante, a 14 ^th century Franciscan friar from central Italy, with macroscopic diagnosis of probable brucellosis.

METHODS

Shotgun sequencing data from was examined to determine the presence of Brucella DNA.

RESULTS

Three of the 14 samples contained authentic ancient DNA, identified as belonging to B. melitensis. A genome (23.81X depth coverage, 0.98 breadth coverage) was recovered from a kidney stone. Nine of the samples contained reads classified as B. melitensis (7-169), but for many the data quality was insufficient to withstand our identification and authentication criteria.

CONCLUSIONS

We identified significant variation in the preservation and abundance of B. melitensis DNA present across multiple tissues, with calcified nodules yielding the highest number of authenticated reads. This shows how greatly sample selection can impact pathogen identification.

SIGNIFICANCE

Our results demonstrate variation in the preservation and recovery of pathogen DNA across tissues. This study highlights the importance of sample selection in the reconstruction of infectious disease burden and highlights the importance of a holistic approach to identifying disease.

LIMITATIONS

Study focuses on pathogen recovery in a single individual.

SUGGESTIONS FOR FURTHER RESEARCH

Further analysis of how sampling impacts aDNA recovery will improve pathogen aDNA recovery and advance our understanding of disease in past peoples.

A cancer model with nonlocal free boundary dynamics

Cancer cells at the tumor boundary move in the direction of the oxygen gradient, while cancer cells far within the tumor are in a necrotic state. This paper introduces a simple mathematical model that accounts for these facts. The model consists of cancer cells, cytotoxic T cells, and oxygen satisfying a system of partial differential equations. Some of the model parameters represent the effect of anti-cancer drugs. The tumor boundary is a free boundary whose dynamics is determined by the movement of cancer cells at the boundary. The model is simulated for radially symmetric and axially symmetric tumors, and it is shown that the tumor may increase or decrease in size, depending on the "strength" of the drugs. Existence theorems are proved, global in-time in the radially symmetric case, and local in-time for any shape of tumor. In the radially symmetric case, it is proved, under different conditions, that the tumor may shrink monotonically, or expand monotonically.

Modulation of TDM-induced granuloma pathology by human lactoferrin: a persistent effect in mice

Lactoferrin (LTF), an iron binding protein, is known to exhibit immune modulatory effects on pulmonary pathology during insult-induced models of primary Mycobacterium tuberculosis (Mtb) infection. The effects of LTF correlate with modulation of the immune related development of the pathology, and altering of the histological nature of the physically compact and dense lung granuloma in mice. Specifically, a recombinant human version of LTF limits immediate progression of granulomatous severity following administration of the Mtb cell wall mycolic acid, trehalose 6,6'-dimycolate (TDM), in part through reduced pro-inflammatory responses known to control these events. This current study investigates a limited course of LTF to modulate not only initiation, but also maintenance and resolution of pathology post development of the granulomatous response in mice. Comparison is made to a fusion of LTF with the Fc domain of IgG2 (FcLTF), which is known to extend LTF half-life in circulation. TDM induced granulomas were examined at extended times post insult (day 7 and 14). Both LTF and the novel FcLTF exerted sustained effects on lung granuloma pathology. Reduction of pulmonary pro-inflammatory cytokines TNF-α and IL-1β occurred, correlating with reduced pathology. Increase in IL-6, known to regulate granuloma maintenance, was also seen with the LTFs. The FcLTF demonstrated greater impact than the recombinant LTF, and was superior in limiting damage to pulmonary tissues while limiting residual inflammatory cytokine production.

Human M1 macrophages express unique innate immune response genes after mycobacterial infection to defend against tuberculosis

Mycobacterium tuberculosis (Mtb) is responsible for approximately 1.5 million deaths each year. Though 10% of patients develop tuberculosis (TB) after infection, 90% of these infections are latent. Further, mice are nearly uniformly susceptible to Mtb but their M1-polarized macrophages (M1-MΦs) can inhibit Mtb in vitro, suggesting that M1-MΦs may be able to regulate anti-TB immunity. We sought to determine whether human MΦ heterogeneity contributes to TB immunity. Here we show that IFN-γ-programmed M1-MΦs degrade Mtb through increased expression of innate immunity regulatory genes (Inregs). In contrast, IL-4-programmed M2-polarized MΦs (M2-MΦs) are permissive for Mtb proliferation and exhibit reduced Inregs expression. M1-MΦs and M2-MΦs express pro- and anti-inflammatory cytokine-chemokines, respectively, and M1-MΦs show nitric oxide and autophagy-dependent degradation of Mtb, leading to increased antigen presentation to T cells through an ATG-RAB7-cathepsin pathway. Despite Mtb infection, M1-MΦs show increased histone acetylation at the ATG5 promoter and pro-autophagy phenotypes, while increased histone deacetylases lead to decreased autophagy in M2-MΦs. Finally, Mtb-infected neonatal macaques express human Inregs in their lymph nodes and macrophages, suggesting that M1 and M2 phenotypes can mediate immunity to TB in both humans and macaques. We conclude that human MФ subsets show unique patterns of gene expression that enable differential control of TB after infection. These genes could serve as targets for diagnosis and immunotherapy of TB.

Micro-Computed Tomography Analysis of the Human Tuberculous Lung Reveals Remarkable Heterogeneity in Three-dimensional Granuloma Morphology

Rationale: Our current understanding of tuberculosis (TB) pathophysiology is limited by a reliance on animal models, the paucity of human TB lung tissue, and traditional histopathological analysis, a destructive two-dimensional approach that provides limited spatial insight. Determining the three-dimensional (3D) structure of the necrotic granuloma, a characteristic feature of TB, will more accurately inform preventive TB strategies.Objectives: To ascertain the 3D shape of the human tuberculous granuloma and its spatial relationship with airways and vasculature within large lung tissues.Methods: We characterized the 3D microanatomical environment of human tuberculous lungs by using micro computed tomography, histopathology, and immunohistochemistry. By using 3D segmentation software, we accurately reconstructed TB granulomas, vasculature, and airways in three dimensions and confirmed our findings by using histopathology and immunohistochemistry.Measurements and Main Results: We observed marked heterogeneity in the morphology, volume, and number of TB granulomas in human lung sections. Unlike depictions of granulomas as simple spherical structures, human necrotic granulomas exhibit complex, cylindrical, branched morphologies that are connected to the airways and shaped by the bronchi. The use of 3D imaging of human TB lung sections provides unanticipated insight into the spatial organization of TB granulomas in relation to the airways and vasculature.Conclusions: Our findings highlight the likelihood that a single, structurally complex lesion could be mistakenly viewed as multiple independent lesions when evaluated in two dimensions. In addition, the lack of vascularization within obstructed bronchi establishes a paradigm for antimycobacterial drug tolerance. Lastly, our results suggest that bronchogenic spread of Mycobacterium tuberculosis reseeds the lung.

A computational bio-chemo-mechanical model of in vivo tissue-engineered vascular graft development

Stenosis is the primary complication of current tissue-engineered vascular grafts used in pediatric congenital cardiac surgery. Murine models provide considerable insight into the possible mechanisms underlying this situation, but they are not efficient for identifying optimal changes in scaffold design or therapeutic strategies to prevent narrowing. In contrast, computational modeling promises to enable time- and cost-efficient examinations of factors leading to narrowing. Whereas past models have been limited by their phenomenological basis, we present a new mechanistic model that integrates molecular- and cellular-driven immuno- and mechano-mediated contributions to in vivo neotissue development within implanted polymeric scaffolds. Model parameters are inferred directly from in vivo measurements for an inferior vena cava interposition graft model in the mouse that are augmented by data from the literature. By complementing Bayesian estimation with identifiability analysis and simplex optimization, we found optimal parameter values that match model outputs with experimental targets and quantify variability due to measurement uncertainty. Utility is illustrated by parametrically exploring possible graft narrowing as a function of scaffold pore size, macrophage activity, and the immunomodulatory cytokine transforming growth factor beta 1 (TGF-β1). The model captures salient temporal profiles of infiltrating immune and synthetic cells and associated secretion of cytokines, proteases, and matrix constituents throughout neovessel evolution, and parametric studies suggest that modulating scaffold immunogenicity with early immunomodulatory therapies may reduce graft narrowing without compromising compliance.

Clinicopathologic Features of Tissue Granulomas in Transplant Recipients: A Single Center Study in a Nontuberculosis Endemic Region

CONTEXT.—

There is a paucity of literature about tissue granulomas in transplant patients.

OBJECTIVE.—

To characterize the clinicopathologic features of granulomas in this population and develop a clinically judicious approach to their evaluation.

DESIGN.—

We performed chart reviews of solid organ and allogeneic hematopoietic stem cell transplant recipients at Yale New Haven Hospital to identify patients with granulomas on biopsy obtained pathologic specimens. Pretransplant and posttransplant specimens were included. Data points included demographics, clinical presentation, epidemiologic risk factors, biopsy indication, location and timing, immunosuppression, histopathology, microbiology, and associated clinical diagnosis. Granuloma-related readmissions and mortality were recorded at 1, 3, and 12 months.

RESULTS.—

Biopsy proven granulomas were identified in 56 of 2139 (2.6%) patients. Of 56, 16 (29%) were infectious. Common infectious etiologies were bartonellosis (n = 3) and cytomegalovirus hepatitis (n = 3). Tuberculosis was not identified. Clinical symptoms prompted tissue biopsy in 27 of 56 (48.2%) cases while biopsies were obtained for evaluation of incidental findings or routine disease surveillance in 29 of 56 (51.8%). Presence of symptoms was significantly associated with infectious etiologies; 11 of 27 (40.7%) symptomatic patients compared with 5 of 29 (17.2%) asymptomatic patients had infectious causes. One death from granulomatous cryptogenic organizing pneumonia occurred. In pretransplant asymptomatic patients, no episodes of symptomatic disease occurred posttransplantation.

CONCLUSIONS.—

Granulomas were uncommon in a large transplant population; most were noninfectious but presence of symptoms was associated with infectious etiologies. Granulomas discovered pretransplant without clear infectious etiology likely do not require prolonged surveillance after transplantation. Symptomatology and epidemiologic risks factors should guide extent of microbiologic evaluation.

The Pathogenesis of Tuberculosis-The Koch Phenomenon Reinstated

Research on the pathogenesis of tuberculosis (TB) has been hamstrung for half a century by the paradigm that granulomas are the hallmark of active disease. Human TB, in fact, produces two types of granulomas, neither of which is involved in the development of adult type or post-primary TB. This disease begins as the early lesion; a prolonged subclinical stockpiling of secreted mycobacterial antigens in foamy alveolar macrophages and nearby highly sensitized T cells in preparation for a massive necrotizing hypersensitivity reaction, the Koch Phenomenon, that produces caseous pneumonia that is either coughed out to form cavities or retained to become the focus of post-primary granulomas and fibrocaseous disease. Post-primary TB progresses if the antigens are continuously released and regresses when they are depleted. This revised paradigm is supported by nearly 200 years of research and suggests new approaches and animal models to investigate long standing mysteries of human TB and vaccines that inhibit the early lesion to finally end its transmission.

A reaction-diffusion model to understand granulomas formation inside secondary lobule during tuberculosis infection

Mycobacterium tuberculosis (Mtb) is the causative agent for tuberculosis, the most extended infectious disease around the world. When Mtb enters inside the pulmonary alveolus it is rapidly phagocytosed by the alveolar macrophage. Although this controls the majority of inhaled microorganisms, in this case, Mtb survives inside the macrophage and multiplies. A posterior chemokine and cytokine cascade generated by the irruption of monocytes, neutrophils and posteriorly, by T-cells, does not necessarily stop the growth of the granuloma. Interestingly, the encapsulation process built by fibroblasts is able to surround the lesion and stop its growing. The success of this last process determines if the host enters in an asymptomatic latent state or continues into a life-threatening and infective active tuberculosis disease (TB). Understanding such dichotomic process is challenging, and computational modeling can bring new ideas. Thus, we have modeled the different stages of the infection, first in a single alveolus (a sac with a radius of 0.15 millimeters) and, second, inside a secondary lobule (a compartment of the lungs of around 3 cm³). We have employed stochastic reaction-diffusion equations to model the interactions among the cells and the diffusive transport to neighboring alveolus. The whole set of equations have successfully described the encapsulation process and determine that the size of the lesions depends on its position on the secondary lobule. We conclude that size and shape of the secondary lobule are the relevant variables to control the lesions, and, therefore, to avoid the evolution towards TB development. As lesions appear near to interlobular connective tissue they are easily controlled and their growth is drastically stopped, in this sense secondary lobules with a more flattened shape could control better the lesion.

VapBC22 toxin-antitoxin system from Mycobacterium tuberculosis is required for pathogenesis and modulation of host immune response

Virulence-associated protein B and C toxin-antitoxin (TA) systems are widespread in prokaryotes, but their precise role in physiology is poorly understood. We have functionally characterized the VapBC22 TA system from Mycobacterium tuberculosis. Transcriptome analysis revealed that overexpression of VapC22 toxin in M. tuberculosis results in reduced levels of metabolic enzymes and increased levels of ribosomal proteins. Proteomics studies showed reduced expression of virulence-associated proteins and increased levels of cognate antitoxin, VapB22 in the ΔvapC22 mutant strain. Furthermore, both the ΔvapC22 mutant and VapB22 overexpression strains of M. tuberculosis were susceptible to killing upon exposure to oxidative stress and showed attenuated growth in guinea pigs and mice. Host transcriptome analysis suggests upregulation of the transcripts involved in innate immune responses and tissue remodeling in mice infected with the ΔvapC22 mutant strain. Together, we demonstrate that the VapBC22 TA system belongs to a key regulatory network and is essential for M. tuberculosis pathogenesis.

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.

Lactoferrin reduces mycobacterial M1-type inflammation induced with trehalose 6,6'-dimycolate and facilitates the entry of fluoroquinolone into granulomas

Primary infection with Mycobacterium tuberculosis (Mtb) results in the formation of a densely packed granulomatous response that essentially limits the entry and efficacy of immune effector cells. Furthermore, the physical nature of the granuloma does not readily permit the entry of therapeutic agents to sites where organisms reside. The Mtb cell wall mycolic acid, trehalose 6,6'-dimycolate (TDM), is a physiologically relevant molecule for modelling macrophage-mediated events during the establishment of the tuberculosis-induced granuloma pathogenesis. At present, there are no treatments for tuberculosis that focus on modulating the host's immune responses. Previous studies showed that lactoferrin (LF), a natural iron-binding protein proven to modulate inflammation, can ameliorate the cohesiveness of granuloma. This led to a series of studies that further examined the effects of recombinant human LF (rHLF) on the histological progression of TDM-induced pathology. Treatment with rHLF demonstrated significant reduction in size and number of inflammatory foci following injections of TDM, together with reduced levels pulmonary pro-inflammatory cytokines TNF-α and IL-1β. LF facilitated greater penetration of fluoroquinolone to the sites of pathology. Mice treated with TDM alone demonstrated exclusion of ofloxacin to regions of inflammatory response, whereas the animals treated with rHLF demonstrated increased penetration to inflammatory foci. Finally, recent findings support the hypothesis that this mycobacterial mycolic acid can specifically recruit M1-like polarized macrophages; rHLF treatment was shown to limit the level of this M1-like phenotypic recruitment, corresponding highly with decreased inflammatory response.

Modelling the dynamics of tuberculosis lesions in a virtual lung: Role of the bronchial tree in endogenous reinfection

Tuberculosis (TB) is an infectious disease that still causes more than 1.5 million deaths annually. The World Health Organization estimates that around 30% of the world's population is latently infected. However, the mechanisms responsible for 10% of this reserve (i.e., of the latently infected population) developing an active disease are not fully understood, yet. The dynamic hypothesis suggests that endogenous reinfection has an important role in maintaining latent infection. In order to examine this hypothesis for falsifiability, an agent-based model of growth, merging, and proliferation of TB lesions was implemented in a computational bronchial tree, built with an iterative algorithm for the generation of bronchial bifurcations and tubes applied inside a virtual 3D pulmonary surface. The computational model was fed and parameterized with computed tomography (CT) experimental data from 5 latently infected minipigs. First, we used CT images to reconstruct the virtual pulmonary surfaces where bronchial trees are built. Then, CT data about TB lesion' size and location to each minipig were used in the parameterization process. The model's outcome provides spatial and size distributions of TB lesions that successfully reproduced experimental data, thus reinforcing the role of the bronchial tree as the spatial structure triggering endogenous reinfection. A sensitivity analysis of the model shows that the final number of lesions is strongly related with the endogenous reinfection frequency and maximum growth rate of the lesions, while their mean diameter mainly depends on the spatial spreading of new lesions and the maximum radius. Finally, the model was used as an in silico experimental platform to explore the transition from latent infection to active disease, identifying two main triggering factors: a high inflammatory response and the combination of a moderate inflammatory response with a small breathing amplitude.

Analysis of a mathematical model of rheumatoid arthritis

Rheumatoid arthritis is an autoimmune disease characterized by inflammation in the synovial fluid within the synovial joint connecting two contiguous bony surfaces. The inflammation diffuses into the cartilage adjacent to each of the bony surfaces, resulting in their gradual destruction. The interface between the cartilage and the synovial fluid is an evolving free boundary. In this paper we consider a two-phase free boundary problem based on a simplified model of rheumatoid arthritis. We prove global existence and uniqueness of a solution, and derive properties of the free boundary. In particular it is proved that the free boundary increases in time, and the cartilage shrinks to zero as [Formula: see text], even under treatment by a drug. It is also shown in the reduced one-phased problem, with cartilage alone, that a larger prescribed inflammation function leads to a faster destruction of the cartilage.

Mycobacterial Trehalose 6,6'-Dimycolate-Induced M1-Type Inflammation

Murine models of Mycobacterium tuberculosis (Mtb) infection demonstrate progression of M1-like (proinflammatory) and M2-like (anti-inflammatory) macrophage morphology following primary granuloma formation. The Mtb cell wall cording factor, trehalose 6,6'-dimycolate (TDM), is a physiologically relevant and useful molecule for modeling early macrophage-mediated events during establishment of the tuberculosis-induced granuloma pathogenesis. Here, it is shown that TDM is a major driver of the early M1-like macrophage response as seen during initiation of the granulomas of primary pathology. Proinflammatory cytokines tumor necrosis factor-α, IL-1β, IL-6, and IL-12p40 are produced in lung tissue after administration of TDM to mice. Furthermore, CD11b⁺CD45⁺ macrophages with a high surface expression of the M1-like markers CD38 and CD86 were found present in regions of pathology in lungs of mice at 7 days post-TDM introduction. Conversely, only low phenotypic marker expression of M2-like markers CD206 and EGR-2 were present on macrophages. These findings suggest that TDM plays a role in establishment of the M1-like shift in the microenvironment during primary tuberculosis.

The DosR antigen Rv1737c from Mycobacterium tuberculosis confers inflammation regulation in tuberculosis infection

There is an urgent need to identify the potential risk factors for activating latent Mycobacterium tuberculosis infection. In this study, we evaluated the immune function of Rv1737c, which is a latency-associated antigen of dormancy survival regulator (DosR) of M. tuberculosis in a mouse model. Our data showed that mice pretreated with recombinant Rv1737c (rRv1737c) exhibited higher levels of antigen-specific antibodies (IgG, IgM and IgA) than sham-treated mice. Following Bacilli Calmette-Guerin (BCG) challenge, rRv1737c adjuvanted with cholera toxin subunit B (CTB) induced diffuse lung inflammation and fibrosis compared to the control mice. The inflammatory pathogenesis due to rRv1737c pre-exposure was associated with a switch in the macrophage phenotype from M1 to activated M2 and was characterized by IL-10 production. Intracellular cytokine analysis further showed that the rRv1737c-pretreated mice exhibited an increased frequency of Th2 cells in the lungs, lymph nodes and spleen after BCG challenge. Furthermore, IFN-γ expression increased in the lungs after rRv1737c pretreatment compared to that in the sham mice. Accordingly, lung cells from rRv1737c-immunized mice stimulated with killed BCG produced higher levels of multiple cytokines, such as IFN-γ, IL-10 and IL-6. The results confirmed that the pathological features of rRv1737c promoted inflammation. Overall, our findings provide direct evidence of the pro-inflammatory function of rRv1737c in a murine model of BCG infection, indicating that Rv1737c is a pathogenic antigen of M. tuberculosis and may be key to the recurrence of latent infection.

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

Mathematical Modeling of Tuberculosis Granuloma Activation

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

In-silico modeling of granulomatous diseases

PURPOSE OF REVIEW

The pathogenesis of genetically complex granulomatous diseases, such as sarcoidosis and latent tuberculosis, remains largely unknown. With the recent advent of more powerful research tools, such as genome-wide expression platforms, comes the challenge of making sense of the enormous data sets so generated. This manuscript will provide demonstrations of how in-silico (computer) analysis of large research data sets can lead to novel discoveries in the field of granulomatous lung disease.

RECENT FINDINGS

The application of in-silico research tools has led to novel discoveries in the fields of noninfectious (e.g., sarcoidosis) and infectious granulomatous diseases. Computer models have identified novel disease mechanisms and can be used to perform 'virtual' experiments rapidly and at low cost compared with conventional laboratory techniques.

SUMMARY

Granulomatous lung diseases are extremely complex, involving dynamic interactions between multiple genes, cells, and molecules. In-silico interpretation of large data sets generated from new research platforms that are capable of comprehensively characterizing and quantifying pools of biological molecules promises to rapidly accelerate the rate of scientific discovery in the field of granulomatous lung disorders.

Combination therapy for melanoma with BRAF/MEK inhibitor and immune checkpoint inhibitor: a mathematical model

BACKGROUND

The B-raf gene is mutated in up to 66% of human malignant melanomas, and its protein product, BRAF kinase, is a key part of RAS-RAF-MEK-ERK (MAPK) pathway of cancer cell proliferation. BRAF-targeted therapy induces significant responses in the majority of patients, and the combination BRAF/MEK inhibitor enhances clinical efficacy, but the response to BRAF inhibitor and to BRAF/MEK inhibitor is short lived. On the other hand, treatment of melanoma with an immune checkpoint inhibitor, such as anti-PD-1, has lower response rate but the response is much more durable, lasting for years. For this reason, it was suggested that combination of BRAF/MEK and PD-1 inhibitors will significantly improve overall survival time.

RESULTS

This paper develops a mathematical model to address the question of the correlation between BRAF/MEK inhibitor and PD-1 inhibitor in melanoma therapy. The model includes dendritic and cancer cells, CD 4+ and CD 8+ T cells, MDSC cells, interleukins IL-12, IL-2, IL-6, IL-10 and TGF- β, PD-1 and PD-L1, and the two drugs: BRAF/MEK inhibitor (with concentration γ B ) and PD-1 inhibitor (with concentration γ A ). The model is represented by a system of partial differential equations, and is used to develop an efficacy map for the combined concentrations (γ B ,γ A ). It is shown that the two drugs are positively correlated if γ B and γ A are at low doses, that is, the growth of the tumor volume decreases if either γ B or γ A is increased. On the other hand, the two drugs are antagonistic at some high doses, that is, there are zones of (γ B ,γ A ) where an increase in one of the two drugs will increase the tumor volume growth, rather than decrease it.

CONCLUSIONS

It will be important to identify, by animal experiments or by early clinical trials, the zones of (γ B ,γ A ) where antagonism occurs, in order to avoid these zones in more advanced clinical trials.

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.

Combination therapy of cancer with cancer vaccine and immune checkpoint inhibitors: A mathematical model

In this paper we consider a combination therapy of cancer. One drug is a vaccine which activates dendritic cells so that they induce more T cells to infiltrate the tumor. The other drug is a checkpoint inhibitor, which enables the T cells to remain active against the cancer cells. The two drugs are positively correlated in the sense that an increase in the amount of each drug results in a reduction in the tumor volume. We consider the question whether a treatment with combination of the two drugs at certain levels is preferable to a treatment by one of the drugs alone at 'roughly' twice the dosage level; if that is the case, then we say that there is a positive 'synergy' for this combination of dosages. To address this question, we develop a mathematical model using a system of partial differential equations. The variables include dendritic and cancer cells, CD4+ and CD8+ T cells, IL-12 and IL-2, GM-CSF produced by the vaccine, and a T cell checkpoint inhibitor associated with PD-1. We use the model to explore the efficacy of the two drugs, separately and in combination, and compare the simulations with data from mouse experiments. We next introduce the concept of synergy between the drugs and develop a synergy map which suggests in what proportion to administer the drugs in order to achieve the maximum reduction of tumor volume under the constraint of maximum tolerated dose.

Mathematical modeling of liver fibrosis

Fibrosis is the formation of excessive fibrous connective tissue in an organ or tissue, which occurs in reparative process or in response to inflammation. Fibrotic diseases are characterized by abnormal excessive deposition of fibrous proteins, such as collagen, and the disease is most commonly progressive, leading to organ disfunction and failure. Although fibrotic diseases evolve in a similar way in all organs, differences may occur as a result of structure and function of the specific organ. In liver fibrosis, the gold standard for diagnosis and monitoring the progression of the disease is biopsy, which is invasive and cannot be repeated frequently. For this reason there is currently a great interest in identifying non-invasive biomarkers for liver fibrosis. In this paper, we develop for the first time a mathematical model of liver fibrosis by a system of partial differential equations. We use the model to explore the efficacy of potential and currently used drugs aimed at blocking the progression of liver fibrosis. We also use the model to develop a diagnostic tool based on a combination of two biomarkers.

Mathematical model on Alzheimer's disease

Background

Alzheimer disease (AD) is a progressive neurodegenerative disease that destroys memory and cognitive skills. AD is characterized by the presence of two types of neuropathological hallmarks: extracellular plaques consisting of amyloid β-peptides and intracellular neurofibrillary tangles of hyperphosphorylated tau proteins. The disease affects 5 million people in the United States and 44 million world-wide. Currently there is no drug that can cure, stop or even slow the progression of the disease. If no cure is found, by 2050 the number of alzheimer’s patients in the U.S. will reach 15 million and the cost of caring for them will exceed $ 1 trillion annually.

Results

The present paper develops a mathematical model of AD that includes neurons, astrocytes, microglias and peripheral macrophages, as well as amyloid β aggregation and hyperphosphorylated tau proteins. The model is represented by a system of partial differential equations. The model is used to simulate the effect of drugs that either failed in clinical trials, or are currently in clinical trials.

Conclusions

Based on these simulations it is suggested that combined therapy with TNF- α inhibitor and anti amyloid β could yield significant efficacy in slowing the progression of AD.